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Extreme Small Deep Field-Weighted Factorization Machine - xsDeepFwFM

Acceleration and Compression of DeepFwFM.

DeepFwFM:

@inproceedings{deeplight,
  title={DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving},
  author={Wei Deng and Junwei Pan and Tian Zhou and Deguang Kong and Aaron Flores and Guang Lin},
  booktitle={International Conference on Web Search and Data Mining (WSDM'21)},
  year={2021}
}

In this repository additional model compression and acceleration will be conducted.

Used techniques:

  • QR Embeddings
  • Quantization
  • Knowledge Distillation

Evaluation on the Criteo dataset and the Twitter dataset given by the RecSys 2020 Challenge.

Results

Criteo Dataset

  • Split: first 6 days for training & last day for 50% validation and 50% test
  • Epochs: 50 with early stopping
  • Batch Size (Training): 2048
  • Intel Xeon E3-1231v3 & NVIDIA GTX 970
Model LogLoss AUC # Parameters
LR 0.4614 0.7899 1,086,811
FM 0.4555 0.7971 11,954,911
DeepFM 0.4475 0.8056 12,434,912
xDeepFM 0.4454 0.8078 15,508,958
Model 1 (CPU) 64 128 256 512 512 (GPU) 1024 2048 4096 Notes
LR 0.046 0.050 0.078 0.116 0.178 0.285 0.320 0.353 0.419
FM 0.398 0.640 0.770 1.138 2.018 2.130 2.319 2.498 2.604
DeepFM 1.062 20.000 38.186 72.644 142.776 4.215 3.806 4.646 5.914
xDeepFM 14.964 1267.092 2531.474 5011.780 10080.782 20.278 29.985 60.795 119.035
Embedding Quantization # Deep Nodes LogLoss AUC PRAUC RCE # Parameters Size (MB) Notes
- - (0,0,0) 0.4473 0.8058 0.6106 21.71 11,956,823 47.84
- - (400,400,400) 0.4446 0.8086 0.6159 22.18 12,436,824 49.780
QR - (400,400,400) 0.4452 0.8080 0.6150 22.08 7,008,374 28.073 2 collisions
QR - (400,400,400) 0.4460 0.8069 0.6139 21.93 4,294,354 17.217 4 collisions
QR - (400,400,400) 0.4496 0.8031 0.6076 21.31 1,771,504 7.125 60 collisions
- Dynamic (400,400,400) 0.4446 0.8086 0.6159 22.17 12,436,824 48.35
- Static (400,400,400) 0.4448 0.8085 0.6157 22.16 12,436,824 24.46
- QAT (400,400,400) 0.4459 0.8073 0.6135 21.94 12,436,824 24.46
- - (200,200,200) 0.4446 0.8086 0.6160 22.18 11,028,101 44.138 KD, a=0.1, t=3
- - (200,200,200) 0.4449 0.8083 0.6154 22.13 11,028,101 44.138 no KD
- - (100,100,100) 0.4450 0.8082 0.6152 22.11 10,928,101 43.736 KD, a=0.1, t=3
- - (100,100,100) 0.4453 0.8078 0.6146 22.06 10,928,101 43.736 no KD TODO

Embeddings - Latency (ms)

Embedding # Deep Nodes 1 (CPU) 64 128 256 512 512 (GPU) 1024 2048 4096 Notes
Embedding (0,0,0) 1.681 5.788 9.834 18.228 36.250 5.103 6.657 9.867 15.849
Embedding (400,400,400) 3.652 72.418 143.632 279.482 549.400 7.029 7.806 11.757 18.015
QR EmbeddingBag (400,400,400) 2.482 76.736 151.032 301.912 609.880 6.772 9.772 14.440 24.746 2 collisions TODO measure again
QR EmbeddingBag (400,400,400) 4.142 67.244 129.276 253.910 504.246 9.995 10.356 14.040 20.046 4 collisions
QR EmbeddingBag (400,400,400) 4.284 39.628 75.712 147.116 288.920 9.275 10.506 14.431 21.580 60 collisions

Quantization - Latency (ms)

Quantization 1 (CPU) 64 128 256 512
None 3.652 72.418 143.632 279.482 549.400
Dynamic 2.876 9.220 14.284 25.728 49.082
Static 5.062 10.139 16.034 27.146 49.352
QAT 5.457 11.141 16.404 26.892 46.676

Knowledge Distillation - Latency (ms)

Embedding # Deep Nodes 1 (CPU) 64 128 256 512 512 (GPU) 1024 2048 4096
EmbeddingBag (400,400,400) 3.652 72.418 143.632 279.482 549.400 7.029 7.806 11.757 18.015
EmbeddingBag (200,200,200) 0.618 10.626 18.752 37.428 73.060 2.489 2.803 3.175 3.886
EmbeddingBag (100,100,100) 0.536 2.812 4.818 8.454 16.022 2.520 2.817 3.094 3.325

Ensembles

Embedding Quantization # Deep Nodes LogLoss AUC PRAUC RCE # Parameters Size (MB) Notes
QR Embedding None (200,200,200) 0.4449 0.8083 0.6155 22.13 5,599,651 22.431 KD 2 coll
QR Embedding None (200,200,200) 0.4455 0.8076 0.6143 22.02 2,885,631 11.575 KD 4 coll
QR Embedding None (100,100,100) 0.4459 0.8072 0.6138 21.96 2,785,631 11.172 KD 4 coll
Embedding Static (200,200,200) 0.4448 0.8082 0.6159 22.15 11,028,101 19.781 KD
Embedding Static (100,100,100) 0.4452 0.8078 0.6151 22.07 10,928,101 19.671 KD
QR Embedding Static (200,200,200) 0.4451 0.8080 0.6153 22.09 5,599,651 21.947 KD 2 coll

Ensembles - Latency (ms)

Embedding Quantization # Deep Nodes 1 (CPU) 64 128 256 512 Notes
QR Embedding None (200,200,200) 1.486 10.458 19.296 37.062 72.264 KD 4 coll
QR Embedding None (100,100,100) 1.260 3.058 4.778 8.518 16.136 KD 4 coll
Embedding Static (200,200,200) 6.132 6.826 6.772 7.134 9.414 KD
Embedding Static (100,100,100) 5.432 5.726 6.120 6.290 7.240 KD
QR Embedding Static (200,200,200) 3.898 4.414 4.888 5.544 7.062 KD 2 coll

Twitter

  • Threshold: 15
  • Epochs: 50 with early stopping
  • Dropout: 0.2
Model LogLoss AUC PRAUC RCE # Parameters Size (MB) Notes
FwFM 0.3248 0.9368 0.9049 52.19 131,425,764 525.721 Like
FwFM 0.2202 0.8754 0.5223 31.35 131,425,764 525.721 Retweet
FwFM 0.0988 0.8463 0.1388 16.90 131,425,764 525.721 Reply
FwFM 0.0360 0.8231 0.0456 11.43 131,425,764 525.721 Retweet with comment
DeepFwFM 0.3147 0.9385 0.9077 53.67 131,937,765 527.786 Like
DeepFwFM 0.2210 0.8731 0.5121 31.08 131,937,765 527.786 Retweet
DeepFwFM 0.0965 0.8466 0.1372 18.82 131,937,765 527.786 Reply
DeepFwFM 0.0355 0.8183 0.0416 12.56 131,937,765 527.786 Retweet with comment
xsDeepFwFM 0.3226 0.9372 0.9046 52.51 59,917,741 239.705 Like
xsDeepFwFM 0.2200 0.8739 0.5155 31.42 59,917,741 239.705 Retweet
xsDeepFwFM 0.0961 0.8479 0.1424 19.11 59,917,741 239.705 Reply
xsDeepFwFM 0.0358 0.8124 0.0388 11.92 59,917,741 239.705 Retweet with comment
Model 1 (CPU) 64 128 256 512
FwFM 0.780 19.816 39.906 80.346 160.170
DeepFwFM 2.024 74.256 147.038 290.154 578.644
xsDeepFwFM 5.544 6.160 6.764 7.648 10.464

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Acceleration and Compression of DeepFwFM.

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acceleration compression criteo-dataset fwfm deepfwfm xsdeepfwfm

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