added nncf package

Author: NoushNabi

.github/workflows/test_openvino_examples.yml

@@ -40,7 +40,7 @@ jobs:
         # ffmpeg, torchaudio and pillow are required for image classification and audio classification pipelines
         #sudo apt-get install ffmpeg
         #pip install torch torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-        pip install optimum[openvino]
+        pip install optimum[openvino] nncf
         pip install datasets==2.4.0
         pip install -r examples/openvino/audio-classification/requirements.txt
         pip install -r examples/openvino/image-classification/requirements.txt
@@ -56,7 +56,7 @@ jobs:
         python examples/openvino/audio-classification/run_audio_classification.py     --model_name_or_path facebook/wav2vec2-base     --nncf_compression_config examples/openvino/audio-classification/configs/wav2vec2-base-qat.json     --dataset_name superb     --dataset_config_name ks     --max_train_samples 50   --max_eval_samples 10  --output_dir /tmp/qat-wav2vec2-base-ft-keyword-spotting     --overwrite_output_dir     --remove_unused_columns False     --do_train     --do_eval     --learning_rate 3e-5     --max_length_seconds 1     --attention_mask False     --warmup_ratio 0.1     --num_train_epochs 5     --gradient_accumulation_steps 4     --dataloader_num_workers 4     --logging_strategy steps     --logging_steps 10     --evaluation_strategy epoch     --save_strategy epoch     --load_best_model_at_end True     --metric_for_best_model accuracy     --save_total_limit 3     --seed 42
         python examples/openvino/image-classification/run_image_classification.py     --model_name_or_path nateraw/vit-base-beans     --dataset_name beans     --max_train_samples 50   --max_eval_samples 10  --remove_unused_columns False     --do_train     --do_eval     --learning_rate 2e-5     --num_train_epochs 1     --logging_strategy steps     --logging_steps 10     --evaluation_strategy epoch     --save_strategy epoch     --save_total_limit 3     --seed 1337     --output_dir /tmp/beans_outputs/
         python examples/openvino/question-answering/run_qa.py   --model_name_or_path distilbert-base-uncased-distilled-squad   --dataset_name squad   --do_train   --do_eval     --max_train_samples 50   --learning_rate 3e-5   --num_train_epochs 1   --max_seq_length 384   --doc_stride 128   --output_dir /tmp/outputs_squad/   --overwrite_output_dir 
-        TASK_NAME=sst2   &&  python examples/openvino/text-classification/run_glue.py     --model_name_or_path bert-base-uncased     --task_name $TASK_NAME    --max_train_samples 50   --max_eval_samples 10   --output_dir /tmp/qat-bert-base-ft-$TASK_NAME     --overwrite_output_dir     --do_train     --do_eval     --max_seq_length 128     --learning_rate 1e-5     --optim adamw_torch     --num_train_epochs 3     --logging_steps 10     --evaluation_strategy steps     --eval_steps 20     --save_strategy epoch     --seed 42
+        TASK_NAME=sst2   &&  python examples/openvino/text-classification/run_glue.py     --model_name_or_path bert-base-uncased     --task_name $TASK_NAME    --max_train_samples 20   --max_eval_samples 5   --output_dir /tmp/qat-bert-base-ft-$TASK_NAME     --overwrite_output_dir     --do_train     --do_eval     --max_seq_length 128     --learning_rate 1e-5     --optim adamw_torch     --num_train_epochs 1     --logging_steps 10     --evaluation_strategy steps     --eval_steps 5     --save_strategy epoch     --seed 42
 
         #pip install -r examples/openvino/stable-diffusion/requirements.txt
         #python examples/openvino/stable-diffusion/train_text_to_image_qat.py   --ema_device="cpu"  --use_kd  --model_id="svjack/Stable-Diffusion-Pokemon-en" --max_train_samples 20  --center_crop   --random_flip   --dataloader_num_workers=2  --dataset_name="lambdalabs/pokemon-blip-captions"   --max_train_steps=1   --output_dir=sd-quantized-pokemon