[Quant] Add FX support in quantization examples #5797
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Commits on Apr 10, 2022
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[Quant] Add FX support in quantization examples
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. TODO: provide accuracy comparison. Test Plan: python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' --model='$MODEL' model: $MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, shufflenet_v2_x0_5 and shufflenet_v2_x1_0 Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
Commits on Apr 12, 2022
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. TODO: provide accuracy comparison. Test Plan: python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' --model='$MODEL' model: $MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, shufflenet_v2_x0_5 and shufflenet_v2_x1_0 Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. TODO: provide accuracy comparison. Test Plan: python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' --model='$MODEL' model: $MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, shufflenet_v2_x0_5 and shufflenet_v2_x1_0 Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
Commits on Apr 13, 2022
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. TODO: provide accuracy comparison. Test Plan: python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' --model='$MODEL' model: $MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, shufflenet_v2_x0_5 and shufflenet_v2_x1_0 Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. Test Plan: ``` # MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, # shufflenet_v2_x0_5, shufflenet_v2_x1_0, mobilenet_v2, mobilenet_v3_large # eager python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm" --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # fx python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm" --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # eager QAT (mobilenet only) python train_quantization.py --device="cuda" --backend="fbgemm" --model="$MODEL" --epochs=10 --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # fx QAT (mobilenet only) python train_quantization.py --device="cuda" --backend="fbgemm" --model="$MODEL" --epochs=10 --weights="IMAGENET1K_V1" --quantization-workflow-type="fx_graph_mode_quantization" ``` Results: - "Before" column refers to accuracies reported [here](https://github.com/pytorch/vision/blob/main/docs/source/models.rst#quantized-models) - TODO: Add results for QAT mobilenet after it's done <img width="641" alt="Screen Shot 2022-04-12 at 10 58 01 PM" src="https://user-images.githubusercontent.com/2133137/163091177-e1c1c666-c3f7-40c3-8866-c0743c264721.png"> Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
Commits on Apr 14, 2022
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. Test Plan: ``` # ==================== PTQ ==================== # MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, # shufflenet_v2_x0_5, shufflenet_v2_x1_0 # eager python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm"\ --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # fx python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm"\ --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # ==================== QAT ==================== # mobilenet_v2 eager python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v2"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.0001 --weight-decay=0.0001\ --quantization-workflow-type="eager_mode_quantization" # mobilenet_v2 fx python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v2"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.0001 --weight-decay=0.0001\ --quantization-workflow-type="fx_graph_mode_quantization" # mobilenet_v3_large eager python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v3_large"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.001 --weight-decay=0.00001\ --quantization-workflow-type="eager_mode_quantization" # mobilenet_v3_large fx python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v3_large"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.001 --weight-decay=0.00001\ --quantization-workflow-type="fx_graph_mode_quantization" ``` Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
Commits on Apr 21, 2022
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. Test Plan: ``` # ==================== PTQ ==================== # MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, # shufflenet_v2_x0_5, shufflenet_v2_x1_0 # eager python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm"\ --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # fx python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm"\ --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # ==================== QAT ==================== # mobilenet_v2 eager python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v2"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.0001 --weight-decay=0.0001\ --quantization-workflow-type="eager_mode_quantization" # mobilenet_v2 fx python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v2"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.0001 --weight-decay=0.0001\ --quantization-workflow-type="fx_graph_mode_quantization" # mobilenet_v3_large eager python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v3_large"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.001 --weight-decay=0.00001\ --quantization-workflow-type="eager_mode_quantization" # mobilenet_v3_large fx python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v3_large"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.001 --weight-decay=0.00001\ --quantization-workflow-type="fx_graph_mode_quantization" ``` Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
Commits on Apr 22, 2022
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Update on "[Quant] Add FX support in quantization examples"
Summary: Previously, the quantization examples use only eager mode quantization. This commit adds support for FX mode quantization as well. Test Plan: ``` # ==================== PTQ ==================== # MODEL is one of googlenet, inception_v3, resnet18, resnet50, resnext101_32x8d, # shufflenet_v2_x0_5, shufflenet_v2_x1_0 # eager python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm"\ --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # fx python train_quantization.py --device="cpu" --post-training-quantize --backend="fbgemm"\ --model="$MODEL" --weights="IMAGENET1K_V1" --quantization-workflow-type="eager_mode_quantization" # ==================== QAT ==================== # mobilenet_v2 eager python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v2"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.0001 --weight-decay=0.0001\ --quantization-workflow-type="eager_mode_quantization" # mobilenet_v2 fx python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v2"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.0001 --weight-decay=0.0001\ --quantization-workflow-type="fx_graph_mode_quantization" # mobilenet_v3_large eager python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v3_large"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.001 --weight-decay=0.00001\ --quantization-workflow-type="eager_mode_quantization" # mobilenet_v3_large fx python train_quantization.py --device="cuda" --backend="qnnpack" --model="mobilenet_v3_large"\ --epochs=10 --workers=64 --weights="IMAGENET1K_V1" --lr=0.001 --weight-decay=0.00001\ --quantization-workflow-type="fx_graph_mode_quantization" ``` <img width="638" alt="Screen Shot 2022-04-21 at 8 33 41 PM" src="https://user-images.githubusercontent.com/2133137/164572469-5848c86b-0813-42f6-bcb7-0298ff4bb25b.png"> Reviewers: jerryzh168, vkuzo Subscribers: jerryzh168, vkuzo [ghstack-poisoned]
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