A State-of-the-Art Large-scale Pretrained Response Generation Model (DialoGPT)

This repository contains the source code and trained model for a large-scale pretrained dialogue response generation model. The human evaluation results indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.

The repository is based on huggingface pytorch-transformer and OpenAI GPT-2, containing data extraction script, model training code and pretrained small (117M) medium (345M) and large (762M) model checkpoint.

The model is trained on 147M multi-turn dialogue from Reddit discussion thread. The largest model can be trained in several hours on a 8 V100 machines (however this is not required), with distributed training and FP16 option.

The include script can be used to reproduce the results of DSTC-7 grounded dialogue generation challenge and a 6k multi-reference dataset created from Reddit data.

Project webpage: https://www.microsoft.com/en-us/research/project/large-scale-pretraining-for-response-generation/

ArXiv paper: https://arxiv.org/abs/1911.00536

News

(Update 06/23/2021) We have released a retrieval-augmented/grounded version of DialoGPT (RetGen), please check out the RetGen repo and RetGen paper

(Update 05/20/2021) An awesome video walkthrough on YouTube for DialoGPT by Prakhar Mishra

(Update 03/31/2021) A 3rd party demo by AK391 using Gradio web demo try it out

(Update 09/15/2020) A set of large-scale dialog ranking models has been released!

DialoGPT generation is improved by integrating with our latest dialog ranking models, DialogRPT

(Update 07/08/2020) The 6K multi-ref test set has been released!

To generate the data, pleaser run demo.py and set the data option to 'full', the generated 6k multi-ref test set will be located at

./data/test.refs.txt

(Update 03/10/2020) Model cards available in Huggingface Transformers!

Please check out our model cards in huggingface Transformers repository. With several lines of code it should be pretty straighforward to play with the DialoGPT interactively.

small model: https://huggingface.co/microsoft/DialoGPT-small

medium model: https://huggingface.co/microsoft/DialoGPT-medium

large model: https://huggingface.co/microsoft/DialoGPT-large

(New) Ranking model: https://huggingface.co/microsoft/DialogRPT-updown

(Update 01/06/2020) Some third-party decoding script implementations:

• https://github.com/polakowo/gpt2bot GPT2Bot implementation based on telegram by polakowo, ref
• https://colab.research.google.com/drive/1PslHE4Rl4RqSa20s7HEp0ZKITBir6ezE A colab interactive notebook by qywu,ref
• https://github.com/andreamad8/DialoGPT2-Interact An interactive script featuring multiturn chatbot by andreamad8,ref
• https://github.com/LHolten/DialoGTP-MMI-decoder An MMI implementation by LHolten,ref
• https://colab.research.google.com/drive/1-_KjlAV3J1IVDw_9KogjKDCzgFY7Jp7E A colab interactive notebook by [email protected],ref
• https://colab.research.google.com/drive/15wa925dj7jvdvrz8_z3vU7btqAFQLVlG A great tutorial of how to finetune DialoGPT to build a customized bot built by Rostyslav Neskorozhenyi. ref
• https://gradio.app/g/AK391/DialoGPT A 3rd party demo by AK391 using Gradio web demo

Recommended Configuration

• Linux Ubuntu 16.04
• GPU with at least 12G memory

DialoGPT was developed entirely on Ubuntu 16.04, and -- depending on our availability -- we try to provide support if you experience difficulties running the code on the same configuration. However, we are unable to provide support for other distributions or operating systems. Portions of the code may run on other UNIX flavors (macOS, Windows subsystem for Linux, Cygwin, etc.), but it is recommended to use Ubuntu for the main training code.

The training code can be run on CPU, but it can be slow. We would recommend to use GPU to train and finetune all models. There is no minimal limit of the number of GPUs. However, if using distributed train for multiple GPUs configuration, the speed-up vs the number of GPUs is roughly sub-linear. To simulate the same batchsize when using less GPUs, please use a larger gradient_accumulation_steps in model training.

The 117M and 345M model can be loaded in a single GPU with 12G memory. The 762M model would require a single GPU that has greater than 16G memory for efficient training. The training speed on a benchmark data with 50M training instances and V100 GPUs:

Fine-tuning from our pretrained model on a new dataset typically requires 1-2 epochs.

Setup & Installation (TL;DR)

We created a demo script demo.py to ease the difficulty of the deployment of this system. The demo.py contains a pipeline of model downloading, data extraction, data preprocessing and model training over a dummy dataset within one commandline.

Train model with Conda Environment

Please use the below commandlines to clone, install the requirements and load the Conda environment (Note that the Nvidia CUDA 10.0 developer toolkit is required):

sudo apt-get install -y make wget gzip bzip2 xz-utils zstd sed

git clone https://github.com/microsoft/DialoGPT.git
cd DialoGPT
conda env create -f LSP-linux.yml -n LSP
conda activate LSP

If you run this on an architecture other than Linux, please use LSP-generic.yml instead of LSP-linux.yml but please note that the generic one is not tested in all platform, so the stablity can not be gauranteed. To use fp16 training, please install apex by using commands below

conda activate LSP
git clone https://github.com/NVIDIA/apex
cd apex
git reset --hard 3d01e4a0a188cc8df54bc6e44cf5eb40ff6b4cc5
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .
python3.6 demo.py

Train model with Docker environment

To start, first install the docker and Nvidia-docker from their official repos. The image environment for running the code can be loaded as below:

Nvidia-docker v2.*

$ docker run --gpus all --ipc=host --rm -it -v $PWD:/workspace --network=host icaruszyz/large-scale-training:dialogpt bash

Nvidia-docker v1.*

$ nvidia-docker --rm -it -v $PWD:/workspace --network=host icaruszyz/large-scale-training:dialogpt bash

Inside the docker container, run

python demo.py

Pipeline details

This section explains all components in the demo.py.

Data loading

Before running demo.py, you can set DATA_FOLDER (default value ./models) in demo.py as the place you want to download all the data and pretrained/fine-tuned models. Then simply run

python demo.py

to

• automatically download models and data,
• prepare raw data into db that is ready to use for the program,
• generate a training scripts.

Note that by default the demo.py will use a dummy data, please specify the Reddit training data by using option --data. Three options are available:dummy,small and full.

python demo.py --data small
python demo.py --data full

The small Reddit data is around 140MB and the full Reddit data is more than 27GB. You can prepare a cup of coffee when processing with the full Reddit data because it takes a long time!

To generate the 6k multi-ref test set data, pleaser run demo.py and set the data option to 'full', the generation will be located at

./data/test.refs.txt

Pretrained model

The pretrained and fine-tuned models are available on azure blobstorage. Please run/see demo.py for more details about how to download/use those models. Or you could download directly by using the links in demo_utils.py.

Preparing data

First, use the prepare4db.sh to convert a tsv data file into the correct format that the following script can recognize. The trainig data need to be then processed into a database file with below commandline:

python prepro.py --corpus $DATA_PATH

Using the training script

The training script can be used in single GPU or multiple GPU settings (distributed training across multiple GPUs within a single node):

python ./LSP_train.py  # Single GPU training
python -m torch.distributed.launch --nproc_per_node=8 ./LSP_train.py  # Training on 8 GPUs

The training script accept several arguments to tweak the training:

During the training, two log files will be updated. The train_log.txt and eval_log.txt contains the model loss, perplexity and training speed (tokens/sec) statistics for the training and dev set.

The log file and saved model checkpoint can be found in ./models/output_model

Model decoding

We note that even with properly filtered Reddit dataset, sometimes our model can still generate moderately toxic/inappropriate responses. Due to this reason, we are unable to provide the decoding script at this time (The live demo and decoding script access is upon invitation only now ). We are currently still working on a controlled decoding method to prevent this system from toxic generation. Please stay tuned.

See issues #3 and Reddit discussions for some discussions on third-party decoding methods.

See below for some third-party decoding methods:

• https://github.com/polakowo/gpt2bot GPT2Bot implementation based on telegram by polakowo, ref
• https://colab.research.google.com/drive/1PslHE4Rl4RqSa20s7HEp0ZKITBir6ezE A colab interactive notebook by qywu,ref
• https://github.com/andreamad8/DialoGPT2-Interact An interactive script featuring multiturn chatbot by andreamad8,ref
• https://github.com/LHolten/DialoGTP-MMI-decoder An MMI implementation by LHolten,ref
• https://colab.research.google.com/drive/1-_KjlAV3J1IVDw_9KogjKDCzgFY7Jp7E A colab interactive notebook by [email protected],ref
• https://gradio.app/g/AK391/DialoGPT A 3rd party demo by AK391 using Gradio web demo

Models

We release 6 fine-tuned models which can be further fine-tuned on low-resource user-customized dataset. The total parameters in these models range from 117M to 762M, in accord with OpenAI GPT-2 model sizes.

The model files can be loaded exactly as the GPT-2 model checkpoints from Huggingface's Transformers. You can find the corresponding configuration files (merges.txt, config.json, vocab.json) in DialoGPT's repo in ./configs/*.

The reverse model is predicting the source from the target. This model is used for MMI reranking.

The DialogRPT models our recently proposed ranking models used to predict the human feedback (upvotes, replies) of the responses. These models can be used to improve the DialoGPT generation quality (see our EMNLP paper for details).

Retraining full models

Preparation

The first step to retrain the full models is to generate the aforementioned 27GB Reddit dataset. This involves downloading full Reddit submission and comments dumps from https://files.pushshift.io/reddit and creating intermediate files, which overall require 700GB of local disk space. Downloading and processing the full data requires about 1-2 days, depending on your (CPU) compute capabilties (e.g., ~24 hours with 8 cores on a recent computer). Assuming you ran the above setup and installation steps (conda activate LSP, etc.), you can create the full dataset by running either:

python demo.py --data full

or

cd reddit_extractor; SIZE=full make -j 8; cd ..

The former command calls the latter, so the two methods are equivalent. We recommend the former, as the latter is mostly useful if you run into any problem or want to customize any arguments (e.g., the make command lets you build only a subset of the data). Note that the downloading phase can be error prone, for example based on your geolocation (firewall, etc.). If the above commands fail to generate data/train.tsv, or if that file is not anywhere close to 27GB, it means something went wrong. In that case, you may want to inspect reddit_extractor/wget-log and reddit_extractor/logs/*.log for any obvious error (e.g., wget unable to download from pushshift.io). If error messages don't make sense to you, feel free to contact us. If so, please be sure to include any error messages gathered from these log files.

Training data statistics: the generated training tsv file should be roughly 26.8 GB uncompressed, with 146.8M training instances, 3.87B source tokens, and 2.14B target tokens (including utterance-level 0/1 weights).

Training

We recommand generating the above data using the demo.py --data full, as it (1) generates the data, (2) converts it into DB format, and (3) trains a model using python LSP_train.py. Please directly edit demo.py if you want to customize any of the hyperparameters.

Evaluations

DSTC-7 challenge

Our model achieved the state-of-the-art results in DSTC-7 Challenge response generation task.

where ENT represents the Entropy score, and DIST represents the Distinct score. For all metrics except the average length, larger are better.

Note that the superior automatic evaluation comparing to human responses does not necessary imply that our model achieves human parity. Please check out our paper for more detailed analysis.

To fine-tune the 345M DialoGPT model on the DSTC-7 challenge data on a server with 8 V100 GPUs, please run the following commandline (The DSTC data can be found at DSTC-7 repo):

python3 -m torch.distributed.launch --nproc_per_node=8 train_LSP.py --init_checkpoint ./models/medium/medium_ft.pkl --train_input_file ./data/DSTC_train.db --eval_input_file ./data/DSTC_valid.tsv --model_name_or_path ./model/medium/ --learning_rate 1e-4  --train_batch_size 64 --eval_batch_size 64 --no_token_id

The trained model can be found at DSTC medium model

Evaluation

1. Please downloads the following 3rd-party packages and save into the empty folder 3rdparty:

   • mteval-v14c.pl to compute NIST. You may need to install the following perl modules (e.g. by cpan install): XML:Twig, Sort:Naturally and String:Util.
   • meteor-1.5 to compute METEOR. It requires Java.

2. Please follow the DSTC-7 official repo to extract the data, and put data-official-test/test.refs.txt into ./dstc/data/ folder.

3. Run the extraction script below to produce the human response hypothesis file human.resp.txt:

   python extract_human.py

4. Finally, to reproduce the results of human hypothesis on DSTC dataset, please run following commands under the repo folder:

   python batch_eval.py

The evaluation results will be generated in the folder ./dstc/eval/

6K multi-ref dataset result

Automatic evaluation

We test on 6K multi-ref dataset from Reddit. The results are summarized in below

Human evaluation

We further conduct human evaluations (6K examples for each methods, each example is evaluated by 3 human judges). The results show a strong evidence that our generation quality is towards approaching the quality of real human responses, under this non-interactive Turing test:

Relevance: A and B, which one is more relevant to the source prompt.

Informativeness: A and B, which one is more contentful and informative.

Human-Like: A and B, which one do you think is more likely to be generated by Human.

Please see full details in our arxiv paper.

Generated Examples

• Multi-turn generation examples from an interactive environment:

• Multi-turn self-playing dialogue with user prompt

• Commonsense question answering examples

• Handling context that does not appear in training examples (note that training examples are sampled from 2005 to 2017)

• Our bot's response to philosophy questions

• Finally

Please start a issue if you spot any :)

Try our system

The live demo and decoding script access is upon invitation only now. Please stayed tuned for the full release.

Related Project

• RetGen: https://github.com/dreasysnail/RetGen. Retrieval-augmented/grounded DialoGPT and beyond. RetGen is a joint training framework that simultaneously optimizes a dense passage retriever and a knowledge-grounded text generator in an end-to-end fashion.

• Microsoft ICECAPS: https://github.com/microsoft/icecaps.

  As an orthogonal repository of this project, Microsoft Icecaps is an open-source toolkit (in tensorflow) for building neural conversational systems. Icecaps provides an array of tools from recent conversation modeling and general NLP literature within a flexible paradigm that enables complex multi-task learning setups.

• Pretrained UniLM: https://github.com/microsoft/unilm

• MT-DNN: https://github.com/namisan/mt-dnn

• A chinese counterpart of DialoGPT by yangjianxin1. https://github.com/yangjianxin1/GPT2-chitchat. We are glad to see that the MMI strategy that we used in DialoGPT has also improved the performance for this project as well!

Contact

Please contact [email protected] if you have any questions/suggestions. However, the response will be sporadic. Please expect delay.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Disclaimer

This repository aims to facilitate research in large-scale pretraining for conversational data. This toolkit contains only part of the modeling machinery needed to actually produce a model weight file in a running dialog. On its own, this model provides only information about the weights of various text spans; in order for a researcher to actually use it, they will need to bring conversational data of their own and decode the response generation from the pretrained system. Microsoft is not responsible for any generation from the 3rd party utilization of the pretrained system.

Citation

If you use this code in your research, you can cite our arxiv paper:

@inproceedings{zhang2019dialogpt,
    title={DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation},
    author={Yizhe Zhang and Siqi Sun and Michel Galley and Yen-Chun Chen and Chris Brockett and Xiang Gao and Jianfeng Gao and Jingjing Liu and Bill Dolan},
    year={2020},
    booktitle={ACL, system demonstration}
}