Awesome

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks

Microsoft Research [Task-specific] [Task-agnostic with Task Transfer]

Releasing [XtremeDistilTransformers] with Tensorflow 2.3 and HuggingFace Transformers with an unified API with the following features:

• Distil any supported pre-trained language models as teachers (e.g, Bert, Electra, Roberta)
• Initialize student model with any pre-trained model (e.g, MiniLM, DistilBert, TinyBert), or initialize from scratch
• Multilingual text classification and sequence tagging
• Distil multiple hidden states from teacher
• Distil deep attention networks from teacher
• Pairwise and instance-level classification tasks (e.g, MNLI, MRPC, SST)
• Progressive knowledge transfer with gradual unfreezing
• Fast mixed precision training for distillation (e.g, mixed_float16, mixed_bfloat16)
• ONNX runtime inference

You can use the following task-agnostic pre-distilled checkpoints from XtremeDistilTransformers for (only) fine-tuning on labeled data from downstream tasks:

• 6/256 xtremedistil pre-trained checkpoint
• 6/384 xtremedistil pre-trained checkpoint
• 12/384 xtremedistil pre-trained checkpoint

For further performance improvement, initialize XtremeDistilTransformers with any of the above pre-distilled checkpoints for task-specific distillation with additional unlabeled data from the downstream task for the best performance.

The following table shows the performance of the above checkpoints on GLUE dev set and SQuAD-v2.

Install requirements pip install -r requirements.txt

Tested with tensorflow 2.3.1, transformers 4.1.1, torch 1.6.0, python 3.6.9 and CUDA 10.2

Sample usages for distilling different pre-trained language models

Training

Sequence Labeling for Wiki NER

PYTHONHASHSEED=42 python run_xtreme_distil.py 
--task $$PT_DATA_DIR/datasets/NER 
--model_dir $$PT_OUTPUT_DIR 
--seq_len 32  
--transfer_file $$PT_DATA_DIR/datasets/NER/unlabeled.txt 
--do_NER 
--pt_teacher TFBertModel 
--pt_teacher_checkpoint bert-base-multilingual-cased 
--student_distil_batch_size 256 
--student_ft_batch_size 32
--teacher_batch_size 128  
--pt_student_checkpoint microsoft/xtremedistil-l6-h384-uncased 
--distil_chunk_size 10000 
--teacher_model_dir $$PT_OUTPUT_DIR 
--distil_multi_hidden_states 
--distil_attention 
--compress_word_embedding 
--freeze_word_embedding
--opt_policy mixed_float16

Text Classification for MNLI

PYTHONHASHSEED=42 python run_xtreme_distil.py 
--task $$PT_DATA_DIR/glue_data/MNLI 
--model_dir $$PT_OUTPUT_DIR 
--seq_len 128  
--transfer_file $$PT_DATA_DIR/glue_data/MNLI/train.tsv 
--do_pairwise 
--pt_teacher TFElectraModel 
--pt_teacher_checkpoint google/electra-base-discriminator 
--student_distil_batch_size 128  
--student_ft_batch_size 32
--pt_student_checkpoint microsoft/xtremedistil-l6-h384-uncased 
--teacher_model_dir $$PT_OUTPUT_DIR 
--teacher_batch_size 32
--distil_chunk_size 300000
--opt_policy mixed_float16

Alternatively, use TinyBert pre-trained student model checkpoint as --pt_student_checkpoint nreimers/TinyBERT_L-4_H-312_v2

Arguments

- task folder contains
	-- train/dev/test '.tsv' files with text and classification labels / token-wise tags (space-separated)
	--- Example 1: feel good about themselves <tab> 1
	--- Example 2: '' Atelocentra '' Meyrick , 1884 <tab> O B-LOC O O O O
	-- label files containing class labels for sequence labeling
	-- transfer file containing unlabeled data
	
- model_dir to store/restore model checkpoints

- task arguments
-- do_pairwise for pairwise classification tasks like MNLI and MRPC
-- do_NER for sequence labeling

- teacher arguments
-- pt_teacher for teacher model to distil (e.g., TFBertModel, TFRobertaModel, TFElectraModel)
-- pt_teacher_checkpoint for pre-trained teacher model checkpoints (e.g., bert-base-multilingual-cased, roberta-large, google/electra-base-discriminator)

- student arguments
-- pt_student_checkpoint to initialize from pre-trained small student models (e.g., MiniLM, DistilBert, TinyBert)
-- instead of pre-trained checkpoint, initialize a raw student from scratch with
--- hidden_size
--- num_hidden_layers
--- num_attention_heads

- distillation features
-- distil_multi_hidden_states to distil multiple hidden states from the teacher
-- distil_attention to distil deep attention network of the teacher
-- compress_word_embedding to initialize student word embedding with SVD-compressed teacher word embedding (useful for multilingual distillation)
-- freeze_word_embedding to keep student word embeddings frozen during distillation (useful for multilingual distillation)
-- opt_policy (e.g., mixed_float16 for GPU and mixed_bfloat16 for TPU)
-- distil_chunk_size for using transfer data in chunks during distillation (reduce for OOM issues, checkpoints are saved after every distil_chunk_size steps)

Model Outputs

The above training code generates intermediate model checkpoints to continue the training in case of abrupt termination instead of starting from scratch -- all saved in $$PT_OUTPUT_DIR. The final output of the model consists of (i) xtremedistil.h5 with distilled model weights, (ii) xtremedistil-config.json with the training configuration, and (iii) word_embedding.npy for the input word embeddings from the student model.

Prediction

PYTHONHASHSEED=42 python run_xtreme_distil_predict.py 
--do_eval 
--model_dir $$PT_OUTPUT_DIR 
--do_predict 
--pred_file ../../datasets/NER/unlabeled.txt
--opt_policy mixed_float16

*ONNX Runtime Inference

You can also use ONXX Runtime for inference speedup with the following script:

PYTHONHASHSEED=42 python run_xtreme_distil_predict_onnx.py 
--do_eval 
--model_dir $$PT_OUTPUT_DIR 
--do_predict 
--pred_file ../../datasets/NER/unlabeled.txt

For details on ONNX Runtime Inference, environment and arguments refer to this Notebook The script is for online inference with batch_size=1.

*Continued Fine-tuning

You can continue fine-tuning the distilled/compressed student model on more labeled data with the following script:

PYTHONHASHSEED=42 python run_xtreme_distil_ft.py --model_dir $$PT_OUTPUT_DIR 

If you use this code, please cite:

@misc{mukherjee2021xtremedistiltransformers,
      title={XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation}, 
      author={Subhabrata Mukherjee and Ahmed Hassan Awadallah and Jianfeng Gao},
      year={2021},
      eprint={2106.04563},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{mukherjee-hassan-awadallah-2020-xtremedistil,
    title = "{X}treme{D}istil: Multi-stage Distillation for Massive Multilingual Models",
    author = "Mukherjee, Subhabrata  and
      Hassan Awadallah, Ahmed",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.202",
    pages = "2221--2234",
}

Code is released under MIT license.