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SCORE-Bench

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Where F′′ₐₜₜₙ and F′′ ffn are the corresponding FLOPs scales after selecting the largest value in {𝛼a l , 𝛼m l , 𝛼h l , 𝛼n l } in Eq. 10 respectively. For both stages, dynamic compression is trained with group-level gates, and the γ is set to 100.
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For group-level strategy, we trained the USDC method by splitting sub-groups on average, on random, and our recursive split method separately. The training is on the DeiT-small model on the Imagenet-1K dataset, the training batch size is 256 and all other parameter settings are the same. As shown in Tab. 5, we compare ...
Table 5: The comparisons of different sub-groups split methods for group-level gate augmentation strategy. The first column splits sub-groups uniformly with step size 32. The second column splits sub-groups uniformly with step size 8. The third column splits the sub-groups randomly with step size ranges in [1,64]. The f...
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Model B Top-1 Accuracy (%)
Avg-32 Avg-8 Random Ours
USDC (DeiT-S) 256 77.13 77.43 77.86 78.89
64 77.11 77.62 77.44 78.90
32 77.10 77.62 77.45 78.93
8 77.05 77.65 77.50 78.96
2 77.00 77.62 77.49 78.98
1 76.91 77.60 77.49 78.96
FLOPs - 3.30G 3.36G 3.30G 3.35G
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E. Visualizations
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We illustrate the structures of the compressed DeiT-Small model by USDC at Fig. 5. We trained the model in Fig. 5 by unified static and dynamic compression described in the main text. We can notice that the head number of MHSA and the hidden dimension of FFN were reduced by static compression, and some blocks were prun...
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L=1
FC+LN +ReLU+FC
R=0.75
head=4
R=0.96
h_dim=1276
L=2
FC+LN +ReLU+FC
R=0.92
head=4
R=0.98
h_dim=1319
L=3
Conv1d
R=0.95
head=4
R=0.89
h_dim=1394
L=4
Conv1d
Pruned
R=0.84
h_dim=1306
L=5
FC+LN +ReLU+FC
R=1.0
head=6,
R=0.81
h_dim=1302
L=6
Conv1d
R=0.91
head=6,
R=0.85
h_dim=1249
L=7
FC+BN +ReLU+FC
R=0.78
head=6,
R=0.66
h_dim=1409
L=8
FC
R=1.0
head=6,
Pruned
L=9
FC
R=1.0