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HAT: Hardware Aware Transformers for Efficient Natural Language Processing [paper] [website] [video]

@inproceedings{hanruiwang2020hat,
    title     = {HAT: Hardware-Aware Transformers for Efficient Natural Language Processing},
    author    = {Wang, Hanrui and Wu, Zhanghao and Liu, Zhijian and Cai, Han and Zhu, Ligeng and Gan, Chuang and Han, Song},
    booktitle = {Annual Conference of the Association for Computational Linguistics},
    year      = {2020}
} 

Overview

We release the PyTorch code and 50 pre-trained models for HAT: Hardware-Aware Transformers. Within a Transformer supernet (SuperTransformer), we efficiently search for a specialized fast model (SubTransformer) for each hardware with latency feedback. The search cost is reduced by over 10000×. teaser

HAT Framework overview: overview

HAT models achieve up to 3× speedup and 3.7× smaller model size with no performance loss. results

Usage

Installation

To install from source and develop locally:

git clone https://github.com/mit-han-lab/hardware-aware-transformers.git
cd hardware-aware-transformers
pip install --editable .

Data Preparation

Task task_name Train Valid Test
WMT'14 En-De wmt14.en-de WMT'16 newstest2013 newstest2014
WMT'14 En-Fr wmt14.en-fr WMT'14 newstest2012&2013 newstest2014
WMT'19 En-De wmt19.en-de WMT'19 newstest2017 newstest2018
IWSLT'14 De-En iwslt14.de-en IWSLT'14 train set IWSLT'14 valid set IWSLT14.TED.dev2010
IWSLT14.TEDX.dev2012
IWSLT14.TED.tst2010
IWSLT14.TED.tst2011
IWSLT14.TED.tst2012

To download and preprocess data, run:

bash configs/[task_name]/preprocess.sh

If you find preprocessing time-consuming, you can directly download the preprocessed data we provide:

bash configs/[task_name]/get_preprocessed.sh

Testing

We provide pre-trained models (SubTransformers) on the Machine Translation tasks for evaluations. The #Params and FLOPs do not count in the embedding lookup table and the last output layers because they are dependent on tasks.

Task Hardware Latency #Params
(M)
FLOPs
(G)
BLEU Sacre
BLEU
model_name Link
WMT'14 En-De Raspberry Pi ARM Cortex-A72 CPU 3.5s
4.0s
4.5s
5.0s
6.0s
6.9s
25.22
29.42
35.72
36.77
44.13
48.33
1.53
1.78
2.19
2.26
2.70
3.02
25.8
26.9
27.6
27.8
28.2
28.4
25.6
26.6
27.1
27.2
27.6
27.8
HAT_wmt14ende_raspberrypi@3.5s_bleu@25.8
HAT_wmt14ende_raspberrypi@4.0s_bleu@26.9
HAT_wmt14ende_raspberrypi@4.5s_bleu@27.6
HAT_wmt14ende_raspberrypi@5.0s_bleu@27.8
HAT_wmt14ende_raspberrypi@6.0s_bleu@28.2
HAT_wmt14ende_raspberrypi@6.9s_bleu@28.4
link
link
link
link
link
link
WMT'14 En-De Intel Xeon E5-2640 CPU 137.9ms
204.2ms
278.7ms
340.2ms
369.6ms
450.9ms
30.47
35.72
40.97
46.23
51.48
56.73
1.87
2.19
2.54
2.86
3.21
3.53
25.8
27.6
27.9
28.1
28.2
28.5
25.6
27.1
27.3
27.5
27.6
27.9
HAT_wmt14ende_xeon@137.9ms_bleu@25.8
HAT_wmt14ende_xeon@204.2ms_bleu@27.6
HAT_wmt14ende_xeon@278.7ms_bleu@27.9
HAT_wmt14ende_xeon@340.2ms_bleu@28.1
HAT_wmt14ende_xeon@369.6ms_bleu@28.2
HAT_wmt14ende_xeon@450.9ms_bleu@28.5
link
link
link
link
link
link
WMT'14 En-De Nvidia TITAN Xp GPU 57.1ms
91.2ms
126.0ms
146.7ms
208.1ms
30.47
35.72
40.97
51.20
49.38
1.87
2.19
2.54
3.17
3.09
25.8
27.6
27.9
28.1
28.5
25.6
27.1
27.3
27.5
27.8
HAT_wmt14ende_titanxp@57.1ms_bleu@25.8
HAT_wmt14ende_titanxp@91.2ms_bleu@27.6
HAT_wmt14ende_titanxp@126.0ms_bleu@27.9
HAT_wmt14ende_titanxp@146.7ms_bleu@28.1
HAT_wmt14ende_titanxp@208.1ms_bleu@28.5
link
link
link
link
link
WMT'14 En-Fr Raspberry Pi ARM Cortex-A72 CPU 4.3s
5.3s
5.8s
6.9s
7.8s
9.1s
25.22
35.72
36.77
44.13
49.38
56.73
1.53
2.23
2.26
2.70
3.09
3.57
38.8
40.1
40.6
41.1
41.4
41.8
36.0
37.3
37.8
38.3
38.5
38.9
HAT_wmt14enfr_raspberrypi@4.3s_bleu@38.8
HAT_wmt14enfr_raspberrypi@5.3s_bleu@40.1
HAT_wmt14enfr_raspberrypi@5.8s_bleu@40.6
HAT_wmt14enfr_raspberrypi@6.9s_bleu@41.1
HAT_wmt14enfr_raspberrypi@7.8s_bleu@41.4
HAT_wmt14enfr_raspberrypi@9.1s_bleu@41.8
link
link
link
link
link
link
WMT'14 En-Fr Intel Xeon E5-2640 CPU 154.7ms
208.8ms
329.4ms
394.5ms
442.0ms
30.47
35.72
44.13
51.48
56.73
1.84
2.23
2.70
3.28
3.57
39.1
40.0
41.1
41.4
41.7
36.3
37.2
38.2
38.5
38.8
HAT_wmt14enfr_xeon@154.7ms_bleu@39.1
HAT_wmt14enfr_xeon@208.8ms_bleu@40.0
HAT_wmt14enfr_xeon@329.4ms_bleu@41.1
HAT_wmt14enfr_xeon@394.5ms_bleu@41.4
HAT_wmt14enfr_xeon@442.0ms_bleu@41.7
link
link
link
link
link
WMT'14 En-Fr Nvidia TITAN Xp GPU 69.3ms
94.9ms
132.9ms
168.3ms
208.3ms
30.47
35.72
40.97
46.23
51.48
1.84
2.23
2.51
2.90
3.25
39.1
40.0
40.7
41.1
41.7
36.3
37.2
37.8
38.3
38.8
HAT_wmt14enfr_titanxp@69.3ms_bleu@39.1
HAT_wmt14enfr_titanxp@94.9ms_bleu@40.0
HAT_wmt14enfr_titanxp@132.9ms_bleu@40.7
HAT_wmt14enfr_titanxp@168.3ms_bleu@41.1
HAT_wmt14enfr_titanxp@208.3ms_bleu@41.7
link
link
link
link
link
WMT'19 En-De Nvidia TITAN Xp GPU 55.7ms
93.2ms
134.5ms
176.1ms
204.5ms
237.8ms
36.89
42.28
40.97
46.23
51.48
56.73
2.27
2.63
2.54
2.86
3.18
3.53
42.4
44.4
45.4
46.2
46.5
46.7
41.9
43.9
44.7
45.6
45.7
46.0
HAT_wmt19ende_titanxp@55.7ms_bleu@42.4
HAT_wmt19ende_titanxp@93.2ms_bleu@44.4
HAT_wmt19ende_titanxp@134.5ms_bleu@45.4
HAT_wmt19ende_titanxp@176.1ms_bleu@46.2
HAT_wmt19ende_titanxp@204.5ms_bleu@46.5
HAT_wmt19ende_titanxp@237.8ms_bleu@46.7
link
link
link
link
link
link
IWSLT'14 De-En Nvidia TITAN Xp GPU 45.6ms
74.5ms
109.0ms
137.8ms
168.8ms
16.82
19.98
23.13
27.33
31.54
0.78
0.93
1.13
1.32
1.52
33.4
34.2
34.5
34.7
34.8
32.5
33.3
33.6
33.8
33.9
HAT_iwslt14deen_titanxp@45.6ms_bleu@33.4
HAT_iwslt14deen_titanxp@74.5ms_bleu@34.2
HAT_iwslt14deen_titanxp@109.0ms_bleu@34.5
HAT_iwslt14deen_titanxp@137.8ms_bleu@34.7
HAT_iwslt14deen_titanxp@168.8ms_bleu@34.8
link
link
link
link
link

Download models:

python download_model.py --model-name=[model_name]
# for example
python download_model.py --model-name=HAT_wmt14ende_raspberrypi@3.5s_bleu@25.8
# to download all models
python download_model.py --download-all

Test BLEU (SacreBLEU) score:

bash configs/[task_name]/test.sh \
    [model_file] \
    configs/[task_name]/subtransformer/[model_name].yml \
    [normal|sacre]
# for example
bash configs/wmt14.en-de/test.sh \
    ./downloaded_models/HAT_wmt14ende_raspberrypi@3.5s_bleu@25.8.pt \
    configs/wmt14.en-de/subtransformer/HAT_wmt14ende_raspberrypi@3.5s_bleu@25.8.yml \
    normal
# another example
bash configs/iwslt14.de-en/test.sh \
    ./downloaded_models/HAT_iwslt14deen_titanxp@137.8ms_bleu@34.7.pt \
    configs/iwslt14.de-en/subtransformer/HAT_iwslt14deen_titanxp@137.8ms_bleu@34.7.yml \
    sacre

Test Latency, model size and FLOPs

To profile the latency, model size and FLOPs (FLOPs profiling needs torchprofile), you can run the commands below. By default, only the model size is profiled:

python train.py \
    --configs=configs/[task_name]/subtransformer/[model_name].yml \
    --sub-configs=configs/[task_name]/subtransformer/common.yml \
    [--latgpu|--latcpu|--profile-flops]
# for example
python train.py \
    --configs=configs/wmt14.en-de/subtransformer/HAT_wmt14ende_raspberrypi@3.5s_bleu@25.8.yml \
    --sub-configs=configs/wmt14.en-de/subtransformer/common.yml --latcpu
# another example
python train.py \
    --configs=configs/iwslt14.de-en/subtransformer/HAT_iwslt14deen_titanxp@137.8ms_bleu@34.7.yml \
    --sub-configs=configs/iwslt14.de-en/subtransformer/common.yml --profile-flops

Training

1. Train a SuperTransformer

The SuperTransformer is a supernet that contains many SubTransformers with weight-sharing. By default, we train WMT tasks on 8 GPUs. Please adjust --update-freq according to GPU numbers (128/x for x GPUs). Note that for IWSLT, we only train on one GPU with --update-freq=1.

python train.py --configs=configs/[task_name]/supertransformer/[search_space].yml
# for example
python train.py --configs=configs/wmt14.en-de/supertransformer/space0.yml
# another example
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --configs=configs/wmt14.en-fr/supertransformer/space0.yml --update-freq=32

In the --configs file, SuperTransformer model architecture, SubTransformer search space and training settings are specified.

We also provide pre-trained SuperTransformers for the four tasks as below. To download, run python download_model.py --model-name=[model_name].

Task search_space model_name Link
WMT'14 En-De space0 HAT_wmt14ende_super_space0 link
WMT'14 En-Fr space0 HAT_wmt14enfr_super_space0 link
WMT'19 En-De space0 HAT_wmt19ende_super_space0 link
IWSLT'14 De-En space1 HAT_iwslt14deen_super_space1 link

2. Evolutionary Search

The second step of HAT is to perform an evolutionary search in the trained SuperTransformer with a hardware latency constraint in the loop. We train a latency predictor to get fast and accurate latency feedback.

2.1 Generate a latency dataset
python latency_dataset.py --configs=configs/[task_name]/latency_dataset/[hardware_name].yml
# for example
python latency_dataset.py --configs=configs/wmt14.en-de/latency_dataset/cpu_raspberrypi.yml

hardware_name can be cpu_raspberrypi, cpu_xeon and gpu_titanxp. The --configs file contains the design space in which we sample models to get (model_architecture, real_latency) data pairs.

We provide the datasets we collect in the latency_dataset folder.

2.2 Train a latency predictor

Then train a predictor with collected dataset:

python latency_predictor.py --configs=configs/[task_name]/latency_predictor/[hardware_name].yml
# for example
python latency_predictor.py --configs=configs/wmt14.en-de/latency_predictor/cpu_raspberrypi.yml

The --configs file contains the predictor's model architecture and training settings. We provide pre-trained predictors in latency_dataset/predictors folder.

2.3 Run evolutionary search with a latency constraint
python evo_search.py --configs=[supertransformer_config_file].yml --evo-configs=[evo_settings].yml
# for example
python evo_search.py --configs=configs/wmt14.en-de/supertransformer/space0.yml --evo-configs=configs/wmt14.en-de/evo_search/wmt14ende_titanxp.yml

The --configs file points to the SuperTransformer training config file. --evo-configs file includes evolutionary search settings, and also specifies the desired latency constraint latency-constraint. Note that the feature-norm and lat-norm here should be the same as those when training the latency predictor. --write-config-path specifies the location to write out the searched SubTransformer architecture.

3. Train a Searched SubTransformer

Finally, we train the search SubTransformer from scratch:

python train.py --configs=[subtransformer_architecture].yml --sub-configs=configs/[task_name]/subtransformer/common.yml
# for example
python train.py --configs=configs/wmt14.en-de/subtransformer/wmt14ende_titanxp@200ms.yml --sub-configs=configs/wmt14.en-de/subtransformer/common.yml

--configs points to the --write-config-path in step 2.3. --sub-configs contains training settings for the SubTransformer.

After training a SubTransformer, you can test its performance with the methods in Testing section.

Dependencies

  • Python >= 3.6
  • PyTorch >= 1.0.0
  • configargparse >= 0.14
  • New model training requires NVIDIA GPUs and NCCL

Related works on efficient deep learning

MicroNet for Efficient Language Modeling

Lite Transformer with Long-Short Range Attention

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Once-for-All: Train One Network and Specialize it for Efficient Deployment

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Contact

If you have any questions, feel free to contact Hanrui Wang through Email (hanrui@mit.edu) or Github issues. Pull requests are highly welcomed!

Licence

This repository is released under the MIT license. See LICENSE for more information.

Acknowledgements

We are thankful to fairseq as the backbone of this repo.

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[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

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