English | 中文说明

TextBrewer is a PyTorch-based model distillation toolkit for natural language processing. It includes various distillation techniques from both NLP and CV field and provides an easy-to-use distillation framework, which allows users to quickly experiment with the state-of-the-art distillation methods to compress the model with a relatively small sacrifice in the performance, increasing the inference speed and reducing the memory usage.

Paper: https://arxiv.org/abs/2002.12620

Full Documentation

Update

Apr 26, 2020

• Added Chinese NER task (MSRA NER) results.
• Added results for distilling to T12-nano model, which has a similar strcuture to Electra-small.
• Updated some results of CoNLL-2003, CMRC 2018 and DRCD.

Apr 22, 2020

• Updated to 0.1.9 (added cache option which speeds up distillation; fixed some bugs). See details in releases.
• Added experimential results for distilling Electra-base to Electra-small on Chinese tasks.
• TextBrewer has been accepted by ACL 2020 as a demo paper, please use our new bib entry.

Mar 17, 2020

• Added CoNLL-2003 English NER distillation example, see examples/conll2003_example.

Mar 11, 2020

• Updated to 0.1.8 (Improvements on TrainingConfig and train method). See details in releases.

Mar 2, 2020

• Initial public version 0.1.7 has been released. See details in releases.

Table of Contents

Section	Contents
Introduction	Introduction to TextBrewer
Installation	How to install
Workflow	Two stages of TextBrewer workflow
Quickstart	Example: distilling BERT-base to a 3-layer BERT
Experiments	Distillation experiments on typical English and Chinese datasets
Core Concepts	Brief explanations of the core concepts in TextBrewer
FAQ	Frequently asked questions
Known Issues	Known issues
Citation	Citation to TextBrewer
Follow Us	-

Introduction

Textbrewer is designed for the knowledge distillation of NLP models. It provides various distillation methods and offers a distillation framework for quickly setting up experiments.

The main features of TextBrewer are:

• Wide-support: it supports various model architectures (especially transformer-based models)
• Flexibility: design your own distillation scheme by combining different techniques; it also supports user-defined loss functions, modules, etc.
• Easy-to-use: users don't need to modify the model architectures
• Built for NLP: it is suitable for a wide variety of NLP tasks: text classification, machine reading comprehension, sequence labeling, ...

TextBrewer currently is shipped with the following distillation techniques:

• Mixed soft-label and hard-label training
• Dynamic loss weight adjustment and temperature adjustment
• Various distillation loss functions: hidden states MSE, attention-matrix-based loss, neuron selectivity transfer, ...
• Freely adding intermediate features matching losses
• Multi-teacher distillation
• ...

TextBrewer includes:

1. Distillers: the cores of distillation. Different distillers perform different distillation modes. There are GeneralDistiller, MultiTeacherDistiller, BasicTrainer, etc.
2. Configurations and presets: Configuration classes for training and distillation, and predefined distillation loss functions and strategies.
3. Utilities: auxiliary tools such as model parameters analysis.

To start distillation, users need to provide

1. the models (the trained teacher model and the un-trained student model)
2. datasets and experiment configurations

TextBrewer has achieved impressive results on several typical NLP tasks. See Experiments.

See Full Documentation for detailed usages.

Installation

• Requirements

  • Python >= 3.6
  • PyTorch >= 1.1.0
  • TensorboardX or Tensorboard
  • NumPy
  • tqdm
  • Transformers >= 2.0 (optional, used by some examples)

• Install from PyPI

  pip install textbrewer

• Install from the Github source

  git clone https://github.com/airaria/TextBrewer.git
  pip install ./textbrewer

Workflow

• Stage 1: Preparation:

  1. Train the teacher model
  2. Define and initialize the student model
  3. Construct a dataloader, an optimizer, and a learning rate scheduler

• Stage 2: Distillation with TextBrewer:

  1. Construct a TraningConfig and a DistillationConfig, initialize a distiller
  2. Define an adaptor and a callback. The adaptor is used for adaptation of model inputs and outputs. The callback is called by the distiller during training
  3. Call the train method of the distiller

Quickstart

Here we show the usage of TextBrewer by distilling BERT-base to a 3-layer BERT.

Before distillation, we assume users have provided:

• A trained teacher model teacher_model (BERT-base) and a to-be-trained student model student_model (3-layer BERT).
• a dataloader of the dataset, an optimizer and a learning rate builder or class scheduler_class and its args dict scheduler_dict.

Distill with TextBrewer:

import textbrewer
from textbrewer import GeneralDistiller
from textbrewer import TrainingConfig, DistillationConfig

# Show the statistics of model parameters
print("\nteacher_model's parametrers:")
result, _ = textbrewer.utils.display_parameters(teacher_model,max_level=3)
print (result)

print("student_model's parametrers:")
result, _ = textbrewer.utils.display_parameters(student_model,max_level=3)
print (result)

# Define an adaptor for translating the model inputs and outputs
def simple_adaptor(batch, model_outputs):
      # The second and third elements of model outputs are the logits and hidden states
    return {'logits': model_outputs[1],
            'hidden': model_outputs[2]}

# Training configuration 
train_config = TrainingConfig()
# Distillation configuration
# Matching different layers of the student and the teacher
distill_config = DistillationConfig(
    intermediate_matches=[    
     {'layer_T':0, 'layer_S':0, 'feature':'hidden', 'loss': 'hidden_mse','weight' : 1},
     {'layer_T':8, 'layer_S':2, 'feature':'hidden', 'loss': 'hidden_mse','weight' : 1}])

# Build distiller
distiller = GeneralDistiller(
    train_config=train_config, distill_config = distill_config,
    model_T = teacher_model, model_S = student_model, 
    adaptor_T = simple_adaptor, adaptor_S = simple_adaptor)

# Start!
with distiller:
    distiller.train(optimizer, dataloader, num_epochs=1, scheduler_class=scheduler_class, scheduler_args = scheduler_args, callback=None)

Examples can be found in the examples directory :

• examples/random_token_example : a simple runable toy example which demonstrates the usage of TextBrewer. This example performs distillation on the text classification task with random tokens as inputs.
• examples/cmrc2018_example (Chinese): distillation on CMRC 2018, a Chinese MRC task, using DRCD as data augmentation.
• examples/mnli_example (English): distillation on MNLI, an English sentence-pair classification task. This example also shows how to perform multi-teacher distillation.
• examples/conll2003_example (English): distillation on CoNLL-2003 English NER task, which is in form of sequence labeling.

Experiments

We have performed distillation experiments on several typical English and Chinese NLP datasets. The setups and configurations are listed below.

Models

• For English tasks, the teacher model is BERT-base-cased.
• For Chinese tasks, the teacher models are RoBERTa-wwm-ext and Electra-base released by the Joint Laboratory of HIT and iFLYTEK Research.

We have tested different student models. To compare with public results, the student models are built with standard transformer blocks except for BiGRU which is a single-layer bidirectional GRU. The architectures are listed below. Note that the number of parameters includes the embedding layer but does not include the output layer of each specific task.

English models

Model	#Layers	Hidden size	Feed-forward size	#Params	Relative size
BERT-base-cased (teacher)	12	768	3072	108M	100%
T6 (student)	6	768	3072	65M	60%
T3 (student)	3	768	3072	44M	41%
T3-small (student)	3	384	1536	17M	16%
T4-Tiny (student)	4	312	1200	14M	13%
T12-nano (student)	12	256	1024	17M	16%
BiGRU (student)	-	768	-	31M	29%

Chinese models

Model	#Layers	Hidden size	Feed-forward size	#Params	Relative size
RoBERTa-wwm-ext (teacher)	12	768	3072	102M	100%
Electra-base (teacher)	12	768	3072	102M	100%
T3 (student)	3	768	3072	38M	37%
T3-small (student)	3	384	1536	14M	14%
T4-Tiny (student)	4	312	1200	11M	11%
Electra-small (student)	12	256	1024	12M	12%

• T6 archtecture is the same as DistilBERT^[1], BERT₆-PKD^[2], and BERT-of-Theseus^[3].
• T4-tiny archtecture is the same as TinyBERT^[4].
• T3 architecure is the same as BERT₃-PKD^[2].

Distillation Configurations

distill_config = DistillationConfig(temperature = 8, intermediate_matches = matches)
# Others arguments take the default values

matches are differnt for different models:

Model	matches
BiGRU	None
T6	L6_hidden_mse + L6_hidden_smmd
T3	L3_hidden_mse + L3_hidden_smmd
T3-small	L3n_hidden_mse + L3_hidden_smmd
T4-Tiny	L4t_hidden_mse + L4_hidden_smmd
T12-nano	small_hidden_mse + small_hidden_smmd
Electra-small	small_hidden_mse + small_hidden_smmd

The definitions of matches are at examples/matches/matches.py.

We use GeneralDistiller in all the distillation experiments.

Training Configurations

• Learning rate is 1e-4 (unless otherwise specified).
• We train all the models for 30~60 epochs.

Results on English Datasets

We experiment on the following typical Enlgish datasets:

Dataset	Task type	Metrics	#Train	#Dev	Note
MNLI	text classification	m/mm Acc	393K	20K	sentence-pair 3-class classification
SQuAD 1.1	reading comprehension	EM/F1	88K	11K	span-extraction machine reading comprehension
CoNLL-2003	sequence labeling	F1	23K	6K	named entity recognition

We list the public results from DistilBERT, BERT-PKD, BERT-of-Theseus, TinyBERT and our results below for comparison.

Public results:

Model (public)	MNLI	SQuAD	CoNLL-2003
DistilBERT (T6)	81.6 / 81.1	78.1 / 86.2	-
BERT6-PKD (T6)	81.5 / 81.0	77.1 / 85.3	-
BERT-of-Theseus (T6)	82.4/ 82.1	-	-
BERT3-PKD (T3)	76.7 / 76.3	-	-
TinyBERT (T4-tiny)	82.8 / 82.9	72.7 / 82.1	-

Our results:

Model (ours)	MNLI	SQuAD	CoNLL-2003
BERT-base-cased (teacher)	83.7 / 84.0	81.5 / 88.6	91.1
BiGRU	-	-	85.3
T6	83.5 / 84.0	80.8 / 88.1	90.7
T3	81.8 / 82.7	76.4 / 84.9	87.5
T3-small	81.3 / 81.7	72.3 / 81.4	78.6
T4-tiny	82.0 / 82.6	75.2 / 84.0	89.1
T12-nano	83.2 / 83.9	79.0 / 86.6	89.6

Note:

1. The equivalent model structures of public models are shown in the brackets after their names.
2. When distilling to T4-tiny, NewsQA is used for data augmentation on SQuAD and HotpotQA is used for data augmentation on CoNLL-2003.
3. When distilling to T12-nano, HotpotQA is used for data augmentation on CoNLL-2003.

Results on Chinese Datasets

We experiment on the following typical Chinese datasets:

Dataset	Task type	Metrics	#Train	#Dev	Note
XNLI	text classification	Acc	393K	2.5K	Chinese translation version of MNLI
LCQMC	text classification	Acc	239K	8.8K	sentence-pair matching, binary classification
CMRC 2018	reading comprehension	EM/F1	10K	3.4K	span-extraction machine reading comprehension
DRCD	reading comprehension	EM/F1	27K	3.5K	span-extraction machine reading comprehension (Traditional Chinese)
MSRA NER	sequence labeling	F1	45K	3.4K (#Test)	Chinese named entity recognition

The results are listed below.

Model	XNLI	LCQMC	CMRC 2018	DRCD
RoBERTa-wwm-ext (teacher)	79.9	89.4	68.8 / 86.4	86.5 / 92.5
T3	78.4	89.0	66.4 / 84.2	78.2 / 86.4
T3-small	76.0	88.1	58.0 / 79.3	75.8 / 84.8
T4-tiny	76.2	88.4	61.8 / 81.8	77.3 / 86.1

Model	XNLI	LCQMC	CMRC 2018	DRCD	MSRA NER
Electra-base (teacher))	77.8	89.8	65.6 / 84.7	86.9 / 92.3	95.14
Electra-small	77.7	89.3	66.5 / 84.9	85.5 / 91.3	93.48

Note:

1. Learning rate decay is not used in distillation on CMRC 2018 and DRCD.
2. CMRC 2018 and DRCD take each other as the augmentation dataset in the distillation.
3. The settings of training Electra-base teacher model can be found at Chinese-ELECTRA.
4. Electra-small student model is intialized with the pretrained weights.

Core Concepts

Configurations

• TrainingConfig: configuration related to general deep learning model training
• DistillationConfig: configuration related to distillation methods

Distillers

Distillers are in charge of conducting the actual experiments. The following distillers are available:

• BasicDistiller: single-teacher single-task distillation, provides basic distillation strategies.
• GeneralDistiller (Recommended): single-teacher single-task distillation, supports intermediate features matching. Recommended most of the time.
• MultiTeacherDistiller: multi-teacher distillation, which distills multiple teacher models (of the same task) into a single student model. This class doesn't support Intermediate features matching.
• MultiTaskDistiller: multi-task distillation, which distills multiple teacher models (of different tasks) into a single student. This class doesn't support Intermediate features matching.
• BasicTrainer: Supervised training a single model on a labeled dataset, not for distillation. It can be used to train a teacher model.

User-Defined Functions

In TextBrewer, there are two functions that should be implemented by users: callback and adaptor.

Callback

At each checkpoint, after saving the student model, the callback function will be called by the distiller. A callback can be used to evaluate the performance of the student model at each checkpoint.

Adaptor

It converts the model inputs and outputs to the specified format so that they could be recognized by the distiller, and distillation losses can be computed. At each training step, batch and model outputs will be passed to the adaptor; the adaptor re-organizes the data and returns a dictionary.

For more details, see the explanations in Full Documentation.

FAQ

Q: How to initialize the student model?

A: The student model could be randomly initialized (i.e., with no prior knowledge) or be initialized by pre-trained weights. For example, when distilling a BERT-base model to a 3-layer BERT, you could initialize the student model with RBT3 (for Chinese tasks) or the first three layers of BERT (for English tasks) to avoid cold start problem. We recommend that users use pre-trained student models whenever possible to fully take advantage of large-scale pre-training.

Q: How to set training hyperparameters for the distillation experiments？

A: Knowledge distillation usually requires more training epochs and larger learning rate than training on the labeled dataset. For example, training SQuAD on BERT-base usually takes 3 epochs with lr=3e-5; however, distillation takes 30~50 epochs with lr=1e-4. The conclusions are based on our experiments, and you are advised to try on your own data.

Known Issues

• Compatibility with FP16 training has not been tested.
• Multi-GPU training support is only available through DataParallel currently.

Citation

If you find TextBrewer is helpful, please cite our paper:

@InProceedings{textbrewer-acl2020-demo,
  author =  "Yang, Ziqing and Cui, Yiming and Chen, Zhipeng and Che, Wanxiang and Liu, Ting and Wang, Shijin and Hu, Guoping",
  title =   "{T}ext{B}rewer: {A}n {O}pen-{S}ource {K}nowledge {D}istillation {T}oolkit for {N}atural {L}anguage {P}rocessing",
  booktitle =   "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
  year =  "2020",
  publisher =   "Association for Computational Linguistics"
}

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