PaddlePALM

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PaddlePALM (PArallel Learning from Multi-tasks) is a fast, flexible, extensible and easy-to-use NLP large-scale pretraining and multi-task learning framework. PaddlePALM is a high level framework aiming at fastly developing high-performance NLP models.

With PaddlePALM, it is easy to achieve effecient exploration of robust learning of NLP models with multiple auxilary tasks. For example, based on PaddlePALM, the produced robust MRC model, D-Net, has achieved the 1st place in EMNLP2019 MRQA track.

MRQA2019 Leaderboard

Beyond the research scope, PaddlePALM has been applied on Baidu Search Engine to seek for more accurate user query understanding and answer mining, which implies the high reliability and performance of PaddlePALM.

Features:

• Easy-to-use: with PALM, 8 steps to achieve a typical NLP task. Moreover, all basic components (e.g., the model backbone, dataset reader, task output head, optimizer...) have been decoupled, which allows the replacement of any component to other candidates with quite minor changes of your code.
• Built-in Popular NLP Backbones and Pre-trained models: multiple state-of-the-art general purpose model architectures and pretrained models (e.g., BERT,ERNIE,RoBERTa,...) are built-in.
• Easy to play Multi-task Learning: only one API is needed for jointly training of several tasks with parameters reusement.
• Support train/eval with Multi-GPUs: automatically recognize and adapt to multiple gpus mode to accelerate training and inference.
• Pre-training friendly: self-supervised tasks (e.g., mask language model) are built-in to facilitate pre-training. Easy to train from scratch.
• Easy to Customize: support customized development of any component (e.g, backbone, task head, reader and optimizer) with reusement of pre-defined ones, which gives developers high flexibility and effeciency to adapt for diverse NLP scenes.

You can easily re-produce following competitive results with minor codes, which covers most of NLP tasks such as classification, matching, sequence labeling, reading comprehension, dialogue understanding and so on. More details can be found in examples.

Overview

Architecture Diagram

PaddlePALM is a well-designed high-level NLP framework. You can efficiently achieve supervised learning, unsupervised/self-supervised learning, multi-task learning and transfer learning with minor codes based on PaddlePALM. There are three layers in PaddlePALM architecture, i.e., component layer, trainer layer and high-level trainer layer from bottom to top.

In component layer, PaddlePALM supplies 6 decoupled components to achieve a NLP task. Each component contains rich pre-defined classes and a Base class. Pre-defined classes are aiming at typical NLP tasks, and the base class is to help users develop a new Class (based on pre-defined ones or from the base).

The trainer layer is to establish a computation graph with selected components and do training and predicting. The training strategy, model saving and loading, evaluation and predicting procedures are described in this layer. Noted a trainer can only process one task.

The high-level trainer layer is for complicated learning and inference strategy, e.g., multi-task learning. You can add auxilary tasks to train robust NLP models (improve test set and out-of-domain performance of a model), or jointly training multiple related tasks to gain more performance for each task.

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.

pip install paddlepalm

Installing via source

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 >= 1.7.0 (Please refer to this to install)

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):

>>> from paddlepalm import downloader
>>> downloader.ls('pretrain')
Available pretrain items:
  => RoBERTa-zh-base
  => RoBERTa-zh-large
  => ERNIE-v2-en-base
  => ERNIE-v2-en-large
  => XLNet-cased-base
  => XLNet-cased-large
  => ERNIE-v1-zh-base
  => ERNIE-v1-zh-base-max-len-512
  => BERT-en-uncased-large-whole-word-masking
  => BERT-en-cased-large-whole-word-masking
  => BERT-en-uncased-base
  => BERT-en-uncased-large
  => BERT-en-cased-base
  => BERT-en-cased-large
  => BERT-multilingual-uncased-base
  => BERT-multilingual-cased-base
  => BERT-zh-base

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

Usage

Quick Start

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.

For more implementation details, see following demos:

• Sentiment Classification
• Question Pairs matching
• Named Entity Recognition
• SQuAD-like Machine Reading Comprehension.

Multi-task Learning

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. load pretrain model with multi_head_trainer.load_pretrain, or load checkpoint with multi_head_trainer.load_ckpt or nothing to do for training from scratch, 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.

For more implementation details with multi_head_trainer, see

• ATIS: joint training of dialogue intent recognition and slot filling

Save models

To save models/checkpoints and logs during training, just call trainer.set_saver method. More implementation details see this.

Evaluation/Inference

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.

If you want to do evaluation during training process, use trainer.train_one_step() instead of trainer.train(). The trainer.train_one_step(batch) achieves to train only one step, thus you can insert evaluation code into any point of training process. The argument batch can be fetched from trainer.get_one_batch.

PaddlePALM also supports multi-head inference, please reference examples/multi-task/joint_predict.py.

Play with Multiple GPUs

If there exists multiple GPUs in your environment, you can control the number and index of these GPUs through the environment variable CUDA_VISIBLE_DEVICES. For example, if 4 GPUs in your enviroment, indexed with 0,1,2,3, you can run with GPU2 only with following commands

CUDA_VISIBLE_DEVICES=2 python run.py

Multiple GPUs should be seperated with ,. For example, running with GPU2 and GPU3, following commands is refered:

CUDA_VISIBLE_DEVICES=2,3 python run.py

On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the batch_size is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. Therefore, when running with multiple cards, you need to ensure that the set batch_size can be divided by the number of cards.

License

This tutorial is contributed by PaddlePaddle and licensed under the Apache-2.0 license.