Add DeeBERT (entropy-based early exiting for *BERT) (#5477)

* Add deebert code

* Add readme of deebert

* Add test for deebert

Update test for Deebert

* Update DeeBert (README, class names, function refactoring); remove requirements.txt

* Format update

* Update test

* Update readme and model init methods

examples/deebert/train_deebert.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=0
+
+PATH_TO_DATA=/h/xinji/projects/GLUE
+
+MODEL_TYPE=bert  # bert or roberta
+MODEL_SIZE=base  # base or large
+DATASET=MRPC  # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
+
+MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
+EPOCHS=10
+if [ $MODEL_TYPE = 'bert' ]
+then
+  EPOCHS=3
+  MODEL_NAME=${MODEL_NAME}-uncased
+fi
+
+
+python -u run_glue_deebert.py \
+  --model_type $MODEL_TYPE \
+  --model_name_or_path $MODEL_NAME \
+  --task_name $DATASET \
+  --do_train \
+  --do_eval \
+  --do_lower_case \
+  --data_dir $PATH_TO_DATA/$DATASET \
+  --max_seq_length 128 \
+  --per_gpu_eval_batch_size=1 \
+  --per_gpu_train_batch_size=8 \
+  --learning_rate 2e-5 \
+  --num_train_epochs $EPOCHS \
+  --overwrite_output_dir \
+  --seed 42 \
+  --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
+  --plot_data_dir ./results/ \
+  --save_steps 0 \
+  --overwrite_cache \
+  --eval_after_first_stage