| Model | -Domain | -Method | -Examples | -
|---|---|---|---|
| gpt_j | -Natural Language Processing | -Weight-Only Quantization | -link | -
| Static Quantization (IPEX) | -link | -||
| llama2_7b | -Natural Language Processing | -Weight-Only Quantization | -link | -
| Static Quantization (IPEX) | -link | -||
| opt_125m | -Natural Language Processing | -Static Quantization (IPEX) | -link | -
| Static Quantization (PT2E) | -link | -||
| Weight-Only Quantization | -link | -||
| resnet18 | -Image Recognition | -Mixed Precision | -link | -
| Static Quantization | -link | -
| Model | -Domain | -Method | -Examples | -
|---|---|---|---|
| bert_large_squad_model_zoo | -Natural Language Processing | -Post-Training Static Quantization | -link | -
| transformer_lt | -Natural Language Processing | -Post-Training Static Quantization | -link | -
| inception_v3 | -Image Recognition | -Post-Training Static Quantization | -link | -
| mobilenetv2 | -Image Recognition | -Post-Training Static Quantization | -link | -
| resnetv2_50 | -Image Recognition | -Post-Training Static Quantization | -link | -
| vgg16 | -Image Recognition | -Post-Training Static Quantization | -link | -
| ViT | -Image Recognition | -Post-Training Static Quantization | -link | -
| GraphSage | -Graph Networks | -Post-Training Static Quantization | -link | -
| yolo_v5 | -Object Detection | -Post-Training Static Quantization | -link | -
| faster_rcnn_resnet50 | -Object Detection | -Post-Training Static Quantization | -link | -
| mask_rcnn_inception_v2 | -Object Detection | -Post-Training Static Quantization | -link | -
| ssd_mobilenet_v1 | -Object Detection | -Post-Training Static Quantization | -link | -
| wide_deep_large_ds | -Recommendation | -Post-Training Static Quantization | -link | -
| 3dunet-mlperf | -Semantic Image Segmentation | -Post-Training Static Quantization | -link | -
| Model | -Domain | -Approach | -Examples | -
|---|---|---|---|
| ResNet50 V1.0 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| ResNet50 V1.5 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| ResNet101 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| MobileNet V1 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| MobileNet V2 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| MobileNet V3 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| Inception V1 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| Inception V2 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| Inception V3 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| Inception V4 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| Inception ResNet V2 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| VGG16 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| VGG19 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| ResNet V2 50 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| ResNet V2 101 | -Image Recognition | -Post-Training Static Quantization | -pb / keras | -
| ResNet V2 152 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| DenseNet121 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| DenseNet161 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| DenseNet169 | -Image Recognition | -Post-Training Static Quantization | -pb | -
| EfficientNet B0 | -Image Recognition | -Post-Training Static Quantization | -ckpt | -
| Xception | -Image Recognition | -Post-Training Static Quantization | -keras | -
| ResNet V2 | -Image Recognition | -Quantization-Aware Training | -keras | -
| EfficientNet V2 B0 | -Image Recognition | -Post-Training Static Quantization | -SavedModel | -BERT base MRPC | -Natural Language Processing | -Post-Training Static Quantization | -ckpt | - -
| BERT large SQuAD (Model Zoo) | -Natural Language Processing | -Post-Training Static Quantization | -pb | -
| BERT large SQuAD | -Natural Language Processing | -Post-Training Static Quantization | -pb | -
| DistilBERT base | -Natural Language Processing | -Post-Training Static Quantization | -pb | -
| Transformer LT | -Natural Language Processing | -Post-Training Static Quantization | -pb | -
| Transformer LT MLPerf | -Natural Language Processing | -Post-Training Static Quantization | -pb | -
| SSD ResNet50 V1 | -Object Detection | -Post-Training Static Quantization | -pb / ckpt | -
| SSD MobileNet V1 | -Object Detection | -Post-Training Static Quantization | -pb / ckpt | -
| Faster R-CNN Inception ResNet V2 | -Object Detection | -Post-Training Static Quantization | -pb / SavedModel | -
| Faster R-CNN ResNet101 | -Object Detection | -Post-Training Static Quantization | -pb / SavedModel | -
| Faster R-CNN ResNet50 | -Object Detection | -Post-Training Static Quantization | -pb | -
| Mask R-CNN Inception V2 | -Object Detection | -Post-Training Static Quantization | -pb / ckpt | -
| SSD ResNet34 | -Object Detection | -Post-Training Static Quantization | -pb | -
| YOLOv3 | -Object Detection | -Post-Training Static Quantization | -pb | -
| Wide & Deep | -Recommendation | -Post-Training Static Quantization | -pb | -
| Arbitrary Style Transfer | -Style Transfer | -Post-Training Static Quantization | -ckpt | -
| OPT | -Natural Language Processing | -Post-Training Static Quantization | -pb (smooth quant) | -
| GPT2 | -Natural Language Processing | -Post-Training Static Quantization | -pb (smooth quant) | -
| ViT | -Image Recognition | -Post-Training Static Quantization | -pb | -
| GraphSage | -Graph Networks | -Post-Training Static Quantization | -pb | -
| EleutherAI/gpt-j-6B | -Natural Language Processing | -Post-Training Static Quantization | -saved_model (smooth quant) | -
| Student Model | -Teacher Model | -Domain | -Approach | -Examples | -
|---|---|---|---|---|
| MobileNet | -DenseNet201 | -Image Recognition | -Knowledge Distillation | -pb | -
| Model | -Domain | -Approach | -Examples | -
|---|---|---|---|
| ResNet V2 | -Image Recognition | -Structured (4x1, 2in4) | -keras | -
| ViT | -Image Recognition | -Structured (4x1, 2in4) | -keras | -
| Model | -Domain | -Approach | -Examples | -
|---|---|---|---|
| ResNet50 V1.5 | -Image Recognition | -TF2ONNX | -int8 fp32 | -
| Model | -Domain | -Approach | -Examples | -
|---|---|---|---|
| ResNet18 | -Image Recognition | -Post-Training Static Quantization | -fx / ipex | -
| ResNet18 | -Image Recognition | -Quantization-Aware Training | -fx | -
| ResNet50 | -Image Recognition | -Post-Training Static Quantization | -fx / ipex | -
| ResNet50 | -Image Recognition | -Quantization-Aware Training | -fx | -
| ResNeXt101_32x16d_wsl | -Image Recognition | -Post-Training Static Quantization | -ipex | -
| ResNeXt101_32x8d | -Image Recognition | -Post-Training Static Quantization | -fx | -
| Se_ResNeXt50_32x4d | -Image Recognition | -Post-Training Static Quantization | -fx | -
| Inception V3 | -Image Recognition | -Post-Training Static Quantization | -fx | -
| MobileNet V2 | -Image Recognition | -Post-Training Static Quantization | -fx | -
| PeleeNet | -Image Recognition | -Post-Training Static Quantization | -fx | -
| ResNeSt50 | -Image Recognition | -Post-Training Static Quantization | -fx | -
| 3D-UNet | -Image Recognition | -Post-Training Static Quantization | -fx | -
| SSD ResNet34 | -Object Detection | -Post-Training Static Quantization | -fx / ipex | -
| YOLOv3 | -Object Detection | -Post-Training Static Quantization | -fx | -
| Mask R-CNN | -Object Detection | -Post-Training Static Quantization | -fx | -
| DLRM | -Recommendation | -Post-Training Static Quantization | -ipex / fx | -
| HuBERT | -Speech Recognition | -Post-Training Static Quantization | -fx | -
| HuBERT | -Speech Recognition | -Post-Training Dynamic Quantization | -fx | -
| BlendCNN | -Natural Language Processing | -Post-Training Static Quantization | -ipex | -
| bert-large-uncased-whole-word-masking-finetuned-squad | -Natural Language Processing | -Post-Training Static Quantization | -fx / ipex(Intel GPU) | -
| distilbert-base-uncased-distilled-squad | -Natural Language Processing | -Post-Training Static Quantization | -ipex | -
| yoshitomo-matsubara/bert-large-uncased-rte | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| Intel/xlm-roberta-base-mrpc | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| textattack/distilbert-base-uncased-MRPC | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| textattack/albert-base-v2-MRPC | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| Intel/xlm-roberta-base-mrpc | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| yoshitomo-matsubara/bert-large-uncased-rte | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| Intel/bert-base-uncased-mrpc | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| textattack/bert-base-uncased-CoLA | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| textattack/bert-base-uncased-STS-B | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| gchhablani/bert-base-cased-finetuned-sst2 | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| ModelTC/bert-base-uncased-rte | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| textattack/bert-base-uncased-QNLI | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| yoshitomo-matsubara/bert-large-uncased-cola | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| textattack/distilbert-base-uncased-MRPC | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| Intel/xlnet-base-cased-mrpc | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| textattack/roberta-base-MRPC | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| Intel/camembert-base-mrpc | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| t5-small | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| Helsinki-NLP/opus-mt-en-ro | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| lvwerra/pegasus-samsum | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| google/reformer-crime-and-punishment | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| EleutherAI/gpt-j-6B | -Natural Language Processing | -Post-Training Static Quantization | -fx / smooth quant | -
| EleutherAI/gpt-j-6B | -Natural Language Processing | -Post-Training Weight Only Quantization | -weight_only | -
| abeja/gpt-neox-japanese-2.7b | -Natural Language Processing | -Post-Training Static Quantization | -fx | -
| bigscience/bloom | -Natural Language Processing | -Post-Training Static Quantization | -smooth quant | -
| facebook/opt | -Natural Language Processing | -Post-Training Static Quantization | -smooth quant | -
| SD Diffusion | -Text to Image | -Post-Training Static Quantization | -fx | -
| openai/whisper-large | -Speech Recognition | -Post-Training Dynamic Quantization | -fx | -
| torchaudio/wav2vec2 | -Speech Recognition | -Post-Training Dynamic Quantization | -fx | -
| Model | -Domain | -Approach | -Examples | -
|---|---|---|---|
| T5 Large | -Natural Language Processing | -Post-Training Dynamic Quantization | -fx | -
| Flan T5 Large | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -fx | -
| Model | -Domain | -Pruning Type | -Approach | -Examples | -
|---|---|---|---|---|
| Distilbert-base-uncased | -Natural Language Processing (text classification) | -Structured (4x1, 2in4), Unstructured | -Snip-momentum | -eager | -
| Bert-mini | -Natural Language Processing (text classification) | -Structured (4x1, 2in4, per channel), Unstructured | -Snip-momentum | -eager | -
| Distilbert-base-uncased | -Natural Language Processing (question answering) | -Structured (4x1, 2in4), Unstructured | -Snip-momentum | -eager | -
| Bert-mini | -Natural Language Processing (question answering) | -Structured (4x1, 2in4), Unstructured | -Snip-momentum | -eager | -
| Bert-base-uncased | -Natural Language Processing (question answering) | -Structured (4x1, 2in4), Unstructured | -Snip-momentum | -eager | -
| Bert-large | -Natural Language Processing (question answering) | -Structured (4x1, 2in4), Unstructured | -Snip-momentum | -eager | -
| Flan-T5-small | -Natural Language Processing (translation) | -Structured (4x1) | -Snip-momentum | -eager | -
| YOLOv5s6 | -Object Detection | -Structured (4x1, 2in4), Unstructured | -Snip-momentum | -eager | -
| ResNet50 | -Image Recognition | -Structured (2x1) | -Snip-momentum | -eager | -
| Bert-base | -Question Answering | -Structured (channel, multi-head attention) | -Snip-momentum | -eager | -
| Bert-large | -Question Answering | -Structured (channel, multi-head attention) | -Snip-momentum | -eager | -
| Student Model | -Teacher Model | -Domain | -Approach | -Examples | -
|---|---|---|---|---|
| CNN-2 | -CNN-10 | -Image Recognition | -Knowledge Distillation | -eager | -
| MobileNet V2-0.35 | -WideResNet40-2 | -Image Recognition | -Knowledge Distillation | -eager | -
| ResNet18|ResNet34|ResNet50|ResNet101 | -ResNet18|ResNet34|ResNet50|ResNet101 | -Image Recognition | -Knowledge Distillation | -eager | -
| ResNet18|ResNet34|ResNet50|ResNet101 | -ResNet18|ResNet34|ResNet50|ResNet101 | -Image Recognition | -Self Distillation | -eager | -
| VGG-8 | -VGG-13 | -Image Recognition | -Knowledge Distillation | -eager | -
| BlendCNN | -BERT-Base | -Natural Language Processing | -Knowledge Distillation | -eager | -
| DistilBERT | -BERT-Base | -Natural Language Processing | -Knowledge Distillation | -eager | -
| BiLSTM | -RoBERTa-Base | -Natural Language Processing | -Knowledge Distillation | -eager | -
| TinyBERT | -BERT-Base | -Natural Language Processing | -Knowledge Distillation | -eager | -
| BERT-3 | -BERT-Base | -Natural Language Processing | -Knowledge Distillation | -eager | -
| DistilRoBERTa | -RoBERTa-Large | -Natural Language Processing | -Knowledge Distillation | -eager | -
| Model | Domain | -Approach | +Method | Examples |
|---|---|---|---|---|
| ResNet50 | -Image Recognition | -Multi-shot: Pruning and PTQ |
- link | -|
| ResNet50 | -Image Recognition | -One-shot: QAT during Pruning |
- link | -|
| Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | -Natural Language Processing (question-answering) | -One-shot: Pruning, Distillation and QAT |
- link | -|
| Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | -Natural Language Processing (text-classification) | -One-shot: Pruning, Distillation and QAT |
- link | -
| Model | -Domain | -Approach | -Examples | -
|---|---|---|---|
| ResNet18 | -Image Recognition | -PT2ONNX | -int8 fp32 | -
| ResNet50 | -Image Recognition | -PT2ONNX | -int8 fp32 | -
| bert base MRPC | -Natural Language Processing | -PT2ONNX | -int8 fp32 | -
| bert large MRPC | -Natural Language Processing | -PT2ONNX | -int8 fp32 | -
| gpt_j | +Natural Language Processing | +Weight-Only Quantization | +link | +
| Static Quantization (IPEX) | +link | +||
| llama2_7b | +Natural Language Processing | +Weight-Only Quantization | +link | +
| Static Quantization (IPEX) | +link | +||
| opt_125m | +Natural Language Processing | +Static Quantization (IPEX) | +link | +
| Static Quantization (PT2E) | +link | +||
| Weight-Only Quantization | +link | +||
| resnet18 | +Image Recognition | +Mixed Precision | +link | +
| Static Quantization | +link | +
| Model | Domain | -Approach | +Method | Examples |
|---|---|---|---|---|
| ResNet50 V1.5 | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| ResNet50 V1.5 MLPerf | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| VGG16 | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| MobileNet V2 | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| MobileNet V3 MLPerf | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| AlexNet (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| CaffeNet (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| DenseNet (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops | -|
| EfficientNet (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| FCN (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| GoogleNet (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| Inception V1 (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| MNIST (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops | -|
| MobileNet V2 (ONNX Model Zoo) | -Image Recognition | -Post-Training Static Quantization | -qlinearops / qdq | -|
| ResNet50 V1.5 (ONNX Model Zoo) | -Image Recognition | +|||
| bert_large_squad_model_zoo | +Natural Language Processing | Post-Training Static Quantization | -qlinearops / qdq | -|
| ShuffleNet V2 (ONNX Model Zoo) | -Image Recognition | +link | +||
| transformer_lt | +Natural Language Processing | Post-Training Static Quantization | -qlinearops / qdq | -|
| SqueezeNet (ONNX Model Zoo) | +link | +|||
| inception_v3 | Image Recognition | Post-Training Static Quantization | -qlinearops / qdq | -|
| VGG16 (ONNX Model Zoo) | +link | +|||
| mobilenetv2 | Image Recognition | Post-Training Static Quantization | -qlinearops / qdq | -|
| ZFNet (ONNX Model Zoo) | +link | +|||
| resnetv2_50 | Image Recognition | Post-Training Static Quantization | -qlinearops / qdq | -|
| ArcFace (ONNX Model Zoo) | +link | +|||
| vgg16 | Image Recognition | Post-Training Static Quantization | -qlinearops | -|
| BEiT | +link | +|||
| ViT | Image Recognition | Post-Training Static Quantization | -qlinearops | -|
| BERT base MRPC | -Natural Language Processing | -Post-Training Static Quantization | -integerops / qdq | -|
| BERT base MRPC | -Natural Language Processing | -Post-Training Dynamic Quantization | -integerops | -|
| DistilBERT base MRPC | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -integerops / qdq | -|
| Mobile bert MRPC | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -integerops / qdq | -|
| Roberta base MRPC | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -integerops / qdq | -|
| BERT SQuAD (ONNX Model Zoo) | -Natural Language Processing | -Post-Training Dynamic Quantization | -integerops | -|
| GPT2 lm head WikiText (ONNX Model Zoo) | -Natural Language Processing | -Post-Training Dynamic Quantization | -integerops | -|
| MobileBERT SQuAD MLPerf (ONNX Model Zoo) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -integerops / qdq | -|
| BiDAF (ONNX Model Zoo) | -Natural Language Processing | -Post-Training Dynamic Quantization | -integerops | -|
| Spanbert SQuAD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| Bert base multilingual cased SQuAD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| DistilBert base uncased SQuAD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| BERT large uncased whole word masking SQuAD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| Roberta large SQuAD v2 (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| GPT2 WikiText (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| DistilGPT2 WikiText (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| LayoutLMv3 FUNSD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| LayoutLMv2 FUNSD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| LayoutLM FUNSD (HuggingFace) | -Natural Language Processing | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| SSD MobileNet V1 | -Object Detection | -Post-Training Static Quantization | -qlinearops / qdq | -|
| SSD MobileNet V2 | -Object Detection | -Post-Training Static Quantization | -qlinearops / qdq | -|
| Table Transformer Structure Recognition | -Object Detection | -Post-Training Static Quantization | -qlinearops | -|
| Table Transformer Detection | -Object Detection | -Post-Training Static Quantization | -qlinearops | -|
| SSD MobileNet V1 (ONNX Model Zoo) | -Object Detection | -Post-Training Static Quantization | -qlinearops / qdq | -|
| DUC (ONNX Model Zoo) | -Object Detection | -Post-Training Static Quantization | -qlinearops | -|
| Faster R-CNN (ONNX Model Zoo) | -Object Detection | -Post-Training Static Quantization | -qlinearops / qdq | -|
| Mask R-CNN (ONNX Model Zoo) | -Object Detection | +link | +||
| GraphSage | +Graph Networks | Post-Training Static Quantization | -qlinearops / qdq | -|
| SSD (ONNX Model Zoo) | +link | +|||
| yolo_v5 | Object Detection | Post-Training Static Quantization | -qlinearops / qdq | -|
| Tiny YOLOv3 (ONNX Model Zoo) | +link | +|||
| faster_rcnn_resnet50 | Object Detection | Post-Training Static Quantization | -qlinearops | -|
| YOLOv3 (ONNX Model Zoo) | +link | +|||
| mask_rcnn_inception_v2 | Object Detection | Post-Training Static Quantization | -qlinearops | -|
| YOLOv4 (ONNX Model Zoo) | +link | +|||
| ssd_mobilenet_v1 | Object Detection | Post-Training Static Quantization | -qlinearops | -|
| Emotion FERPlus (ONNX Model Zoo) | -Body Analysis | +link | +||
| wide_deep_large_ds | +Recommendation | Post-Training Static Quantization | -qlinearops | -|
| Ultra Face (ONNX Model Zoo) | -Body Analysis | +link | +||
| 3dunet-mlperf | +Semantic Image Segmentation | Post-Training Static Quantization | -qlinearops | -|
| GPT-J-6B (HuggingFace) | -Text Generation | -Post-Training Dynamic / Static Quantization | -- integerops / qlinearops - | -|
| Llama-7B (HuggingFace) | -Text Generation | -Static / Weight Only Quantization | -- qlinearops / weight_only - | -link | + +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet50 V1.0 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| ResNet50 V1.5 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| ResNet101 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| MobileNet V1 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| MobileNet V2 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| MobileNet V3 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| Inception V1 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| Inception V2 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| Inception V3 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| Inception V4 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| Inception ResNet V2 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| VGG16 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| VGG19 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| ResNet V2 50 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| ResNet V2 101 | +Image Recognition | +Post-Training Static Quantization | +pb / keras | +
| ResNet V2 152 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| DenseNet121 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| DenseNet161 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| DenseNet169 | +Image Recognition | +Post-Training Static Quantization | +pb | +
| EfficientNet B0 | +Image Recognition | +Post-Training Static Quantization | +ckpt | +
| Xception | +Image Recognition | +Post-Training Static Quantization | +keras | +
| ResNet V2 | +Image Recognition | +Quantization-Aware Training | +keras | +
| EfficientNet V2 B0 | +Image Recognition | +Post-Training Static Quantization | +SavedModel | +
| BERT base MRPC | +Natural Language Processing | +Post-Training Static Quantization | +ckpt | +
| BERT large SQuAD (Model Zoo) | +Natural Language Processing | +Post-Training Static Quantization | +pb | +
| BERT large SQuAD | +Natural Language Processing | +Post-Training Static Quantization | +pb | +
| DistilBERT base | +Natural Language Processing | +Post-Training Static Quantization | +pb | +
| Transformer LT | +Natural Language Processing | +Post-Training Static Quantization | +pb | +
| Transformer LT MLPerf | +Natural Language Processing | +Post-Training Static Quantization | +pb | +
| SSD ResNet50 V1 | +Object Detection | +Post-Training Static Quantization | +pb / ckpt | +
| SSD MobileNet V1 | +Object Detection | +Post-Training Static Quantization | +pb / ckpt | +
| Faster R-CNN Inception ResNet V2 | +Object Detection | +Post-Training Static Quantization | +pb / SavedModel | +
| Faster R-CNN ResNet101 | +Object Detection | +Post-Training Static Quantization | +pb / SavedModel | +
| Faster R-CNN ResNet50 | +Object Detection | +Post-Training Static Quantization | +pb | +
| Mask R-CNN Inception V2 | +Object Detection | +Post-Training Static Quantization | +pb / ckpt | +
| SSD ResNet34 | +Object Detection | +Post-Training Static Quantization | +pb | +
| YOLOv3 | +Object Detection | +Post-Training Static Quantization | +pb | +
| Wide & Deep | +Recommendation | +Post-Training Static Quantization | +pb | +
| Arbitrary Style Transfer | +Style Transfer | +Post-Training Static Quantization | +ckpt | +
| OPT | +Natural Language Processing | +Post-Training Static Quantization | +pb (smooth quant) | +
| GPT2 | +Natural Language Processing | +Post-Training Static Quantization | +pb (smooth quant) | +
| ViT | +Image Recognition | +Post-Training Static Quantization | +pb | +
| GraphSage | +Graph Networks | +Post-Training Static Quantization | +pb | +
| EleutherAI/gpt-j-6B | +Natural Language Processing | +Post-Training Static Quantization | +saved_model (smooth quant) | +
| Student Model | +Teacher Model | +Domain | +Approach | +Examples | +
|---|---|---|---|---|
| MobileNet | +DenseNet201 | +Image Recognition | +Knowledge Distillation | +pb | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet V2 | +Image Recognition | +Structured (4x1, 2in4) | +keras | +
| ViT | +Image Recognition | +Structured (4x1, 2in4) | +keras | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet50 V1.5 | +Image Recognition | +TF2ONNX | +int8 fp32 | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet18 | +Image Recognition | +Post-Training Static Quantization | +fx / ipex | +
| ResNet18 | +Image Recognition | +Quantization-Aware Training | +fx | +
| ResNet50 | +Image Recognition | +Post-Training Static Quantization | +fx / ipex | +
| ResNet50 | +Image Recognition | +Quantization-Aware Training | +fx | +
| ResNeXt101_32x16d_wsl | +Image Recognition | +Post-Training Static Quantization | +ipex | +
| ResNeXt101_32x8d | +Image Recognition | +Post-Training Static Quantization | +fx | +
| Se_ResNeXt50_32x4d | +Image Recognition | +Post-Training Static Quantization | +fx | +
| Inception V3 | +Image Recognition | +Post-Training Static Quantization | +fx | +
| MobileNet V2 | +Image Recognition | +Post-Training Static Quantization | +fx | +
| PeleeNet | +Image Recognition | +Post-Training Static Quantization | +fx | +
| ResNeSt50 | +Image Recognition | +Post-Training Static Quantization | +fx | +
| 3D-UNet | +Image Recognition | +Post-Training Static Quantization | +fx | +
| SSD ResNet34 | +Object Detection | +Post-Training Static Quantization | +fx / ipex | +
| YOLOv3 | +Object Detection | +Post-Training Static Quantization | +fx | +
| Mask R-CNN | +Object Detection | +Post-Training Static Quantization | +fx | +
| DLRM | +Recommendation | +Post-Training Static Quantization | +ipex / fx | +
| HuBERT | +Speech Recognition | +Post-Training Static Quantization | +fx | +
| HuBERT | +Speech Recognition | +Post-Training Dynamic Quantization | +fx | +
| BlendCNN | +Natural Language Processing | +Post-Training Static Quantization | +ipex | +
| bert-large-uncased-whole-word-masking-finetuned-squad | +Natural Language Processing | +Post-Training Static Quantization | +fx / ipex(Intel GPU) | +
| distilbert-base-uncased-distilled-squad | +Natural Language Processing | +Post-Training Static Quantization | +ipex | +
| yoshitomo-matsubara/bert-large-uncased-rte | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| Intel/xlm-roberta-base-mrpc | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| textattack/distilbert-base-uncased-MRPC | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| textattack/albert-base-v2-MRPC | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| Intel/xlm-roberta-base-mrpc | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| yoshitomo-matsubara/bert-large-uncased-rte | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| Intel/bert-base-uncased-mrpc | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| textattack/bert-base-uncased-CoLA | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| textattack/bert-base-uncased-STS-B | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| gchhablani/bert-base-cased-finetuned-sst2 | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| ModelTC/bert-base-uncased-rte | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| textattack/bert-base-uncased-QNLI | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| yoshitomo-matsubara/bert-large-uncased-cola | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| textattack/distilbert-base-uncased-MRPC | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| Intel/xlnet-base-cased-mrpc | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| textattack/roberta-base-MRPC | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| Intel/camembert-base-mrpc | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| t5-small | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| Helsinki-NLP/opus-mt-en-ro | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| lvwerra/pegasus-samsum | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| google/reformer-crime-and-punishment | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| EleutherAI/gpt-j-6B | +Natural Language Processing | +Post-Training Static Quantization | +fx / smooth quant | +
| EleutherAI/gpt-j-6B | +Natural Language Processing | +Post-Training Weight Only Quantization | +weight_only | +
| abeja/gpt-neox-japanese-2.7b | +Natural Language Processing | +Post-Training Static Quantization | +fx | +
| bigscience/bloom | +Natural Language Processing | +Post-Training Static Quantization | +smooth quant | +
| facebook/opt | +Natural Language Processing | +Post-Training Static Quantization | +smooth quant | +
| SD Diffusion | +Text to Image | +Post-Training Static Quantization | +fx | +
| openai/whisper-large | +Speech Recognition | +Post-Training Dynamic Quantization | +fx | +
| torchaudio/wav2vec2 | +Speech Recognition | +Post-Training Dynamic Quantization | +fx | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| T5 Large | +Natural Language Processing | +Post-Training Dynamic Quantization | +fx | +
| Flan T5 Large | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | +fx | +
| Model | +Domain | +Pruning Type | +Approach | +Examples | +
|---|---|---|---|---|
| Distilbert-base-uncased | +Natural Language Processing (text classification) | +Structured (4x1, 2in4), Unstructured | +Snip-momentum | +eager | +
| Bert-mini | +Natural Language Processing (text classification) | +Structured (4x1, 2in4, per channel), Unstructured | +Snip-momentum | +eager | +
| Distilbert-base-uncased | +Natural Language Processing (question answering) | +Structured (4x1, 2in4), Unstructured | +Snip-momentum | +eager | +
| Bert-mini | +Natural Language Processing (question answering) | +Structured (4x1, 2in4), Unstructured | +Snip-momentum | +eager | +
| Bert-base-uncased | +Natural Language Processing (question answering) | +Structured (4x1, 2in4), Unstructured | +Snip-momentum | +eager | +
| Bert-large | +Natural Language Processing (question answering) | +Structured (4x1, 2in4), Unstructured | +Snip-momentum | +eager | +
| Flan-T5-small | +Natural Language Processing (translation) | +Structured (4x1) | +Snip-momentum | +eager | +
| YOLOv5s6 | +Object Detection | +Structured (4x1, 2in4), Unstructured | +Snip-momentum | +eager | +
| ResNet50 | +Image Recognition | +Structured (2x1) | +Snip-momentum | +eager | +
| Bert-base | +Question Answering | +Structured (channel, multi-head attention) | +Snip-momentum | +eager | +
| Bert-large | +Question Answering | +Structured (channel, multi-head attention) | +Snip-momentum | +eager | +
| Student Model | +Teacher Model | +Domain | +Approach | +Examples | +
|---|---|---|---|---|
| CNN-2 | +CNN-10 | +Image Recognition | +Knowledge Distillation | +eager | +
| MobileNet V2-0.35 | +WideResNet40-2 | +Image Recognition | +Knowledge Distillation | +eager | +
| ResNet18|ResNet34|ResNet50|ResNet101 | +ResNet18|ResNet34|ResNet50|ResNet101 | +Image Recognition | +Knowledge Distillation | +eager | +
| ResNet18|ResNet34|ResNet50|ResNet101 | +ResNet18|ResNet34|ResNet50|ResNet101 | +Image Recognition | +Self Distillation | +eager | +
| VGG-8 | +VGG-13 | +Image Recognition | +Knowledge Distillation | +eager | +
| BlendCNN | +BERT-Base | +Natural Language Processing | +Knowledge Distillation | +eager | +
| DistilBERT | +BERT-Base | +Natural Language Processing | +Knowledge Distillation | +eager | +
| BiLSTM | +RoBERTa-Base | +Natural Language Processing | +Knowledge Distillation | +eager | +
| TinyBERT | +BERT-Base | +Natural Language Processing | +Knowledge Distillation | +eager | +
| BERT-3 | +BERT-Base | +Natural Language Processing | +Knowledge Distillation | +eager | +
| DistilRoBERTa | +RoBERTa-Large | +Natural Language Processing | +Knowledge Distillation | +eager | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet50 | +Image Recognition | +Multi-shot: Pruning and PTQ |
+ link | +
| ResNet50 | +Image Recognition | +One-shot: QAT during Pruning |
+ link | +
| Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | +Natural Language Processing (question-answering) | +One-shot: Pruning, Distillation and QAT |
+ link | +
| Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | +Natural Language Processing (text-classification) | +One-shot: Pruning, Distillation and QAT |
+ link | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet18 | +Image Recognition | +PT2ONNX | +int8 fp32 | +
| ResNet50 | +Image Recognition | +PT2ONNX | +int8 fp32 | +
| bert base MRPC | +Natural Language Processing | +PT2ONNX | +int8 fp32 | +
| bert large MRPC | +Natural Language Processing | +PT2ONNX | +int8 fp32 | +
| Model | +Domain | +Approach | +Examples | +
|---|---|---|---|
| ResNet50 V1.5 | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| ResNet50 V1.5 MLPerf | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| VGG16 | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| MobileNet V2 | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| MobileNet V3 MLPerf | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| AlexNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| CaffeNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| DenseNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops | +
| EfficientNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| FCN (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| GoogleNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| Inception V1 (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| MNIST (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops | +
| MobileNet V2 (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| ResNet50 V1.5 (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| ShuffleNet V2 (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| SqueezeNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| VGG16 (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| ZFNet (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops / qdq | +
| ArcFace (ONNX Model Zoo) | +Image Recognition | +Post-Training Static Quantization | +qlinearops | +
| BEiT | +Image Recognition | +Post-Training Static Quantization | +qlinearops | +
| BERT base MRPC | +Natural Language Processing | +Post-Training Static Quantization | +integerops / qdq | +
| BERT base MRPC | +Natural Language Processing | +Post-Training Dynamic Quantization | +integerops | +
| DistilBERT base MRPC | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | +integerops / qdq | +
| Mobile bert MRPC | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | +integerops / qdq | +
| Roberta base MRPC | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | +integerops / qdq | +
| BERT SQuAD (ONNX Model Zoo) | +Natural Language Processing | +Post-Training Dynamic Quantization | +integerops | +
| GPT2 lm head WikiText (ONNX Model Zoo) | +Natural Language Processing | +Post-Training Dynamic Quantization | +integerops | +
| MobileBERT SQuAD MLPerf (ONNX Model Zoo) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | +integerops / qdq | +
| BiDAF (ONNX Model Zoo) | +Natural Language Processing | +Post-Training Dynamic Quantization | +integerops | +
| Spanbert SQuAD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| Bert base multilingual cased SQuAD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| DistilBert base uncased SQuAD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| BERT large uncased whole word masking SQuAD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| Roberta large SQuAD v2 (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| GPT2 WikiText (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| DistilGPT2 WikiText (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| LayoutLMv3 FUNSD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| LayoutLMv2 FUNSD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| LayoutLM FUNSD (HuggingFace) | +Natural Language Processing | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| SSD MobileNet V1 | +Object Detection | +Post-Training Static Quantization | +qlinearops / qdq | +
| SSD MobileNet V2 | +Object Detection | +Post-Training Static Quantization | +qlinearops / qdq | +
| Table Transformer Structure Recognition | +Object Detection | +Post-Training Static Quantization | +qlinearops | +
| Table Transformer Detection | +Object Detection | +Post-Training Static Quantization | +qlinearops | +
| SSD MobileNet V1 (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops / qdq | +
| DUC (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops | +
| Faster R-CNN (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops / qdq | +
| Mask R-CNN (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops / qdq | +
| SSD (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops / qdq | +
| Tiny YOLOv3 (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops | +
| YOLOv3 (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops | +
| YOLOv4 (ONNX Model Zoo) | +Object Detection | +Post-Training Static Quantization | +qlinearops | +
| Emotion FERPlus (ONNX Model Zoo) | +Body Analysis | +Post-Training Static Quantization | +qlinearops | +
| Ultra Face (ONNX Model Zoo) | +Body Analysis | +Post-Training Static Quantization | +qlinearops | +
| GPT-J-6B (HuggingFace) | +Text Generation | +Post-Training Dynamic / Static Quantization | ++ integerops / qlinearops + | +
| Llama-7B (HuggingFace) | +Text Generation | +Static / Weight Only Quantization | ++ qlinearops / weight_only + | +
-For example, bash command will look like the following, where *`
-Also please note that to use CPU for training in each node with multi nodes settings, argument `--no_cuda` is mandatory. In multi nodes setting, following command needs to be launched in each node, and all the commands should be the same except for *`
+For example, bash command will look like the following, where *`
+Also please note that to use CPU for training in each node with multi nodes settings, argument `--no_cuda` is mandatory. In multi nodes setting, following command needs to be launched in each node, and all the commands should be the same except for *`
-
-
-
-The image generated by original model(FID with ground truth: 333):
-
-
-
-
-
-
-The image generated by quantized UNet(FID with ground truth: 246):
-
-
-
-
-
-
-
-# Performance
-Original model
-```bash
-python run_diffusion.py \
- --model_name_or_path lambdalabs/sd-pokemon-diffusers \
- --output_dir /tmp/diffusion_output \
- --base_images base_images \
- --benchmark
-```
-The model of quantized UNet
-```bash
-python run_diffusion.py \
- --model_name_or_path lambdalabs/sd-pokemon-diffusers \
- --output_dir /tmp/diffusion_output \
- --base_images base_images \
- --benchmark \
- --int8
-```
-
->**Note:** Inference performance speedup with Intel DL Boost (VNNI) on Intel(R) Xeon(R) hardware, Please refer to [Performance Tuning Guide](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html) for more optimizations.
+Step-by-Step
+============
+This document describes the step-by-step instructions for reproducing stable diffusion tuning results with Intel® Neural Compressor.
+
+The script ```run_diffusion.py``` is based on [huggingface/diffusers](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) and provides post-training static quantization approach based on Intel® Neural Compressor.
+
+# Prerequisite
+
+## 1. Environment
+```
+pip install -r requirements.txt
+```
+> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+## 2. Prepare Datasets
+### Metric and Ground Truth Image
+FID metric is used to evaluate the model in this case, so we should download [training datasets](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) and choose one image to a directory(like "base_images").
+>**Note:** In this case we used picture: [Ground_Truth_Image](https://datasets-server.huggingface.co/assets/lambdalabs/pokemon-blip-captions/--/lambdalabs--pokemon-blip-captions/train/14/image/image.jpg).
+
+# Quantization
+
+```bash
+python run_diffusion.py \
+ --model_name_or_path lambdalabs/sd-pokemon-diffusers \
+ --tune \
+ --quantization_approach static \
+ --perf_tol 0.02 \
+ --output_dir /tmp/diffusion_output \
+ --base_images base_images \
+ --input_text "a drawing of a gray and black dragon" \
+ --calib_text "a drawing of a green pokemon with red eyes"
+```
+
+
+## The ground truth images and before and after quantization images show below:
+
+The ground truth image:
+
+
-
+
+
+
+The image generated by original model(FID with ground truth: 333):
+
+
+
+
+
+
+The image generated by quantized UNet(FID with ground truth: 246):
+
+
+
+
+
+
+
+# Performance
+Original model
+```bash
+python run_diffusion.py \
+ --model_name_or_path lambdalabs/sd-pokemon-diffusers \
+ --output_dir /tmp/diffusion_output \
+ --base_images base_images \
+ --benchmark
+```
+The model of quantized UNet
+```bash
+python run_diffusion.py \
+ --model_name_or_path lambdalabs/sd-pokemon-diffusers \
+ --output_dir /tmp/diffusion_output \
+ --base_images base_images \
+ --benchmark \
+ --int8
+```
+
+>**Note:** Inference performance speedup with Intel DL Boost (VNNI) on Intel(R) Xeon(R) hardware, Please refer to [Performance Tuning Guide](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html) for more optimizations.
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/base_images/image.jpg b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/base_images/image.jpg
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/base_images/image.jpg
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/base_images/image.jpg
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/images/fp32.png b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/images/fp32.png
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/images/fp32.png
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/images/fp32.png
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/images/ground_truth.jpg b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/images/ground_truth.jpg
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/images/ground_truth.jpg
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/images/ground_truth.jpg
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/images/int8.png b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/images/int8.png
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/images/int8.png
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/images/int8.png
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/requirements.txt b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/requirements.txt
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/requirements.txt
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/requirements.txt
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_benchmark.sh b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_benchmark.sh
old mode 100755
new mode 100644
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_benchmark.sh
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_benchmark.sh
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_quant.sh b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_quant.sh
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_quant.sh
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/ptq_static/fx/run_quant.sh
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/requirements.txt b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/requirements.txt
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/requirements.txt
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/requirements.txt
diff --git a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/run_diffusion.py b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/run_diffusion.py
similarity index 97%
rename from examples/pytorch/nlp/huggingface_models/text-to-image/quantization/run_diffusion.py
rename to examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/run_diffusion.py
index 1dd38d7a115..b49f8a98546 100644
--- a/examples/pytorch/nlp/huggingface_models/text-to-image/quantization/run_diffusion.py
+++ b/examples/deprecated/pytorch/nlp/huggingface_models/text-to-image/quantization/run_diffusion.py
@@ -1,337 +1,337 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""" Example for stable-diffusion to generate a picture from a text ."""
-# You can also adapt this script on your own text to image task. Pointers for this are left as comments.
-
-import argparse
-import logging
-import math
-import os
-import sys
-import time
-
-import torch
-from PIL import Image
-from torch.utils.data import DataLoader, Dataset
-
-from accelerate.utils import set_seed
-from diffusers import StableDiffusionPipeline
-from pytorch_fid import fid_score
-
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-logger = logging.getLogger(__name__)
-
-
-def parse_args():
- """Parse arguments"""
- parser = argparse.ArgumentParser(description="Example of a post-training quantization script.")
- parser.add_argument(
- "--model_name_or_path",
- type=str,
- required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--input_text",
- type=str,
- default="a drawing of a gray and black dragon",
- help="The input of the model, like: 'a photo of an astronaut riding a horse on mars'.",
- )
- parser.add_argument(
- "--calib_text",
- type=str,
- default="Womens Princess Little Deer Native American Costume",
- help="The calibration data of the model, like: 'Womens Princess Little Deer Native American Costume'.",
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default="saved_results",
- help="The path to save model and quantization configures.",
- )
- parser.add_argument(
- "--num_images_per_prompt",
- type=int,
- default=1,
- help="The number of images to generate per prompt, defaults to 1",
- )
- parser.add_argument(
- "--seed",
- type=int,
- default=666,
- help="random seed",
- )
- parser.add_argument(
- "--local_rank",
- type=int,
- default=-1,
- help="For distributed: local_rank",
- )
- parser.add_argument(
- "--base_images",
- type=str,
- default="base_images",
- help="Path to training images for FID input.",
- )
- parser.add_argument(
- "--tune",
- action="store_true",
- help="Whether or not to apply quantization.",
- )
- parser.add_argument(
- "--quantization_approach",
- type=str,
- default="static",
- help="Quantization approach. Supported approach are static, "
- "dynamic and auto.",
- )
- parser.add_argument(
- "--framework",
- type=str,
- default="pytorch",
- help="Deep learning framework. Supported framework are pytorch, ipex",
- )
- parser.add_argument(
- "--metric_name",
- type=str,
- default="eval_f1",
- help="Metric used for the tuning strategy.",
- )
- parser.add_argument(
- "--is_relative",
- type=bool,
- default="True",
- help="Metric tolerance mode, True for relative, otherwise for absolute.",
- )
- parser.add_argument(
- "--perf_tol",
- type=float,
- default=0.01,
- help="Performance tolerance when optimizing the model.",
- )
- parser.add_argument(
- "--benchmark",
- action="store_true",
- help="Only test performance for model.",
- )
- parser.add_argument(
- "--accuracy_only",
- action="store_true",
- help="Only test accuracy for model.",
- )
- parser.add_argument(
- "--int8",
- action="store_true",
- help="benchmark for int8 model",
- )
-
- args = parser.parse_args()
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- return args
-
-def image_grid(imgs, rows, cols):
- if not len(imgs) == rows * cols:
- raise ValueError("The specified number of rows and columns are not correct.")
-
- w, h = imgs[0].size
- grid = Image.new("RGB", size=(cols * w, rows * h))
- grid_w, grid_h = grid.size
-
- for i, img in enumerate(imgs):
- grid.paste(img, box=(i % cols * w, i // cols * h))
- return grid
-
-
-def benchmark(pipe, generator):
- warmup = 2
- total = 5
- total_time = 0
- with torch.no_grad():
- for i in range(total):
- prompt = "a photo of an astronaut riding a horse on mars"
- start2 = time.time()
- images = pipe(prompt, guidance_scale=7.5, num_inference_steps=50, generator=generator).images
- end2 = time.time()
- if i >= warmup:
- total_time += end2 - start2
- print("Average Latency: ", (total_time) / (total - warmup), "s")
- print("Average Throughput: {:.5f} samples/sec".format((total - warmup) / (total_time)))
-
-
-def accuracy(pipe, generator, rows, args):
- with torch.no_grad():
- new_images = pipe(
- args.input_text,
- guidance_scale=7.5,
- num_inference_steps=50,
- generator=generator,
- num_images_per_prompt=args.num_images_per_prompt,
- ).images
- tmp_save_images = "tmp_save_images"
- os.makedirs(tmp_save_images, exist_ok=True)
- if os.path.isfile(os.path.join(tmp_save_images, "image.png")):
- os.remove(os.path.join(tmp_save_images, "image.png"))
- grid = image_grid(new_images, rows=rows, cols=args.num_images_per_prompt // rows)
- grid.save(os.path.join(tmp_save_images, "image.png"))
- fid = fid_score.calculate_fid_given_paths((args.base_images, tmp_save_images), 1, "cpu", 2048, 8)
- print("Finally FID score Accuracy: {}".format(fid))
- return fid
-
-class CalibDataset(Dataset):
- def __len__(self):
- return 1
-
- def __getitem__(self, idx):
- data = "a photo of an astronaut riding a horse on mars"
- return data
-
-
-def main():
- # Passing the --help flag to this script.
-
- args = parse_args()
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- handlers=[logging.StreamHandler(sys.stdout)],
- )
-
- logger.info(f"Parameters {args}")
-
- # Set seed before initializing model.
- set_seed(args.seed)
-
- # Load pretrained model and generate a pipeline
- #
- # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- pipe = StableDiffusionPipeline.from_pretrained(args.model_name_or_path)
- _rows = int(math.sqrt(args.num_images_per_prompt))
-
- if args.tune:
- tmp_fp32_images = "tmp_fp32_images"
- tmp_int8_images = "tmp_int8_images"
- os.makedirs(tmp_fp32_images, exist_ok=True)
- os.makedirs(tmp_int8_images, exist_ok=True)
- generator = torch.Generator("cpu").manual_seed(args.seed)
- fp32_images = pipe(
- args.input_text,
- guidance_scale=7.5,
- num_inference_steps=50,
- generator=generator,
- num_images_per_prompt=args.num_images_per_prompt,
- ).images
- grid = image_grid(fp32_images, rows=_rows, cols=args.num_images_per_prompt // _rows)
- grid.save(os.path.join(tmp_fp32_images, "fp32.png"))
-
- attr_list = ["unet"]
- for name in attr_list:
- model = getattr(pipe, name)
-
- def calibration_func(model):
- calib_num = 5
- setattr(pipe, name, model)
- with torch.no_grad():
- for i in range(calib_num):
- pipe(
- args.calib_text,
- guidance_scale=7.5,
- num_inference_steps=50,
- generator=generator,
- num_images_per_prompt=args.num_images_per_prompt,
- )
-
- def eval_func(model):
- setattr(pipe, name, model)
- generator = torch.Generator("cpu").manual_seed(args.seed)
- with torch.no_grad():
- new_images = pipe(
- args.input_text,
- guidance_scale=7.5,
- num_inference_steps=50,
- generator=generator,
- num_images_per_prompt=args.num_images_per_prompt,
- ).images
- if os.path.isfile(os.path.join(tmp_int8_images, "int8.png")):
- os.remove(os.path.join(tmp_int8_images, "int8.png"))
- grid = image_grid(new_images, rows=_rows, cols=args.num_images_per_prompt // _rows)
- grid.save(os.path.join(tmp_int8_images, "int8.png"))
- fid = fid_score.calculate_fid_given_paths((args.base_images, tmp_int8_images), 1, "cpu", 2048, 8)
- return fid
-
- from neural_compressor.config import PostTrainingQuantConfig, AccuracyCriterion
- from neural_compressor.quantization import fit
- accuracy_criterion = AccuracyCriterion(
- higher_is_better=False,
- criterion="relative" if args.is_relative else "absolute",
- tolerable_loss=args.perf_tol)
- quantization_config = PostTrainingQuantConfig(
- approach=args.quantization_approach,
- accuracy_criterion=accuracy_criterion
- )
- model = fit(
- model=pipe.unet,
- conf=quantization_config,
- eval_func=eval_func,
- calib_func=calibration_func,
- )
- setattr(pipe, name, model)
- model.save(args.output_dir)
- logger.info(f"Optimized model {name} saved to: {args.output_dir}.")
-
- if args.benchmark or args.accuracy_only:
-
- def b_func(model):
- setattr(pipe, "unet", model)
- benchmark(pipe, generator)
-
- if args.int8:
- print("====int8 inference====")
- from neural_compressor.utils.pytorch import load
- checkpoint = os.path.join(args.output_dir)
- model = load(checkpoint, model=getattr(pipe, "unet"))
- model.eval()
- else:
- print("====fp32 inference====")
- model = getattr(pipe, "unet")
- generator = torch.Generator("cpu").manual_seed(args.seed)
- if args.benchmark:
- from neural_compressor.benchmark import fit
- from neural_compressor.config import BenchmarkConfig
-
- b_conf = BenchmarkConfig(cores_per_instance=4, num_of_instance=1)
- fit(model, conf=b_conf, b_func=b_func)
- if args.accuracy_only:
- setattr(pipe, "unet", model)
- accuracy(pipe, generator, _rows, args)
-
-
-def _mp_fn(index):
- # For xla_spawn (TPUs)
- main()
-
-
-if __name__ == "__main__":
- main()
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Example for stable-diffusion to generate a picture from a text ."""
+# You can also adapt this script on your own text to image task. Pointers for this are left as comments.
+
+import argparse
+import logging
+import math
+import os
+import sys
+import time
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+
+from accelerate.utils import set_seed
+from diffusers import StableDiffusionPipeline
+from pytorch_fid import fid_score
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+ """Parse arguments"""
+ parser = argparse.ArgumentParser(description="Example of a post-training quantization script.")
+ parser.add_argument(
+ "--model_name_or_path",
+ type=str,
+ required=True,
+ help="Path to pretrained model or model identifier from huggingface.co/models.",
+ )
+ parser.add_argument(
+ "--input_text",
+ type=str,
+ default="a drawing of a gray and black dragon",
+ help="The input of the model, like: 'a photo of an astronaut riding a horse on mars'.",
+ )
+ parser.add_argument(
+ "--calib_text",
+ type=str,
+ default="Womens Princess Little Deer Native American Costume",
+ help="The calibration data of the model, like: 'Womens Princess Little Deer Native American Costume'.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default="saved_results",
+ help="The path to save model and quantization configures.",
+ )
+ parser.add_argument(
+ "--num_images_per_prompt",
+ type=int,
+ default=1,
+ help="The number of images to generate per prompt, defaults to 1",
+ )
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=666,
+ help="random seed",
+ )
+ parser.add_argument(
+ "--local_rank",
+ type=int,
+ default=-1,
+ help="For distributed: local_rank",
+ )
+ parser.add_argument(
+ "--base_images",
+ type=str,
+ default="base_images",
+ help="Path to training images for FID input.",
+ )
+ parser.add_argument(
+ "--tune",
+ action="store_true",
+ help="Whether or not to apply quantization.",
+ )
+ parser.add_argument(
+ "--quantization_approach",
+ type=str,
+ default="static",
+ help="Quantization approach. Supported approach are static, "
+ "dynamic and auto.",
+ )
+ parser.add_argument(
+ "--framework",
+ type=str,
+ default="pytorch",
+ help="Deep learning framework. Supported framework are pytorch, ipex",
+ )
+ parser.add_argument(
+ "--metric_name",
+ type=str,
+ default="eval_f1",
+ help="Metric used for the tuning strategy.",
+ )
+ parser.add_argument(
+ "--is_relative",
+ type=bool,
+ default="True",
+ help="Metric tolerance mode, True for relative, otherwise for absolute.",
+ )
+ parser.add_argument(
+ "--perf_tol",
+ type=float,
+ default=0.01,
+ help="Performance tolerance when optimizing the model.",
+ )
+ parser.add_argument(
+ "--benchmark",
+ action="store_true",
+ help="Only test performance for model.",
+ )
+ parser.add_argument(
+ "--accuracy_only",
+ action="store_true",
+ help="Only test accuracy for model.",
+ )
+ parser.add_argument(
+ "--int8",
+ action="store_true",
+ help="benchmark for int8 model",
+ )
+
+ args = parser.parse_args()
+ env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+ if env_local_rank != -1 and env_local_rank != args.local_rank:
+ args.local_rank = env_local_rank
+
+ return args
+
+def image_grid(imgs, rows, cols):
+ if not len(imgs) == rows * cols:
+ raise ValueError("The specified number of rows and columns are not correct.")
+
+ w, h = imgs[0].size
+ grid = Image.new("RGB", size=(cols * w, rows * h))
+ grid_w, grid_h = grid.size
+
+ for i, img in enumerate(imgs):
+ grid.paste(img, box=(i % cols * w, i // cols * h))
+ return grid
+
+
+def benchmark(pipe, generator):
+ warmup = 2
+ total = 5
+ total_time = 0
+ with torch.no_grad():
+ for i in range(total):
+ prompt = "a photo of an astronaut riding a horse on mars"
+ start2 = time.time()
+ images = pipe(prompt, guidance_scale=7.5, num_inference_steps=50, generator=generator).images
+ end2 = time.time()
+ if i >= warmup:
+ total_time += end2 - start2
+ print("Average Latency: ", (total_time) / (total - warmup), "s")
+ print("Average Throughput: {:.5f} samples/sec".format((total - warmup) / (total_time)))
+
+
+def accuracy(pipe, generator, rows, args):
+ with torch.no_grad():
+ new_images = pipe(
+ args.input_text,
+ guidance_scale=7.5,
+ num_inference_steps=50,
+ generator=generator,
+ num_images_per_prompt=args.num_images_per_prompt,
+ ).images
+ tmp_save_images = "tmp_save_images"
+ os.makedirs(tmp_save_images, exist_ok=True)
+ if os.path.isfile(os.path.join(tmp_save_images, "image.png")):
+ os.remove(os.path.join(tmp_save_images, "image.png"))
+ grid = image_grid(new_images, rows=rows, cols=args.num_images_per_prompt // rows)
+ grid.save(os.path.join(tmp_save_images, "image.png"))
+ fid = fid_score.calculate_fid_given_paths((args.base_images, tmp_save_images), 1, "cpu", 2048, 8)
+ print("Finally FID score Accuracy: {}".format(fid))
+ return fid
+
+class CalibDataset(Dataset):
+ def __len__(self):
+ return 1
+
+ def __getitem__(self, idx):
+ data = "a photo of an astronaut riding a horse on mars"
+ return data
+
+
+def main():
+ # Passing the --help flag to this script.
+
+ args = parse_args()
+
+ # Setup logging
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ handlers=[logging.StreamHandler(sys.stdout)],
+ )
+
+ logger.info(f"Parameters {args}")
+
+ # Set seed before initializing model.
+ set_seed(args.seed)
+
+ # Load pretrained model and generate a pipeline
+ #
+ # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+ # download model & vocab.
+ pipe = StableDiffusionPipeline.from_pretrained(args.model_name_or_path)
+ _rows = int(math.sqrt(args.num_images_per_prompt))
+
+ if args.tune:
+ tmp_fp32_images = "tmp_fp32_images"
+ tmp_int8_images = "tmp_int8_images"
+ os.makedirs(tmp_fp32_images, exist_ok=True)
+ os.makedirs(tmp_int8_images, exist_ok=True)
+ generator = torch.Generator("cpu").manual_seed(args.seed)
+ fp32_images = pipe(
+ args.input_text,
+ guidance_scale=7.5,
+ num_inference_steps=50,
+ generator=generator,
+ num_images_per_prompt=args.num_images_per_prompt,
+ ).images
+ grid = image_grid(fp32_images, rows=_rows, cols=args.num_images_per_prompt // _rows)
+ grid.save(os.path.join(tmp_fp32_images, "fp32.png"))
+
+ attr_list = ["unet"]
+ for name in attr_list:
+ model = getattr(pipe, name)
+
+ def calibration_func(model):
+ calib_num = 5
+ setattr(pipe, name, model)
+ with torch.no_grad():
+ for i in range(calib_num):
+ pipe(
+ args.calib_text,
+ guidance_scale=7.5,
+ num_inference_steps=50,
+ generator=generator,
+ num_images_per_prompt=args.num_images_per_prompt,
+ )
+
+ def eval_func(model):
+ setattr(pipe, name, model)
+ generator = torch.Generator("cpu").manual_seed(args.seed)
+ with torch.no_grad():
+ new_images = pipe(
+ args.input_text,
+ guidance_scale=7.5,
+ num_inference_steps=50,
+ generator=generator,
+ num_images_per_prompt=args.num_images_per_prompt,
+ ).images
+ if os.path.isfile(os.path.join(tmp_int8_images, "int8.png")):
+ os.remove(os.path.join(tmp_int8_images, "int8.png"))
+ grid = image_grid(new_images, rows=_rows, cols=args.num_images_per_prompt // _rows)
+ grid.save(os.path.join(tmp_int8_images, "int8.png"))
+ fid = fid_score.calculate_fid_given_paths((args.base_images, tmp_int8_images), 1, "cpu", 2048, 8)
+ return fid
+
+ from neural_compressor.config import PostTrainingQuantConfig, AccuracyCriterion
+ from neural_compressor.quantization import fit
+ accuracy_criterion = AccuracyCriterion(
+ higher_is_better=False,
+ criterion="relative" if args.is_relative else "absolute",
+ tolerable_loss=args.perf_tol)
+ quantization_config = PostTrainingQuantConfig(
+ approach=args.quantization_approach,
+ accuracy_criterion=accuracy_criterion
+ )
+ model = fit(
+ model=pipe.unet,
+ conf=quantization_config,
+ eval_func=eval_func,
+ calib_func=calibration_func,
+ )
+ setattr(pipe, name, model)
+ model.save(args.output_dir)
+ logger.info(f"Optimized model {name} saved to: {args.output_dir}.")
+
+ if args.benchmark or args.accuracy_only:
+
+ def b_func(model):
+ setattr(pipe, "unet", model)
+ benchmark(pipe, generator)
+
+ if args.int8:
+ print("====int8 inference====")
+ from neural_compressor.utils.pytorch import load
+ checkpoint = os.path.join(args.output_dir)
+ model = load(checkpoint, model=getattr(pipe, "unet"))
+ model.eval()
+ else:
+ print("====fp32 inference====")
+ model = getattr(pipe, "unet")
+ generator = torch.Generator("cpu").manual_seed(args.seed)
+ if args.benchmark:
+ from neural_compressor.benchmark import fit
+ from neural_compressor.config import BenchmarkConfig
+
+ b_conf = BenchmarkConfig(cores_per_instance=4, num_of_instance=1)
+ fit(model, conf=b_conf, b_func=b_func)
+ if args.accuracy_only:
+ setattr(pipe, "unet", model)
+ accuracy(pipe, generator, _rows, args)
+
+
+def _mp_fn(index):
+ # For xla_spawn (TPUs)
+ main()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/pytorch/nlp/huggingface_models/translation/pruning/eager/README.md b/examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/README.md
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/pruning/eager/README.md
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/README.md
diff --git a/examples/pytorch/nlp/huggingface_models/translation/pruning/eager/requirements.txt b/examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/requirements.txt
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/pruning/eager/requirements.txt
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/requirements.txt
diff --git a/examples/pytorch/nlp/huggingface_models/translation/pruning/eager/run_translation_no_trainer.py b/examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/run_translation_no_trainer.py
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/pruning/eager/run_translation_no_trainer.py
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/run_translation_no_trainer.py
diff --git a/examples/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_finetune.sh b/examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_finetune.sh
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_finetune.sh
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_finetune.sh
diff --git a/examples/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_prune.sh b/examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_prune.sh
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_prune.sh
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/pruning/eager/scripts/run_translation_prune.sh
diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/README.md b/examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/README.md
similarity index 96%
rename from examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/README.md
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/README.md
index 9bc50694ad7..2199c63afe3 100644
--- a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/README.md
+++ b/examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/README.md
@@ -72,4 +72,4 @@ quantized_model = load(tuned_checkpoint,
model)
```
--------
-For more details, please refer to the [sample code](./run_translation.py).
\ No newline at end of file
+For more details, please refer to the [sample code](run_translation.py).
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/requirements.txt b/examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/requirements.txt
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/requirements.txt
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/requirements.txt
diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_benchmark.sh b/examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_benchmark.sh
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_benchmark.sh
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_benchmark.sh
diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_quant.sh b/examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_quant.sh
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_quant.sh
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_quant.sh
diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_translation.py b/examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_translation.py
similarity index 100%
rename from examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_translation.py
rename to examples/deprecated/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/fx/run_translation.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/README.md b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/README.md
similarity index 98%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/README.md
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/README.md
index fdfe1282b45..52e4e0935fb 100644
--- a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/README.md
+++ b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/README.md
@@ -122,4 +122,4 @@ q_model = load(os.path.abspath(os.path.expanduser(args.tuned_checkpoint)),
fp32_model,
dataloader=your_dataloader)
```
-Please refer to [Sample code](./pytorch/tools/test_net.py)
+Please refer to [Sample code](pytorch/tools/test_net.py)
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_dataset.sh b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_dataset.sh
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_dataset.sh
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_dataset.sh
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_weights.sh b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_weights.sh
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_weights.sh
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/download_weights.sh
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/LICENSE b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/LICENSE
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/LICENSE
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/LICENSE
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/TROUBLESHOOTING.md b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/TROUBLESHOOTING.md
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/TROUBLESHOOTING.md
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/TROUBLESHOOTING.md
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/maskrnn.patch b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/maskrnn.patch
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/maskrnn.patch
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/maskrnn.patch
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/setup.py b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/setup.py
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/setup.py
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/setup.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/instances2dict_with_polygons.py b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/instances2dict_with_polygons.py
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/instances2dict_with_polygons.py
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/cityscapes/instances2dict_with_polygons.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/test_net.py b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/test_net.py
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/test_net.py
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/test_net.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_mlperf.py b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_mlperf.py
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_mlperf.py
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_mlperf.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_net.py b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_net.py
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_net.py
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch/tools/train_net.py
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/requirements.txt b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/requirements.txt
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/requirements.txt
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/requirements.txt
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_and_time.sh b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_and_time.sh
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_and_time.sh
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_and_time.sh
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_benchmark.sh b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_benchmark.sh
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_benchmark.sh
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_benchmark.sh
diff --git a/examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_quant.sh b/examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_quant.sh
similarity index 100%
rename from examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_quant.sh
rename to examples/deprecated/pytorch/object_detection/maskrcnn/quantization/ptq/fx/run_quant.sh
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/README.md b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/README.md
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/README.md
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/README.md
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/mlperf.conf b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/mlperf.conf
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/mlperf.conf
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/mlperf.conf
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_dataset.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_dataset.sh
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_dataset.sh
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_dataset.sh
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_loadgen.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_loadgen.sh
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_loadgen.sh
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/prepare_loadgen.sh
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_null.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_null.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_null.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_null.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_onnxruntime.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_onnxruntime.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_onnxruntime.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_onnxruntime.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch_native.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch_native.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch_native.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_pytorch_native.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tf.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tf.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tf.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tf.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tflite.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tflite.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tflite.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/backend_tflite.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/coco.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/coco.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/coco.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/coco.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/dataset.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/dataset.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/dataset.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/dataset.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/imagenet.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/imagenet.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/imagenet.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/imagenet.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/main.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/main.py
similarity index 99%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/main.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/main.py
index 67159f25a40..cc829f8640b 100644
--- a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/main.py
+++ b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/main.py
@@ -50,7 +50,7 @@
(coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
{"image_size": [300, 300, 3]}),
"coco-300-pt":
- (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False,0.3),
+ (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False, 0.3),
{"image_size": [300, 300, 3]}),
"coco-1200":
(coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
@@ -59,7 +59,7 @@
(coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
{"image_size": [1200, 1200, 3]}),
"coco-1200-pt":
- (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True,0.05),
+ (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True, 0.05),
{"image_size": [1200, 1200, 3],"use_label_map": True}),
"coco-1200-tf":
(coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoTf(),
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/anchor_generator.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/anchor_generator.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/anchor_generator.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/anchor_generator.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/base_model_r34.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/base_model_r34.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/base_model_r34.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/base_model_r34.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/convert_tf_weights.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/convert_tf_weights.py
similarity index 100%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/convert_tf_weights.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/convert_tf_weights.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/tools/make_fake_imagenet.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/tools/make_fake_imagenet.sh
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/tools/ssd-nhwc.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/tools/ssd-nhwc.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/upscale_coco/README.md b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/upscale_coco/README.md
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/upscale_coco/coco.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/upscale_coco/coco.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/upscale_coco/upscale_coco.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/upscale_coco/upscale_coco.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/user.conf b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/user.conf
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/README.md b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/README.md
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/base_model.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/base_model.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/coco.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/coco.py
old mode 100755
new mode 100644
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rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/coco.py
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/coco.py
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_dataset.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_dataset.sh
similarity index 98%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_dataset.sh
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_dataset.sh
index e112564098c..e89739127bb 100644
--- a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_dataset.sh
+++ b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_dataset.sh
@@ -1,9 +1,9 @@
-#!/usr/bin/env bash
-### This file is originally from: [mlcommons repo](https://github.com/mlcommons/inference/tree/r0.5/others/cloud/single_stage_detector/download_dataset.sh)
-DATASET_DIR=${DATASET_DIR-$PWD}
-
-dir=$(pwd)
-mkdir -p ${DATASET_DIR}/coco; cd ${DATASET_DIR}/coco
-curl -O http://images.cocodataset.org/zips/val2017.zip; unzip val2017.zip
-curl -O http://images.cocodataset.org/annotations/annotations_trainval2017.zip; unzip annotations_trainval2017.zip
+#!/usr/bin/env bash
+### This file is originally from: [mlcommons repo](https://github.com/mlcommons/inference/tree/r0.5/others/cloud/single_stage_detector/download_dataset.sh)
+DATASET_DIR=${DATASET_DIR-$PWD}
+
+dir=$(pwd)
+mkdir -p ${DATASET_DIR}/coco; cd ${DATASET_DIR}/coco
+curl -O http://images.cocodataset.org/zips/val2017.zip; unzip val2017.zip
+curl -O http://images.cocodataset.org/annotations/annotations_trainval2017.zip; unzip annotations_trainval2017.zip
cd $dir
\ No newline at end of file
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_model.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_model.sh
similarity index 98%
rename from examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_model.sh
rename to examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_model.sh
index c4ec0683d34..e559f406772 100644
--- a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_model.sh
+++ b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/download_model.sh
@@ -1,8 +1,8 @@
-#!/usr/bin/env bash
-### This file is originally from: [mlcommons repo](https://github.com/mlcommons/inference/tree/r0.5/others/cloud/single_stage_detector/download_model.sh)
-CHECKPOINT_DIR=${CHECKPOINT_DIR-$PWD}
-
-dir=$(pwd)
-mkdir -p ${CHECKPOINT_DIR}/pretrained; cd ${CHECKPOINT_DIR}/pretrained
-wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=13kWgEItsoxbVKUlkQz4ntjl1IZGk6_5Z' -O 'resnet34-ssd1200.pth'
+#!/usr/bin/env bash
+### This file is originally from: [mlcommons repo](https://github.com/mlcommons/inference/tree/r0.5/others/cloud/single_stage_detector/download_model.sh)
+CHECKPOINT_DIR=${CHECKPOINT_DIR-$PWD}
+
+dir=$(pwd)
+mkdir -p ${CHECKPOINT_DIR}/pretrained; cd ${CHECKPOINT_DIR}/pretrained
+wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=13kWgEItsoxbVKUlkQz4ntjl1IZGk6_5Z' -O 'resnet34-ssd1200.pth'
cd $dir
\ No newline at end of file
diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/infer.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/infer.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/infer_weight_sharing.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/infer_weight_sharing.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/oob_utils.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/oob_utils.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/requirements.txt b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/requirements.txt
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/run_benchmark.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/run_benchmark.sh
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/run_quant.sh b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/run_quant.sh
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/ssd300.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/ssd300.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/ssd_r34.py b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/ssd_r34.py
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diff --git a/examples/pytorch/object_detection/ssd_resnet34/quantization/qat/fx/README.md b/examples/deprecated/pytorch/object_detection/ssd_resnet34/quantization/qat/fx/README.md
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diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/requirements.txt b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/requirements.txt
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diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/test.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/test.py
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diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/train.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/train.py
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rename from examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/train.py
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index 00fe3de696d..9d2d7a79e7c 100644
--- a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/train.py
+++ b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/train.py
@@ -5,7 +5,7 @@
from utils.utils import *
from utils.datasets import *
from utils.parse_config import *
-from test import evaluate
+from examples.deprecated.pytorch.object_detection.yolo_v3.quantization.ptq_static.fx.test import evaluate
from terminaltables import AsciiTable
diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/augmentations.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/augmentations.py
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diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/datasets.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/datasets.py
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rename to examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/datasets.py
diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/logger.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/logger.py
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diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/parse_config.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/parse_config.py
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rename to examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/parse_config.py
diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/utils.py b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/utils.py
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rename to examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/utils/utils.py
diff --git a/examples/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/weights/download_weights.sh b/examples/deprecated/pytorch/object_detection/yolo_v3/quantization/ptq_static/fx/weights/download_weights.sh
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diff --git a/examples/pytorch/object_detection/yolo_v5/pruning/eager/README.md b/examples/deprecated/pytorch/object_detection/yolo_v5/pruning/eager/README.md
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diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/README.md b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/README.md
similarity index 97%
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index 39a46923f15..dde21cd0ceb 100644
--- a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/README.md
+++ b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/README.md
@@ -1,53 +1,53 @@
-Step-by-Step
-============
-
-This document describes the step-by-step instructions for reproducing torchaudio pretrained model wav2vec2.0 and Hubert tuning results with Intel® Neural Compressor.
-
-## Prerequisite
-
-### 1. Environment
-```shell
-# Install Intel® Neural Compressor
-pip install neural-compressor
-```
-```shell
-cd examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx
-pip install -r requirements.txt
-```
-> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
-
-### 2. Prepare Dataset
-
-Download [LibriSpeech](https://www.openslr.org/resources/12/) Raw audio to dir: /path/to/speech_dataset. The dir include below folder:
-
-```bash
-cd /path/to/speech_dataset
-wget http://us.openslr.org/resources/12/test-clean.tar.gz
-wget http://us.openslr.org/resources/12/test-other.tar.gz
-tar -zxvf test-clean.tar.gz -C ./
-tar -zxvf test-other.tar.gz -C ./
-
-ls /path/to/speech_dataset
-LibriSpeech #folder_in_archive
-cd LibriSpeech
-BOOKS.TXT CHAPTERS.TXT dataset_infos.json LICENSE.TXT README.TXT SPEAKERS.TXT test-clean test-other
-```
-# Run
-
-### 1. Wav2vec2.0
-```bash
-bash run_quant.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=wav2vec2 --output_model=./saved_results
-```
-```bash
-bash run_benchmark.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=wav2vec2 --output_model=./saved_results --mode=performance
-```
-### 2. Hubert
-
-```bash
-bash run_quant.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results
-```
-```bash
-bash run_benchmark.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results --mode=performance
-```
-
-
+Step-by-Step
+============
+
+This document describes the step-by-step instructions for reproducing torchaudio pretrained model wav2vec2.0 and Hubert tuning results with Intel® Neural Compressor.
+
+## Prerequisite
+
+### 1. Environment
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+```shell
+cd examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx
+pip install -r requirements.txt
+```
+> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### 2. Prepare Dataset
+
+Download [LibriSpeech](https://www.openslr.org/resources/12/) Raw audio to dir: /path/to/speech_dataset. The dir include below folder:
+
+```bash
+cd /path/to/speech_dataset
+wget http://us.openslr.org/resources/12/test-clean.tar.gz
+wget http://us.openslr.org/resources/12/test-other.tar.gz
+tar -zxvf test-clean.tar.gz -C ./
+tar -zxvf test-other.tar.gz -C ./
+
+ls /path/to/speech_dataset
+LibriSpeech #folder_in_archive
+cd LibriSpeech
+BOOKS.TXT CHAPTERS.TXT dataset_infos.json LICENSE.TXT README.TXT SPEAKERS.TXT test-clean test-other
+```
+# Run
+
+### 1. Wav2vec2.0
+```bash
+bash run_quant.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=wav2vec2 --output_model=./saved_results
+```
+```bash
+bash run_benchmark.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=wav2vec2 --output_model=./saved_results --mode=performance
+```
+### 2. Hubert
+
+```bash
+bash run_quant.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results
+```
+```bash
+bash run_benchmark.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results --mode=performance
+```
+
+
diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/requirements.txt b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/requirements.txt
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diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/run_asr.py b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/run_asr.py
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diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/run_benchmark.sh b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/run_benchmark.sh
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diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/run_quant.sh b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_dynamic/fx/run_quant.sh
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diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/README.md b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/README.md
similarity index 96%
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index 6821d5790f7..ba8dd1a9fce 100644
--- a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/README.md
+++ b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/README.md
@@ -1,46 +1,46 @@
-Step-by-Step
-============
-
-This document describes the step-by-step instructions for reproducing torchaudio pretrained model wav2vec2.0 and Hubert tuning results with Intel® Neural Compressor.
-
-## Prerequisite
-
-### 1. Environment
-```shell
-# Install Intel® Neural Compressor
-pip install neural-compressor
-```
-```shell
-cd examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx
-pip install -r requirements.txt
-```
-> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
-
-### 2. Prepare Dataset
-
-Download [LibriSpeech](https://www.openslr.org/resources/12/) Raw audio to dir: /path/to/speech_dataset. The dir include below folder:
-
-```bash
-cd /path/to/speech_dataset
-wget http://us.openslr.org/resources/12/test-clean.tar.gz
-wget http://us.openslr.org/resources/12/test-other.tar.gz
-tar -zxvf test-clean.tar.gz -C ./
-tar -zxvf test-other.tar.gz -C ./
-
-ls /path/to/speech_dataset
-LibriSpeech #folder_in_archive
-cd LibriSpeech
-BOOKS.TXT CHAPTERS.TXT dataset_infos.json LICENSE.TXT README.TXT SPEAKERS.TXT test-clean test-other
-```
-# Run
-
-## Hubert
-
-```bash
-bash run_quant.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results
-```
-```bash
-bash run_benchmark.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results --mode=performance
-```
-
-
+Step-by-Step
+============
+
+This document describes the step-by-step instructions for reproducing torchaudio pretrained model wav2vec2.0 and Hubert tuning results with Intel® Neural Compressor.
+
+## Prerequisite
+
+### 1. Environment
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+```shell
+cd examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx
+pip install -r requirements.txt
+```
+> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### 2. Prepare Dataset
+
+Download [LibriSpeech](https://www.openslr.org/resources/12/) Raw audio to dir: /path/to/speech_dataset. The dir include below folder:
+
+```bash
+cd /path/to/speech_dataset
+wget http://us.openslr.org/resources/12/test-clean.tar.gz
+wget http://us.openslr.org/resources/12/test-other.tar.gz
+tar -zxvf test-clean.tar.gz -C ./
+tar -zxvf test-other.tar.gz -C ./
+
+ls /path/to/speech_dataset
+LibriSpeech #folder_in_archive
+cd LibriSpeech
+BOOKS.TXT CHAPTERS.TXT dataset_infos.json LICENSE.TXT README.TXT SPEAKERS.TXT test-clean test-other
+```
+# Run
+
+## Hubert
+
+```bash
+bash run_quant.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results
+```
+```bash
+bash run_benchmark.sh --dataset_location=/path/to/speech_dataset/LibriSpeech/test-clean --input_model=hubert --output_model=./saved_results --mode=performance
+```
+
+
diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/requirements.txt b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/requirements.txt
similarity index 100%
rename from examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/requirements.txt
rename to examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/requirements.txt
diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_asr.py b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_asr.py
similarity index 100%
rename from examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_asr.py
rename to examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_asr.py
diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_benchmark.sh b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_benchmark.sh
similarity index 100%
rename from examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_benchmark.sh
rename to examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_benchmark.sh
diff --git a/examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_quant.sh b/examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_quant.sh
old mode 100755
new mode 100644
similarity index 100%
rename from examples/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_quant.sh
rename to examples/deprecated/pytorch/speech_recognition/torchaudio_models/quantization/ptq_static/fx/run_quant.sh
diff --git a/examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/README.md b/examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/README.md
similarity index 97%
rename from examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/README.md
rename to examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/README.md
index ff166868444..0df64eac5f0 100644
--- a/examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/README.md
+++ b/examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/README.md
@@ -1,36 +1,36 @@
-Step-by-Step
-============
-
-This document describes the step-by-step instructions for reproducing [openai/whisper-large](https://huggingface.co/openai/whisper-large) from [transformers](https://github.com/huggingface/transformers.git) tuning results with Intel® Neural Compressor.
-
-## Prerequisite
-
-### 1. Environment
-```shell
-# Install Intel® Neural Compressor
-pip install neural-compressor
-```
-```shell
-cd examples/pytorch/speech_recognition/whisper_large/quantization/ptq_static/fx
-pip install -r requirements.txt
-```
-> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
-
-```
-# Run
-
-`--dataset_location` is used to get local saved huggingface/datasets cache.
-
-```bash
-bash run_quant.sh --topology=whisper_large --output_dir=./saved_results
-```
-
-```bash
-# fp32
-bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=performance
-bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=benchmark
-# int8
-bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=performance --int8=true
-bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=benchmark --int8=true
-```
-
+Step-by-Step
+============
+
+This document describes the step-by-step instructions for reproducing [openai/whisper-large](https://huggingface.co/openai/whisper-large) from [transformers](https://github.com/huggingface/transformers.git) tuning results with Intel® Neural Compressor.
+
+## Prerequisite
+
+### 1. Environment
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+```shell
+cd examples/pytorch/speech_recognition/whisper_large/quantization/ptq_static/fx
+pip install -r requirements.txt
+```
+> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+```
+# Run
+
+`--dataset_location` is used to get local saved huggingface/datasets cache.
+
+```bash
+bash run_quant.sh --topology=whisper_large --output_dir=./saved_results
+```
+
+```bash
+# fp32
+bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=performance
+bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=benchmark
+# int8
+bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=performance --int8=true
+bash run_benchmark.sh --topology=whisper_large --output_model=./saved_results --mode=benchmark --int8=true
+```
+
diff --git a/examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/requirements.txt b/examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/requirements.txt
similarity index 100%
rename from examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/requirements.txt
rename to examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/requirements.txt
diff --git a/examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_benchmark.sh b/examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_benchmark.sh
similarity index 100%
rename from examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_benchmark.sh
rename to examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_benchmark.sh
diff --git a/examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_quant.sh b/examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_quant.sh
old mode 100755
new mode 100644
similarity index 100%
rename from examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_quant.sh
rename to examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_quant.sh
diff --git a/examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_whisper_large.py b/examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_whisper_large.py
old mode 100755
new mode 100644
similarity index 100%
rename from examples/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_whisper_large.py
rename to examples/deprecated/pytorch/speech_recognition/whisper_large/quantization/ptq_dynamic/fx/run_whisper_large.py
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/README.md b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/README.md
similarity index 75%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/README.md
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/README.md
index 057b3559756..aed9c1c2f34 100644
--- a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/README.md
+++ b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/README.md
@@ -1,4 +1,4 @@
-Step-by-Step
+Step-by-Step (Deprecated)
============
This document is used to list steps of reproducing TensorFlow Object Detection models tuning results. This example can run on Intel CPUs and GPUs.
@@ -61,6 +61,18 @@ unzip ppi.zip
# Run
+## Quantization Config
+
+The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
+
+```
+config = PostTrainingQuantConfig(
+ device="gpu",
+ backend="itex",
+ ...
+ )
+```
+
## 1. Quantization
```shell
@@ -87,18 +99,23 @@ For graphsage, we applied the latter one because our philosophy is to enable the
After prepare step is done, we just need update main.py like below.
```python
if args.tune:
- from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
- from neural_compressor.tensorflow.utils import BaseDataLoader
-
+ from neural_compressor import quantization
+ from neural_compressor.data import DataLoader
+ from neural_compressor.config import PostTrainingQuantConfig
dataset = CustomDataset()
- calib_dataloader = BaseDataLoader(dataset=dataset, batch_size=1, collate_fn=collate_function)
- quant_config = StaticQuantConfig()
- q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+ calib_dataloader=DataLoader(framework='tensorflow', dataset=dataset, \
+ batch_size=1, collate_fn = collate_function)
+ conf = PostTrainingQuantConfig()
+ q_model = quantization.fit(args.input_graph, conf=conf, \
+ calib_dataloader=calib_dataloader, eval_func=evaluate)
q_model.save(args.output_graph)
if args.benchmark:
if args.mode == 'performance':
- evaluate(args.input_graph)
+ from neural_compressor.benchmark import fit
+ from neural_compressor.config import BenchmarkConfig
+ conf = BenchmarkConfig()
+ fit(args.input_graph, conf, b_func=evaluate)
elif args.mode == 'accuracy':
acc_result = evaluate(args.input_graph)
print("Batch size = %d" % args.batch_size)
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/dataloader.py b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/dataloader.py
similarity index 98%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/dataloader.py
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/dataloader.py
index e2a1d28d7d7..3e237dd972e 100644
--- a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/dataloader.py
+++ b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/dataloader.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/main.py b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/main.py
similarity index 82%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/main.py
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/main.py
index 87837510d3d..8d80b582352 100644
--- a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/main.py
+++ b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/main.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -58,7 +58,7 @@
def prepare_Dataset():
data_location = args.dataset_location
pretrained_model = args.input_graph
- data = dataloader.load_data(prefix=data_location+'/ppi')
+ data = dataloader.load_data(prefix=data_location + '/ppi')
G = data[0]
features = data[1]
id_map = data[2]
@@ -70,13 +70,13 @@ def prepare_Dataset():
context_pairs = data[3]
placeholders = utils.construct_placeholders(num_classes)
- minibatch = utils.NodeMinibatchIterator(G,
- id_map,
- placeholders,
- class_map,
- num_classes,
- batch_size=args.batch_size,
- context_pairs = context_pairs)
+ minibatch = utils.NodeMinibatchIterator(G,
+ id_map,
+ placeholders,
+ class_map,
+ num_classes,
+ batch_size=args.batch_size,
+ context_pairs = context_pairs)
return minibatch
class CustomDataset(object):
@@ -110,7 +110,7 @@ def evaluate(model):
Returns:
accuracy (float): evaluation result, the larger is better.
"""
- from neural_compressor.tensorflow import Model
+ from neural_compressor.model import Model
model = Model(model)
output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
model.output_tensor[0]
@@ -163,22 +163,27 @@ class eval_graphsage_optimized_graph:
def run(self):
"""This is neural_compressor function include tuning, export and benchmark option."""
- from neural_compressor.common import set_random_seed
+ from neural_compressor import set_random_seed
set_random_seed(9527)
if args.tune:
- from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
- from neural_compressor.tensorflow.utils import BaseDataLoader
-
+ from neural_compressor import quantization
+ from neural_compressor.data import DataLoader
+ from neural_compressor.config import PostTrainingQuantConfig
dataset = CustomDataset()
- calib_dataloader = BaseDataLoader(dataset=dataset, batch_size=1, collate_fn=collate_function)
- quant_config = StaticQuantConfig()
- q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+ calib_dataloader=DataLoader(framework='tensorflow', dataset=dataset, \
+ batch_size=1, collate_fn = collate_function)
+ conf = PostTrainingQuantConfig()
+ q_model = quantization.fit(args.input_graph, conf=conf, \
+ calib_dataloader=calib_dataloader, eval_func=evaluate)
q_model.save(args.output_graph)
if args.benchmark:
if args.mode == 'performance':
- evaluate(args.input_graph)
+ from neural_compressor.benchmark import fit
+ from neural_compressor.config import BenchmarkConfig
+ conf = BenchmarkConfig()
+ fit(args.input_graph, conf, b_func=evaluate)
elif args.mode == 'accuracy':
acc_result = evaluate(args.input_graph)
print("Batch size = %d" % args.batch_size)
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/requirements.txt b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/requirements.txt
similarity index 100%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/requirements.txt
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/requirements.txt
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/run_benchmark.sh b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/run_benchmark.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/run_benchmark.sh
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/run_benchmark.sh
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/run_quant.sh b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/run_quant.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/run_quant.sh
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/run_quant.sh
diff --git a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/utils.py b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/utils.py
similarity index 99%
rename from examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/utils.py
rename to examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/utils.py
index babe7146f5c..7ff4311b80d 100644
--- a/examples/3.x_api/tensorflow/graph_networks/graphsage/quantization/ptq/utils.py
+++ b/examples/deprecated/tensorflow/graph_networks/graphsage/quantization/ptq/utils.py
@@ -1,6 +1,6 @@
#!/usr/bin/env bash
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/examples/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/README.md b/examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/README.md
similarity index 100%
rename from examples/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/README.md
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/README.md
diff --git a/examples/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/main.py b/examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/main.py
similarity index 100%
rename from examples/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/main.py
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/main.py
diff --git a/examples/3.x_api/tensorflow/image_recognition/inception_v3/quantization/ptq/run_benchmark.sh b/examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/run_benchmark.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/inception_v3/quantization/ptq/run_benchmark.sh
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/run_benchmark.sh
diff --git a/examples/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/run_quant.sh b/examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/run_quant.sh
similarity index 100%
rename from examples/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/run_quant.sh
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/efficientnet_v2_b0/quantization/ptq/run_quant.sh
diff --git a/examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/README.md b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/README.md
similarity index 95%
rename from examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/README.md
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/README.md
index 59ad4d5de08..b63c2a736dc 100644
--- a/examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/README.md
+++ b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/README.md
@@ -46,7 +46,7 @@ image recognition
## 3. Prepare Dataset
TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-We also prepared related scripts in [TF image_recognition example](/examples/tensorflow/image_recognition/tensorflow_models/mobilenet_v1/quantization/ptq#3-prepare-dataset).
+We also prepared related scripts in [TF image_recognition example](/examples/deprecated/tensorflow/tensorflow/image_recognition/tensorflow_models/mobilenet_v1/quantization/ptq#3-prepare-dataset).
# Run Command
diff --git a/examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/main.py b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/main.py
similarity index 100%
rename from examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/main.py
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/main.py
diff --git a/examples/3.x_api/tensorflow/image_recognition/mobilenet_v2/quantization/ptq/run_benchmark.sh b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/run_benchmark.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/mobilenet_v2/quantization/ptq/run_benchmark.sh
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/run_benchmark.sh
diff --git a/examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/run_quant.sh b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/run_quant.sh
similarity index 100%
rename from examples/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/run_quant.sh
rename to examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v1/quantization/ptq/run_quant.sh
diff --git a/examples/tensorflow/image_recognition/SavedModel/mobilenet_v2/quantization/ptq/README.md b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v2/quantization/ptq/README.md
similarity index 95%
rename from examples/tensorflow/image_recognition/SavedModel/mobilenet_v2/quantization/ptq/README.md
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index a3e1278645c..20adba9545e 100644
--- a/examples/tensorflow/image_recognition/SavedModel/mobilenet_v2/quantization/ptq/README.md
+++ b/examples/deprecated/tensorflow/image_recognition/SavedModel/mobilenet_v2/quantization/ptq/README.md
@@ -46,7 +46,7 @@ image recognition
## 3. Prepare Dataset
TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-We also prepared related scripts in [TF image_recognition example](/examples/tensorflow/image_recognition/tensorflow_models/mobilenet_v1/quantization/ptq#3-prepare-dataset).
+We also prepared related scripts in [TF image_recognition example](/examples/deprecated/tensorflow/tensorflow/image_recognition/tensorflow_models/mobilenet_v1/quantization/ptq#3-prepare-dataset).
# Run Command
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diff --git a/examples/tensorflow/nlp/bert_base_mrpc/quantization/ptq/run_classifier.py b/examples/deprecated/tensorflow/nlp/bert_base_mrpc/quantization/ptq/run_classifier.py
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index 3e2e430407e..88b0d12aa34 100644
--- a/examples/tensorflow/nlp/bert_base_mrpc/quantization/ptq/run_classifier.py
+++ b/examples/deprecated/tensorflow/nlp/bert_base_mrpc/quantization/ptq/run_classifier.py
@@ -886,7 +886,7 @@ def main(_):
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
- FLAGS.init_checkpoint)
+ FLAGS.init_checkpoint)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
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diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py
similarity index 99%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py
index b278c37581d..a9d64a5765d 100644
--- a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py
@@ -297,7 +297,7 @@ def main(_):
run_config = tf.compat.v1.estimator.tpu.RunConfig(
cluster=None,
master=None,
- model_dir='./',
+ model_dir='/',
save_checkpoints_steps=1000,
session_config=session_config,
tpu_config=tf.compat.v1.estimator.tpu.TPUConfig(
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/generic_ops.py b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/generic_ops.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/generic_ops.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/generic_ops.py
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/modeling.py b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/modeling.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/modeling.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/modeling.py
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/optimization.py b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/optimization.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/optimization.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/optimization.py
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_dataset.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_dataset.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_dataset.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_dataset.sh
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_model.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_model.sh
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_model.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_model.sh
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/run_benchmark.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/run_benchmark.sh
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/run_benchmark.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/run_benchmark.sh
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/run_quant.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/run_quant.sh
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/run_quant.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/run_quant.sh
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/tokenization.py b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/tokenization.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/tokenization.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/tokenization.py
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/tune_squad.py b/examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/tune_squad.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/tune_squad.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad/quantization/ptq/tune_squad.py
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
similarity index 82%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
index 41a673fc834..15d84c7f32e 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
@@ -1,4 +1,4 @@
-Step-by-Step
+Step-by-Step (Deprecated)
============
This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor tuning result of Intel® Model Zoo bert large model on squad v1.1 task.
@@ -74,7 +74,7 @@ bash prepare_dataset.sh --output_dir=./data
### Convert the dataset to TF Record format
After the dataset is downloaded by either of ways above, the dataset should be converted to files of TF Record format.
```shell
-python create_tf_record.py --vocab_file=data/vocab.txt --predict_file=data/dev-v1.1.json --output_file=data/eval.tf_record
+python create_tf_record.py --vocab_file=data/vocab.txt --predict_file=data/dev-v1.1.json --output_file=./eval.tf_record
```
# Run Command
@@ -82,11 +82,22 @@ python create_tf_record.py --vocab_file=data/vocab.txt --predict_file=data/dev-v
## Quantization
```shell
- bash run_quant.sh --input_model=./fp32_bert_squad.pb --output_model=./bert_squad_int8.pb --dataset_location=data
+ bash run_quant.sh --input_model=./fp32_bert_squad.pb --output_model=./bert_squad_int8.pb --dataset_location=/path/to/evaluation/dataset
```
+### Quantization Config
+The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
+
+```
+config = PostTrainingQuantConfig(
+ device="gpu",
+ backend="itex",
+ ...
+ )
+```
+
## Benchmark
```shell
- bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=accuracy --dataset_location=data --batch_size=64
- bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=performance --dataset_location=data --batch_size=64
+ bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=accuracy --dataset_location=/path/to/evaluation/dataset --batch_size=64
+ bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=performance --dataset_location=/path/to/evaluation/dataset --batch_size=64
```
\ No newline at end of file
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_pretraining_data.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_pretraining_data.py
similarity index 99%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_pretraining_data.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_pretraining_data.py
index 8adecb971fd..8fb20e36c59 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_pretraining_data.py
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_pretraining_data.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_tf_record.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_tf_record.py
similarity index 99%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_tf_record.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_tf_record.py
index 12c6486283d..d24d701d953 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_tf_record.py
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/create_tf_record.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -113,7 +113,7 @@ def __init__(self,
self.is_impossible = is_impossible
-def read_squad_examples(input_file, is_training=None):
+def read_squad_examples(input_file, is_training):
"""Read a SQuAD json file into a list of SquadExample."""
with tf.io.gfile.GFile(input_file, "r") as reader:
input_data = json.load(reader)["data"]
@@ -196,8 +196,8 @@ def is_whitespace(c):
def convert_examples_to_features(examples, tokenizer, max_seq_length,
- doc_stride, max_query_length, is_training=None,
- output_fn=None):
+ doc_stride, max_query_length, is_training,
+ output_fn):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/evaluate_squad.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/evaluate_squad.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/evaluate_squad.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/evaluate_squad.py
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/export_classifier.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/export_classifier.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/export_classifier.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/export_classifier.py
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py
similarity index 99%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py
index b278c37581d..a9d64a5765d 100644
--- a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py
@@ -297,7 +297,7 @@ def main(_):
run_config = tf.compat.v1.estimator.tpu.RunConfig(
cluster=None,
master=None,
- model_dir='./',
+ model_dir='/',
save_checkpoints_steps=1000,
session_config=session_config,
tpu_config=tf.compat.v1.estimator.tpu.TPUConfig(
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/generic_ops.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/generic_ops.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/generic_ops.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/generic_ops.py
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/modeling.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/modeling.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/modeling.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/modeling.py
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/optimization.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/optimization.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/optimization.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/optimization.py
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_dataset.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_dataset.sh
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad/quantization/ptq/prepare_dataset.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_dataset.sh
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_model.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_model.sh
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_model.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/prepare_model.sh
diff --git a/examples/helloworld/tf_example4/requirements.txt b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/requirements.txt
similarity index 100%
rename from examples/helloworld/tf_example4/requirements.txt
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/requirements.txt
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_benchmark.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_benchmark.sh
similarity index 96%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_benchmark.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_benchmark.sh
index aa8d269a79a..b367b93247b 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_benchmark.sh
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_benchmark.sh
@@ -38,7 +38,7 @@ function init_params {
# run_tuning
function run_benchmark {
- python main.py \
+ python tune_squad.py \
--input_model=${input_model} \
--mode=${mode} \
--dataset_location=${dataset_location} \
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_quant.sh b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_quant.sh
similarity index 96%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_quant.sh
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_quant.sh
index ddc30b40177..bee4ecaf784 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_quant.sh
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/run_quant.sh
@@ -34,7 +34,7 @@ function init_params {
# run_tuning
function run_tuning {
- python main.py \
+ python tune_squad.py \
--input_model=${input_model} \
--output_model=${output_model} \
--dataset_location=${dataset_location} \
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tokenization.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tokenization.py
similarity index 99%
rename from examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tokenization.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tokenization.py
index 77c3175db07..dbf083617f5 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tokenization.py
+++ b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tokenization.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tune_squad.py b/examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tune_squad.py
similarity index 100%
rename from examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tune_squad.py
rename to examples/deprecated/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/tune_squad.py
diff --git a/examples/tensorflow/nlp/distilbert_base/quantization/ptq/README.md b/examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
similarity index 100%
rename from examples/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
rename to examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
diff --git a/examples/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py b/examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
similarity index 100%
rename from examples/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
rename to examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
diff --git a/examples/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt b/examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
similarity index 100%
rename from examples/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
rename to examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
diff --git a/examples/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh b/examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
similarity index 100%
rename from examples/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
rename to examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
diff --git a/examples/tensorflow/nlp/distilbert_base/quantization/ptq/run_inference.py b/examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/run_inference.py
similarity index 100%
rename from examples/tensorflow/nlp/distilbert_base/quantization/ptq/run_inference.py
rename to examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/run_inference.py
diff --git a/examples/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh b/examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
similarity index 100%
rename from examples/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
rename to examples/deprecated/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/README.md b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/README.md
similarity index 76%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/README.md
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/README.md
index 7a8c22631e0..24b558ed032 100644
--- a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/README.md
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/README.md
@@ -1,4 +1,4 @@
-Step-by-Step
+Step-by-Step (Deprecated)
============
This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor smooth quantization of language models gpt-j-6B.
@@ -11,7 +11,7 @@ This document is used to list steps of reproducing TensorFlow Intel® Neural Com
```shell
# Install Intel® Neural Compressor
pip install neural-compressor
-pip install -r requirements.txt
+pip install -r requirements
```
## 2. Prepare Pretrained model
@@ -91,6 +91,16 @@ class MyDataloader:
return self.length
```
+### Quantization Config
+The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
+
+```python
+config = PostTrainingQuantConfig(
+ device="gpu",
+ backend="itex",
+ ...
+ )
+```
### Code Update
After prepare step is done, we add the code for quantization tuning to generate quantized model.
@@ -114,24 +124,30 @@ To apply quantization, the function that maps names from AutoTrackable variables
Please use the recipe to set smooth quantization.
```python
- from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig, autotune
- from neural_compressor.tensorflow.quantization import TuningConfig
- from neural_compressor.tensorflow.utils import BaseDataLoader
-
+ from neural_compressor import quantization, PostTrainingQuantConfig
calib_dataloader = MyDataloader(mydata, batch_size=run_args.batch_size)
- quant_config = [SmoothQuantConfig(alpha=0.52705), StaticQuantConfig(act_dtype="int8", weight_dtype="int8")]
- tune_config = TuningConfig(config_set=quant_config, max_trials=1)
+ recipes = {"smooth_quant": True, "smooth_quant_args": {'alpha': 0.52705}}
+ conf = PostTrainingQuantConfig(quant_level=1,
+ excluded_precisions=["bf16"],##use basic tuning
+ recipes=recipes,
+ calibration_sampling_size=[1],
+ )
+
model.weight_name_mapping = weight_name_mapping
- q_model = autotune(model,
- tune_config,
- eval_fn=evaluate,
- calib_dataloader=calib_dataloader)
+ q_model = quantization.fit( model,
+ conf,
+ eval_func=evaluate,
+ calib_dataloader=calib_dataloader)
+
q_model.save(run_args.output_model)
```
#### Benchmark
```python
if run_args.mode == "performance":
- evaluate(model.model)
+ from neural_compressor.benchmark import fit
+ from neural_compressor.config import BenchmarkConfig
+ conf = BenchmarkConfig(warmup=10, iteration=run_args.iteration, cores_per_instance=4, num_of_instance=1)
+ fit(model, conf, b_func=evaluate)
elif run_args.mode == "accuracy":
acc_result = evaluate(model.model)
print("Batch size = %d" % run_args.batch_size)
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/main.py b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/main.py
similarity index 90%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/main.py
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/main.py
index faf54b65bd0..3de5c654d58 100644
--- a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/main.py
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/main.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -276,10 +276,6 @@ def evaluate(model, tf_eval_dataset=mydata):
iteration = run_args.iteration
correct = 0
latency_list = []
- from neural_compressor.tensorflow.utils import BaseModel
-
- if isinstance(model, BaseModel):
- model = model.model
infer = model.signatures["serving_default"]
for idx, data in enumerate(tf_eval_dataset):
input_ids = tf.convert_to_tensor([data[:-1]], dtype=tf.int32)
@@ -320,26 +316,35 @@ def main():
with train_args.strategy.scope():
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
- from neural_compressor.tensorflow import Model
+ from neural_compressor import Model
model = Model(run_args.input_model, modelType='llm_saved_model')
if run_args.tune:
- from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig, autotune
- from neural_compressor.tensorflow.quantization import TuningConfig
- from neural_compressor.tensorflow.utils import BaseDataLoader
+ from neural_compressor.config import AccuracyCriterion
+ from neural_compressor import quantization, PostTrainingQuantConfig
calib_dataloader = MyDataloader(mydata, batch_size=run_args.batch_size)
- quant_config = [SmoothQuantConfig(alpha=0.52705), StaticQuantConfig(act_dtype="int8", weight_dtype="int8")]
- tune_config = TuningConfig(config_set=quant_config, max_trials=1)
+ recipes = {"smooth_quant": True, "smooth_quant_args": {'alpha': 0.52705}}
+ conf = PostTrainingQuantConfig(quant_level=1,
+ excluded_precisions=["bf16"],##use basic tuning
+ recipes=recipes,
+ calibration_sampling_size=[1],
+ accuracy_criterion=AccuracyCriterion()
+ )
+
model.weight_name_mapping = weight_name_mapping
- q_model = autotune(model,
- tune_config,
- eval_fn=evaluate,
- calib_dataloader=calib_dataloader)
+ q_model = quantization.fit( model,
+ conf,
+ eval_func=evaluate,
+ calib_dataloader=calib_dataloader)
+
q_model.save(run_args.output_model)
if run_args.benchmark:
if run_args.mode == "performance":
- evaluate(model.model)
+ from neural_compressor.benchmark import fit
+ from neural_compressor.config import BenchmarkConfig
+ conf = BenchmarkConfig(warmup=10, iteration=run_args.iteration, cores_per_instance=4, num_of_instance=1)
+ fit(model, conf, b_func=evaluate)
elif run_args.mode == "accuracy":
acc_result = evaluate(model.model)
print("Batch size = %d" % run_args.batch_size)
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.py b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.py
similarity index 95%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.py
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.py
index cb4cd7f3f29..12ac19cf4ba 100644
--- a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.py
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.sh b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.sh
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/prepare_model.sh
diff --git a/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/requirements.txt b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/requirements.txt
new file mode 100644
index 00000000000..186124490e2
--- /dev/null
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/requirements.txt
@@ -0,0 +1,4 @@
+tensorflow==2.12.1
+transformers
+datasets
+numpy
\ No newline at end of file
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_benchmark.sh b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_benchmark.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_benchmark.sh
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_benchmark.sh
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_quant.sh b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_quant.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_quant.sh
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/gpt-j/run_quant.sh
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/README.md b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/README.md
similarity index 96%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/README.md
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/README.md
index fa45adbd5ef..386371f8eb5 100644
--- a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/README.md
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/README.md
@@ -1,4 +1,4 @@
-Step-by-Step
+Step-by-Step (Deprecated)
============
This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor quantization and smooth quantization of language models such as OPT and GPT2.
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/benchmark.py b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/benchmark.py
similarity index 83%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/benchmark.py
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/benchmark.py
index eda0d222d39..acf3299689b 100644
--- a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/benchmark.py
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/benchmark.py
@@ -1,20 +1,3 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2024 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
import os.path
import transformers
import tensorflow as tf
@@ -28,7 +11,7 @@
sys.path.insert(0, './')
parser = argparse.ArgumentParser()
-parser.add_argument('--optimized', action='store_true', help="eval fp32 model or optimized model")
+parser.add_argument('--int8', action='store_true', help="eval fp32 model or int8 model")
parser.add_argument('--model_name_or_path', type=str, default='facebook/opt-125m')
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--warmup', type=int, default=10)
@@ -64,6 +47,7 @@ def get_attention_mask(self, input_ids):
return tf.constant(1 - (input_ids==1).numpy().astype(int))
def evaluate_tf_v1(self, model):
+ # return 0.99 # TODO debug remove
total, hit = 0, 0
index = 1
infer = model.signatures["serving_default"]
@@ -173,18 +157,16 @@ def __len__(self):
evaluator = Evaluator(eval_dataset, tokenizer, 'cpu')
-if args.optimized:
- print("benchmarking optimized model")
- optimized_folder = model_name.split('/')[-1] + "_int8"
- if not os.path.exists(optimized_folder):
- print(f"could not find optimized folder {optimized_folder} ")
+if args.int8:
+ print("benchmarking int8 model")
+ int8_folder = model_name.split('/')[-1] + "_int8"
+ if not os.path.exists(int8_folder):
+ print(f"could not find int8 folder {int8_folder} ")
exit()
- model = tf.saved_model.load(optimized_folder) # tensorflow.python.trackable.autotrackable.AutoTrackable object
+ model = tf.saved_model.load(int8_folder) # tensorflow.python.trackable.autotrackable.AutoTrackable object
else:
print("benchmaking fp32 model")
- model = transformers.TFAutoModelForCausalLM.from_pretrained(model_name)
- from neural_compressor.tensorflow import Model
-
- model = Model(model).model # tensorflow.python.trackable.autotrackable.AutoTrackable object
+ print("Benchmaking fp32 model is not longer supported in this example. please go to 3.x_api examples or raise issue to us.")
+ exit(0)
evaluator.evaluate_tf_v1(model)
diff --git a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/main.py b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/main.py
similarity index 54%
rename from examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/main.py
rename to examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/main.py
index 8f012ceb404..b88cd9f7a09 100644
--- a/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/main.py
+++ b/examples/deprecated/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant/main.py
@@ -1,7 +1,7 @@
#
# -*- coding: utf-8 -*-
#
-# Copyright (c) 2024 Intel Corporation
+# Copyright (c) 2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -28,7 +28,10 @@
parser = argparse.ArgumentParser()
parser.add_argument('--sq', action='store_true', default=False, help="whether to use smooth quant")
+# parser.add_argument('--calib_num', type=int, default=100, help="calibration num for sq")
parser.add_argument('--model_name_or_path', type=str, default="facebook/opt-125m")
+# TODO auto tuning not supported currently for TF backend
+# parser.add_argument('--alpha', default=0.5, help="Set alpha=auto to use alpha tuning.")
parser.add_argument('--alpha', type=float, default=0.5, help="alpha value for smoothing.")
parser.add_argument('--log_frequency', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=16)
@@ -36,6 +39,58 @@
parser.add_argument('--fallback_add', action='store_true', default=False, help="Whether to add fp32 fallback option" )
args = parser.parse_args()
+class Evaluator:
+ def __init__(self, dataset, tokenizer, device, batch_size=args.batch_size):
+ self.dataset = dataset
+ self.tokenizer = tokenizer
+ self.device = device
+ self.dataloader = CustomDataloader(dataset, tokenizer, batch_size, device)
+
+ def evaluate(self, model):
+ # model.eval()
+ # The task is to predict the last word of the input.
+ total, hit = 0, 0
+ index = 1
+ for input_ids, label, label_indices in tqdm(self.dataloader):
+ # TFCausalLMOutputWithPast len: 2
+ # first element shape (16, 196, 50272)
+ # second element shape (16, 12, 196, 64)
+ outputs = model(input_ids)
+ last_token_logits = outputs[0].numpy()[np.arange(len(label_indices)), label_indices, :]
+ pred = last_token_logits.argmax(axis=-1)
+ total += label.shape[0]
+ hit += (pred == label.numpy()).sum().item()
+ if index % args.log_frequency == 0:
+ print(hit / total, flush=True)
+ index += 1
+ acc = hit / total
+ print(acc, flush=True)
+ return acc
+
+ def get_attention_mask(self, input_ids):
+ return tf.constant(1 - (input_ids==1).numpy().astype(int))
+
+ def evaluate_tf_v1(self, model):
+ # return 0.99 # TODO debug remove
+ total, hit = 0, 0
+ index = 1
+ infer = model.signatures["serving_default"]
+ for input_ids, label, label_indices in tqdm(self.dataloader):
+ attention_mask = self.get_attention_mask(input_ids)
+ input_ids = tf.constant(input_ids.numpy(), dtype=infer.inputs[0].dtype)
+ attention_mask = tf.constant(attention_mask.numpy(), dtype=infer.inputs[0].dtype)
+ results = infer(input_ids=input_ids, attention_mask=attention_mask) # len: 25 Identity: [16, 196, 50272], Identity_1: [16, 12, 196, 64]
+ last_token_logits = results['Identity'].numpy()[np.arange(len(label_indices)), label_indices, :]
+ pred = last_token_logits.argmax(axis=-1)
+ total += label.shape[0]
+ hit += (pred == label.numpy()).sum().item()
+ if index % args.log_frequency == 0:
+ print(hit / total, flush=True)
+ index += 1
+ acc = hit / total
+ print(acc, flush=True)
+ return acc
+
class CustomDataloader:
# for_calib=True in quantization, only input_id is needed, =False in evaluation need label
def __init__(self, dataset, tokenizer, batch_size=1, device='cpu', for_calib=False):
@@ -72,9 +127,13 @@ def pad_input(self, input): # input: a record
def __iter__(self):
if self.for_calib:
labels = None
+ # label_indices = None
for idx, record in enumerate(self.dataset):
input_id, label, label_index = self.pad_input(record)
attention_mask = self.get_attention_mask(input_id)
+ # compose attention_mask and input_id together
+ # during the calibration, it requires to yield a
+
- 
- 
-
+
+
+