Update documentation

README.md

@@ -1,7 +1,7 @@
-TODO: Update documentation
-
-Quantized version of the following. Scores are correct.
-This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) on Squadv1 dataset.
+This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-pruneofa-90pc-bt```](https://huggingface.co/vuiseng9/bert-base-squadv1-pruneofa-90pc-bt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
+1. magnitude sparsification at 0% upon initialization. Custom reverse masking and sparsity freezing are applied.
+2. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
+3. Custom distillation with large model ```bert-large-uncased-whole-word-masking-finetuned-squad```  
 
 ```
   eval_exact_match = 80.6623
@@ -10,21 +10,97 @@ This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggi
 ``` 
 
 
+# Setup
+```bash
+# OpenVINO/NNCF
+git clone https://github.com/vuiseng9/nncf && cd nncf
+git checkout tld-poc
+git reset --hard 5647610d5ee2bf9f1324604e6579bca1c391e260
+python setup.py develop
+pip install -r examples/torch/requirements.txt
+
+# Huggingface nn_pruning
+git clone https://github.com/vuiseng9/nn_pruning && cd nn_pruning
+git checkout reproduce-evaluation
+git reset --hard 2d4e196d694c465e43e5fbce6c3836d0a60e1446
+pip install -e ".[dev]"
+
+# Huggingface Transformers
+git clone https://github.com/vuiseng9/transformers && cd transformers
+git checkout tld-poc
+git reset --hard 5dd7402e9a316041dea4ff67508c01047323616e
+pip install -e .
+head -n 1 examples/pytorch/question-answering/requirements.txt | xargs -i pip install {}
+
+# Additional dependencies
+pip install onnx
+```
+
 # Train
-use https://github.com/IntelLabs/Model-Compression-Research-Package.git
-see ```pruneofa-transfer-learning.sh```
+
+```bash
+wget https://huggingface.co/vuiseng9/bert-base-squadv1-pruneofa-90pc-bt-qat-lt/raw/main/nncf_bert_squad_sparsity.json
+NNCF_CFG=/path/to/downloaded_nncf_cfg_above #to-revise
+
+OUTROOT=/path/to/train_output_root #to-revise
+WORKDIR=transformers/examples/pytorch/question-answering #to-revise
+RUNID=bert-base-squadv1-pruneofa-90pc-bt-qat-lt
+
+cd $WORKDIR
+
+OUTDIR=$OUTROOT/$RUNID
+mkdir -p $OUTDIR
+
+export CUDA_VISIBLE_DEVICES=0
+NEPOCH=5
+
+python run_qa.py \
+    --model_name_or_path vuiseng9/bert-base-squadv1-pruneofa-90pc-bt \
+    --pruneofa_qat \
+    --dataset_name squad \
+    --do_eval \
+    --do_train \
+    --evaluation_strategy steps \
+    --eval_steps 250 \
+    --learning_rate 3e-5 \
+    --lr_scheduler_type cosine_with_restarts \
+    --warmup_ratio 0.25 \
+    --cosine_cycles 1 \
+    --teacher bert-large-uncased-whole-word-masking-finetuned-squad \
+    --teacher_ratio 0.9 \
+    --num_train_epochs $NEPOCH \
+    --per_device_eval_batch_size 128 \
+    --per_device_train_batch_size 16 \
+    --max_seq_length 384 \
+    --doc_stride 128 \
+    --save_steps 250 \
+    --nncf_config $NNCF_CFG \
+    --logging_steps 1 \
+    --overwrite_output_dir \
+    --run_name $RUNID \
+    --output_dir $OUTDIR
+```
 
 # Eval
+This repo must be cloned locally.
 ```bash
+git clone https://huggingface.co/vuiseng9/bert-base-squadv1-pruneofa-90pc-bt-qat-lt
+MODELROOT=/path/to/cloned_repo_above #to-revise
+
 export CUDA_VISIBLE_DEVICES=0
 
-OUTDIR=eval-bert-base-squadv1-pruneofa-90pc-bt
-WORKDIR=transformers/examples/pytorch/question-answering
+OUTDIR=eval-bert-base-squadv1-pruneofa-90pc-bt-qat-lt
+WORKDIR=transformers/examples/pytorch/question-answering #to-revise
 cd $WORKDIR
+mkdir $OUTDIR
+
 
 nohup python run_qa.py  \
     --model_name_or_path vuiseng9/bert-base-squadv1-pruneofa-90pc-bt  \
     --dataset_name squad  \
+    --qat_checkpoint $MODELROOT/checkpoint-22000  \
+    --nncf_config $MODELROOT/nncf_bert_squad_sparsity.json  \
+    --to_onnx $OUTDIR/bert-base-squadv1-pruneofa-90pc-bt-qat-lt.onnx  \
     --do_eval  \
     --per_device_eval_batch_size 128  \
     --max_seq_length 384  \