File size: 2,765 Bytes
ffc90e9 ac8897e ffc90e9 ac8897e ffc90e9 ac8897e ffc90e9 ac8897e ffc90e9 ac8897e ffc90e9 346f622 ffc90e9 346f622 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
BERT-base tuned for Squadv1.1 is pruned with movement pruning algorithm in hybrid fashion, i.e. 32x32 block for self-attention layers, per-dimension grain size for ffn layers.
```
eval_exact_match = 78.5241
eval_f1 = 86.4138
eval_samples = 10784
```
This model is a replication of [block pruning paper](https://arxiv.org/abs/2109.04838) with its open-sourced codebase (forked and modified).
To reproduce this model, pls follow [documentation here](https://github.com/vuiseng9/nn_pruning/blob/reproduce-evaluation/reproduce-eval/readme.md) until step 2.
# Eval
The model can be evaluated out-of-the-box with HF QA example. Note that only pruned self-attention heads are discarded where pruned ffn dimension are sparsified instead of removal. Verified in v4.13.0, v4.9.1.
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1-block-pruning-hybrid
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
mkdir $OUTDIR
nohup python run_qa.py \
--model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
--dataset_name squad \
--do_eval \
--per_device_eval_batch_size 16 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
If the intent is to observe inference acceleration, the pruned structure in the model must be "cropped"/discarded. Follow the custom setup below.
```bash
# OpenVINO/NNCF
git clone https://github.com/vuiseng9/nncf && cd nncf
git checkout tld-poc
git reset --hard 1dec7afe7a4b567c059fcf287ea2c234980fded2
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 10a1e29d84484e48fd106f58957d9ffc89dc43c5
pip install -e .
head -n 1 examples/pytorch/question-answering/requirements.txt | xargs -i pip install {}
```
Add ```--optimize_model_before_eval``` during evaluation.
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1-block-pruning-hybrid-cropped
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
mkdir $OUTDIR
nohup python run_qa.py \
--model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
--dataset_name squad \
--optimize_model_before_eval \
--do_eval \
--per_device_eval_batch_size 128 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
|