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‎torchao/sparsity/prototype/superblock/benchmark.py

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from torchao.utils import benchmark_model, profiler_runner
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torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = False
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torch.backends.mha.set_fastpath_enabled(False)
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@torch.inference_mode

‎torchao/sparsity/prototype/superblock/evaluate.py

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)
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torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = False
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torch.backends.mha.set_fastpath_enabled(False)
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def main(args):

‎torchao/sparsity/prototype/superblock/evaluate.sh

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# MODEL=vit_b_16
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# BATCH_SIZE=256
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MODEL=vit_b_16
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BATCH_SIZE=256
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --header
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --quantization
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --sparsity semi_structured
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --sparsity semi_structured --quantization
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.80 --bsr 64 --weights-path checkpoints/$MODEL/sp0.80-ts64.pth
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.80 --bsr 64 --weights-path checkpoints/$MODEL/sp0.80-ts64.pth --quantization
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.84 --bsr 64 --weights-path checkpoints/$MODEL/sp0.84-ts64.pth
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.84 --bsr 64 --weights-path checkpoints/$MODEL/sp0.84-ts64.pth --quantization
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth
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# python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth --quantization
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --header
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --quantization
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --sparsity semi_structured
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --sparsity semi_structured --quantization
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.80 --bsr 64 --weights-path checkpoints/$MODEL/sp0.80-ts64.pth
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.80 --bsr 64 --weights-path checkpoints/$MODEL/sp0.80-ts64.pth --quantization
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.84 --bsr 64 --weights-path checkpoints/$MODEL/sp0.84-ts64.pth
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.84 --bsr 64 --weights-path checkpoints/$MODEL/sp0.84-ts64.pth --quantization
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth --quantization
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MODEL=vit_h_14
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BATCH_SIZE=128
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --header
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --quantization
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python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --sparsity semi_structured

‎torchao/sparsity/prototype/superblock/evaluation_results.txt

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model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,top-1_acc,eval_time
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vit_h_14,128,bfloat16,None,None,0.0,False,89.29286858974359,0:10:37
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vit_h_14,128,bfloat16,None,None,0.0,True,89.30689102564102,0:10:37
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vit_h_14,128,bfloat16,semi_structured,None,0.0,False,82.03926282051282,0:10:15
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vit_h_14,128,bfloat16,semi_structured,None,0.0,True,82.04527243589743,0:09:43
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vit_h_14,128,bfloat16,bsr,64,0.9,False,78.2411858974359,0:01:37
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vit_h_14,128,bfloat16,bsr,64,0.9,True,78.25721153846153,0:08:09
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vit_h_14,128,bfloat16,bsr,64,0.9,True,78.25721153846153,0:02:25
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vit_h_14,128,bfloat16,bsr,64,0.9,True,78.25721153846153,tensor(342.3044)
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vit_h_14,128,bfloat16,bsr,64,0.9,True,78.25721153846153,tensor(368.5567),135.4472771911621,531.5810546875
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vit_h_14,128,bfloat16,bsr,64,0.9,True,78.25721153846153,394.97543297270164,531.5810546875
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model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,top-1_acc,encoder img/s,max_mem (MB)
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vit_b_16,256,bfloat16,None,None,0.0,False,81.97716346153847,734.904399886552,247.97265625
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vit_b_16,256,bfloat16,None,None,0.0,True,81.89503205128206,230.83627917226997,196.841796875
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vit_b_16,256,bfloat16,semi_structured,None,0.0,False,77.05729166666667,1386.7278781133518,316.40234375
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vit_b_16,256,bfloat16,semi_structured,None,0.0,True,76.74078525641026,150.53603093207843,249.25390625
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vit_b_16,256,bfloat16,bsr,64,0.8,False,77.13541666666667,1469.2705176409308,179.55322265625
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vit_b_16,256,bfloat16,bsr,64,0.8,True,77.13341346153847,87.8480561274922,158.70361328125
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vit_b_16,256,bfloat16,bsr,64,0.84,False,76.14983974358974,1752.835540513905,174.01953125
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vit_b_16,256,bfloat16,bsr,64,0.84,True,76.0556891025641,1013.7495284783578,156.630859375
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vit_b_16,256,bfloat16,bsr,64,0.9,False,62.99879807692308,1702.289195236525,164.2822265625
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vit_b_16,256,bfloat16,bsr,64,0.9,True,62.946714743589745,987.5488468441617,152.5732421875
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model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,top-1_acc,encoder img/s,max_mem (MB)
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vit_h_14,128,bfloat16,None,None,0.0,False,89.29286858974359,81.02922135697278,1430.05615234375
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vit_h_14,128,bfloat16,None,None,0.0,True,89.3349358974359,56.076129157634355,1025.00927734375
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vit_h_14,128,bfloat16,semi_structured,None,0.0,False,82.03725961538461,75.83586253901329,1900.36279296875
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vit_h_14,128,bfloat16,semi_structured,None,0.0,True,82.06330128205128,36.36097831133589,1390.98779296875
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vit_h_14,128,bfloat16,bsr,64,0.9,False,78.21113782051282,350.91330496491446,599.6201171875
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vit_h_14,128,bfloat16,bsr,64,0.9,True,78.2051282051282,108.84048044884008,531.5810546875

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