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From GitBox <...@apache.org>
Subject [GitHub] [singa] chrishkchris opened a new pull request #562: SINGA-487 Add support of gradient compression to half precision
Date Tue, 19 Nov 2019 10:23:36 GMT
chrishkchris opened a new pull request #562: SINGA-487 Add support of gradient compression
to half precision
URL: https://github.com/apache/singa/pull/562
 
 
   In this PR, I add an API in opt.py for using half precision in gradient transfer.
   
   Here is the training accurate test using 16 bit for gradient transfer:
   ubuntu@ip-172-31-29-33:~/singa/examples/autograd$ /home/ubuntu/mpich-3.3/build/bin/mpiexec
--hostfile host_file python3 mnist_dist.py
   Starting Epoch 0:
   Training loss = 790.405762, training accuracy = 0.715144
   Evaluation accuracy = 0.928557, Elapsed Time = 0.675930s
   Starting Epoch 1:
   Training loss = 252.329041, training accuracy = 0.915181
   Evaluation accuracy = 0.961143, Elapsed Time = 0.545467s
   Starting Epoch 2:
   Training loss = 181.895905, training accuracy = 0.938618
   Evaluation accuracy = 0.965461, Elapsed Time = 0.554351s
   Starting Epoch 3:
   Training loss = 136.416214, training accuracy = 0.954577
   Evaluation accuracy = 0.970806, Elapsed Time = 0.542592s
   Starting Epoch 4:
   Training loss = 117.712143, training accuracy = 0.960804
   Evaluation accuracy = 0.976460, Elapsed Time = 0.543181s
   Starting Epoch 5:
   Training loss = 102.698730, training accuracy = 0.965562
   Evaluation accuracy = 0.976974, Elapsed Time = 0.541852s
   Starting Epoch 6:
   Training loss = 93.638481, training accuracy = 0.969401
   Evaluation accuracy = 0.978207, Elapsed Time = 0.543727s
   Starting Epoch 7:
   Training loss = 88.651802, training accuracy = 0.970536
   Evaluation accuracy = 0.975123, Elapsed Time = 0.541136s
   Starting Epoch 8:
   Training loss = 80.523178, training accuracy = 0.973508
   Evaluation accuracy = 0.983244, Elapsed Time = 0.544187s
   Starting Epoch 9:
   Training loss = 76.868576, training accuracy = 0.974209
   Evaluation accuracy = 0.982113, Elapsed Time = 0.544531s
   
   There seems to be no different of training accuracy in mnist dataset. But for other more
complex network/dataset I added an option of gradient clipping to assist training.

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