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linly / src /face3d /models /arcface_torch /docs /speed_benchmark.md
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Test Training Speed

  • Test Commands

You need to use the following two commands to test the Partial FC training performance. The number of identites is 3 millions (synthetic data), turn mixed precision training on, backbone is resnet50, batch size is 1024.

# Model Parallel
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions
# Partial FC 0.1
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions_pfc
  • GPU Memory
# (Model Parallel) gpustat -i
[0] Tesla V100-SXM2-32GB | 64'C,  94 % | 30338 / 32510 MB 
[1] Tesla V100-SXM2-32GB | 60'C,  99 % | 28876 / 32510 MB 
[2] Tesla V100-SXM2-32GB | 60'C,  99 % | 28872 / 32510 MB 
[3] Tesla V100-SXM2-32GB | 69'C,  99 % | 28872 / 32510 MB 
[4] Tesla V100-SXM2-32GB | 66'C,  99 % | 28888 / 32510 MB 
[5] Tesla V100-SXM2-32GB | 60'C,  99 % | 28932 / 32510 MB 
[6] Tesla V100-SXM2-32GB | 68'C, 100 % | 28916 / 32510 MB 
[7] Tesla V100-SXM2-32GB | 65'C,  99 % | 28860 / 32510 MB 

# (Partial FC 0.1) gpustat -i
[0] Tesla V100-SXM2-32GB | 60'C,  95 % | 10488 / 32510 MB                                                                                                                                          │·······················
[1] Tesla V100-SXM2-32GB | 60'C,  97 % | 10344 / 32510 MB                                                                                                                                          │·······················
[2] Tesla V100-SXM2-32GB | 61'C,  95 % | 10340 / 32510 MB                                                                                                                                          │·······················
[3] Tesla V100-SXM2-32GB | 66'C,  95 % | 10340 / 32510 MB                                                                                                                                          │·······················
[4] Tesla V100-SXM2-32GB | 65'C,  94 % | 10356 / 32510 MB                                                                                                                                          │·······················
[5] Tesla V100-SXM2-32GB | 61'C,  95 % | 10400 / 32510 MB                                                                                                                                          │·······················
[6] Tesla V100-SXM2-32GB | 68'C,  96 % | 10384 / 32510 MB                                                                                                                                          │·······················
[7] Tesla V100-SXM2-32GB | 64'C,  95 % | 10328 / 32510 MB                                                                                                                                        │·······················
  • Training Speed
# (Model Parallel) trainging.log
Training: Speed 2271.33 samples/sec   Loss 1.1624   LearningRate 0.2000   Epoch: 0   Global Step: 100 
Training: Speed 2269.94 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 150 
Training: Speed 2272.67 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 200 
Training: Speed 2266.55 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 250 
Training: Speed 2272.54 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 300 

# (Partial FC 0.1) trainging.log
Training: Speed 5299.56 samples/sec   Loss 1.0965   LearningRate 0.2000   Epoch: 0   Global Step: 100  
Training: Speed 5296.37 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 150  
Training: Speed 5304.37 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 200  
Training: Speed 5274.43 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 250  
Training: Speed 5300.10 samples/sec   Loss 0.0000   LearningRate 0.2000   Epoch: 0   Global Step: 300

In this test case, Partial FC 0.1 only use1 1/3 of the GPU memory of the model parallel, and the training speed is 2.5 times faster than the model parallel.

Speed Benchmark

  1. Training speed of different parallel methods (samples/second), Tesla V100 32GB * 8. (Larger is better)
Number of Identities in Dataset Data Parallel Model Parallel Partial FC 0.1
125000 4681 4824 5004
250000 4047 4521 4976
500000 3087 4013 4900
1000000 2090 3449 4803
1400000 1672 3043 4738
2000000 - 2593 4626
4000000 - 1748 4208
5500000 - 1389 3975
8000000 - - 3565
16000000 - - 2679
29000000 - - 1855
  1. GPU memory cost of different parallel methods (GB per GPU), Tesla V100 32GB * 8. (Smaller is better)
Number of Identities in Dataset Data Parallel Model Parallel Partial FC 0.1
125000 7358 5306 4868
250000 9940 5826 5004
500000 14220 7114 5202
1000000 23708 9966 5620
1400000 32252 11178 6056
2000000 - 13978 6472
4000000 - 23238 8284
5500000 - 32188 9854
8000000 - - 12310
16000000 - - 19950
29000000 - - 32324