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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/12/27 07:46:16 UTC

[GitHub] [incubator-mxnet] liuzh91 commented on issue #17086: [MKLDNN] RNN Op gradient computation is broken

liuzh91 commented on issue #17086: [MKLDNN] RNN Op gradient computation is broken
URL: https://github.com/apache/incubator-mxnet/issues/17086#issuecomment-569213451
 
 
   > Hi, @liuzh91 @szhengac. We have posted #17183 to fix the gradient explosion issue in RNN Backward. Thanks for reporting this issue again. And it would be greatly appreciated if you could give a test on this patch. Thanks.
   > 
   > BTW, we got the below training log:
   > 
   > ```
   > ❯ python word_language_model.py --log-interval=1
   > /path/to/mxnet/python/mxnet/optimizer/optimizer.py:167: UserWarning: WARNING: New optimizer gluonnlp.optimizer.lamb.LAMB is overriding existing optimizer mxnet.optimizer.optimizer.LAMB
   >   Optimizer.opt_registry[name].__name__))
   > Namespace(alpha=2, batch_size=80, beta=1, bptt=70, clip=0.25, dropout=0.4, dropout_e=0.1, dropout_h=0.2, dropout_i=0.65, emsize=400, epochs=750, eval_only=False, gpu=None, log_interval=1, lr=30, lr_update_factor=0.1, lr_update_interval=30, model='lstm', nhid=1150, nlayers=3, ntasgd=False, optimizer='sgd', save='model.params', test_mode=False, tied=False, wd=1.2e-06, weight_dropout=0.5)
   > Use AWDRNN
   > AWDRNN(
   >   (embedding): HybridSequential(
   >     (0): Embedding(33278 -> 400, float32)
   >     (1): Dropout(p = 0.65, axes=(0,))
   >   )
   >   (encoder): HybridSequential(
   >     (0): LSTM(400 -> 1150, TNC)
   >     (1): LSTM(1150 -> 1150, TNC)
   >     (2): LSTM(1150 -> 1150, TNC)
   >   )
   >   (decoder): HybridSequential(
   >     (0): Dense(None -> 33278, linear)
   >   )
   > )
   > [Epoch 0 Batch 1/372] current loss 20.50, ppl 796977445.38, throughput 18.37 samples/s, lr 30.86
   > [Epoch 0 Batch 2/372] current loss 9.51, ppl 13511.50, throughput 39.56 samples/s, lr 28.29
   > [Epoch 0 Batch 3/372] current loss 17.53, ppl 41003388.51, throughput 40.65 samples/s, lr 27.43
   > [Epoch 0 Batch 4/372] current loss 9.45, ppl 12761.47, throughput 40.39 samples/s, lr 27.43
   > [Epoch 0 Batch 5/372] current loss 14.34, ppl 1695623.66, throughput 35.59 samples/s, lr 31.71
   > [Epoch 0 Batch 6/372] current loss 9.40, ppl 12113.46, throughput 35.10 samples/s, lr 32.14
   > [Epoch 0 Batch 7/372] current loss 8.56, ppl 5232.00, throughput 37.62 samples/s, lr 30.00
   > [Epoch 0 Batch 8/372] current loss 9.32, ppl 11163.67, throughput 42.00 samples/s, lr 26.57
   > [Epoch 0 Batch 9/372] current loss 8.44, ppl 4642.37, throughput 61.95 samples/s, lr 17.14
   > [Epoch 0 Batch 10/372] current loss 8.92, ppl 7494.76, throughput 41.39 samples/s, lr 27.00
   > ```
   
   Thank you for the commit. We will double check whether the bug still persists.

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