liuzh91 opened a new issue #17086: [MKLDNN] Gradient computation is broken for source built MXNET URL: https://github.com/apache/incubator-mxnet/issues/17086 ## Description Gradient computation on CPU is broken for the source built mxnet. I was running a language model training script on my ec2 instance. I test the script with the latest source built mxnet. During training, I ran into the following log: ### Log Message ``` ubuntu@ip-172-31-23-26:~/bug_fix/gluon-nlp/scripts/language_model$ python word_language_model.py --log-interval=1 /home/ubuntu/clean_mxnet/incubator-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) ) ) word_language_model.py:382: UserWarning: nan or inf is detected. Clipping results will be undefined. gluon.utils.clip_global_norm(grads, args.clip) [Epoch 0 Batch 1/372] current loss 20.83, ppl 1107330721.37, throughput 0.71 samples/s, lr 29.14 [Epoch 0 Batch 2/372] current loss 10.41, ppl 33276.90, throughput 1.39 samples/s, lr 29.57 [Epoch 0 Batch 3/372] current loss 10.41, ppl 33276.58, throughput 1.42 samples/s, lr 28.71 [Epoch 0 Batch 4/372] current loss 10.41, ppl 33276.17, throughput 1.33 samples/s, lr 30.43 [Epoch 0 Batch 5/372] current loss 10.41, ppl 33276.86, throughput 1.40 samples/s, lr 29.14 ``` The loss value simply not change any more. If I use the mxnet build by installing `pip install https://apache-mxnet.s3-us-west-2.amazonaws.com/dist/2019-12-15/dist/mxnet_cu100-1.6.0b20191215-py2.py3-none-manylinux1_x86_64.whl` . The log is normal because the loss keeps changing: ``` python word_language_model.py --log-interval=1 /home/ubuntu/anaconda3/lib/python3.6/site-packages/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 796093229.98, throughput 2.13 samples/s, lr 29.14 [Epoch 0 Batch 2/372] current loss 9.57, ppl 14283.55, throughput 4.20 samples/s, lr 29.57 [Epoch 0 Batch 3/372] current loss 17.85, ppl 56261658.19, throughput 4.32 samples/s, lr 28.71 [Epoch 0 Batch 4/372] current loss 9.50, ppl 13370.27, throughput 4.08 samples/s, lr 30.43 [Epoch 0 Batch 5/372] current loss 14.46, ppl 1903888.17, throughput 4.26 samples/s, lr 29.14 ``` ## To Reproduce The training script can be found at `https://github.com/dmlc/gluon-nlp/blob/master/scripts/language_model/word_language_model.py`. To reproduce the log message, I simply ran the script with the following command: `python word_language_model.py --log-interval=1` ## What have you tried to solve it? The problem occurred when computing gradients (https://github.com/dmlc/gluon-nlp/blob/master/scripts/language_model/word_language_model.py#L381) Some gradient values are of order `10^34`. Normally the gradient value should be within `[-10, 10]`. Thanks to @leezu , he found the error was introduced because of the MKLDNN. If we use mxnet built from source with MKLDNN on, i.e., `-DUSE_MKLDNN=ON`, the gradient error appears whereas the problem is gone when `MKLDNN=OFF`. Therefore the issue is introduced by the MKLDNN. @zixuanweeei @ciyongch @pengzhao-intel ## Environment My environment specs can be found here ``` ----------Python Info---------- Version : 3.6.6 Compiler : GCC 7.2.0 Build : ('default', 'Jun 28 2018 17:14:51') Arch : ('64bit', '') ------------Pip Info----------- Version : 19.2.3 Directory : /home/ubuntu/anaconda3/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.6.0 Directory : /home/ubuntu/clean_mxnet/incubator-mxnet/python/mxnet Num GPUs : 0 Hashtag not found. Not installed from pre-built package. ----------System Info---------- Platform : Linux-4.15.0-1056-aws-x86_64-with-debian-buster-sid system : Linux node : ip-172-31-23-26 release : 4.15.0-1056-aws version : #58-Ubuntu SMP Tue Nov 26 15:14:34 UTC 2019 ----------Hardware Info---------- machine : x86_64 processor : x86_64 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz Stepping: 1 CPU MHz: 2702.241 CPU max MHz: 3000.0000 CPU min MHz: 1200.0000 BogoMIPS: 4600.12 Hypervisor vendor: Xen Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 46080K NUMA node0 CPU(s): 0-7 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ```
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