SCIENCE CHINA Information Sciences, Volume 63 , Issue 12 : 222103(2020) https://doi.org/10.1007/s11432-020-3097-4

## Jittor: a novel deep learning framework with meta-operators and unified graph execution

Shi-Min HU 1,2,*,
• AcceptedSep 23, 2020
• PublishedNov 13, 2020
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### References

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• Figure 1

Jittor in use.

• Figure 2

Building models from meta-operators. Operators from the three meta-operator classes, reindex, reindex-reduce, and element-wise, are fused to provide other common deep learning operators, which in turn are used to build the model.

• Table 1

Table 1Inferencing speed comparison (FPS) between Jittor and PyTorch$^{\rm~a)}$

 Model Batch size 1 2 4 8 16 32 64 128 PT JT PT JT PT JT PT JT PT JT PT JT PT JT PT JT ResNet50 [20] 185 220 353 357 492 548 575 667 643 773 668 810 680 829 692 836 ResNet152 [20] 64 86 126 132 209 231 251 287 273 321 283 335 288 346 296 354 Wide ResNet50_2 [23] 134 131 204 202 275 288 310 335 353 397 379 426 391 441 397 437 Wide ResNet101_2 [23] 73 72 111 112 162 169 180 194 203 225 220 243 228 254 230 253 ResNEXT50_32$\times$4d [25] 118 132 236 216 310 341 393 445 458 536 484 572 495 579 503 586 ResNEXT101_32$\times$8d [25] 50 53 78 81 123 136 149 162 166 185 178 198 183 204 185 205 Res2Net50 [26] 79 149 152 240 299 355 399 451 441 507 460 547 468 553 468 559 AlexNet [21] 865 818 1562 1500 2626 2622 3553 3745 5070 5546 6736 7431 6836 7531 6856 7551 VGG11 [22] 303 315 201 208 322 337 593 665 741 855 808 873 818 883 820 885 SqueezeNet1_1 [24] 404 842 769 1461 1619 2102 2656 2700 3035 3382 3258 3628 3406 3658 3407 3631

a) The boldface represents the faster framework for the same model and batch size.

• Table 2

Table 2Training speed comparison (number of iterations per second) of 4 GAN models

 WGAN-GP DCGAN LSGAN CycleGAN Dataset MNIST MNIST MNIST cityscapes PyTorch 52.35 37.73 38.61 10.08 Jittor 149.25 78.74 78.74 13.96 Jittor relative speed 2.9 2.1 2.0 1.4
• Table 3

Table 3Ablation studies about asynchronous interface, cross iteration fusion, unified memory and lazy execution

 FPS Speedup ratio PyTorch 253.1 0.965 Without asynchronous interface 254.9 0.972 Without cross iteration fusion 260.4 0.993 Without unified memory 264.7 1.009 Without lazy execution 73.2 0.279 All-features-on 262.2 1.000

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