FoConrad opened a new issue #10563: Suboptimal performance implementing PPO with Adam Optimizer URL: https://github.com/apache/incubator-mxnet/issues/10563 ## Description We noticed our gluon/MXNet [Proximal Policy Optimization](https://arxiv.org/abs/1707.06347) (PPO) implementation is under-performing compared to the OpenAI Baselines version in TensorFlow. Upon inspection it appears that this may be, in part, to the Adam optimizer in MXNet. This is seen by initializing both networks with the same parameters, and taking very much care that all the computation is equivalent (which can be seen by using another optimizer, aside from Adam, and noting the weights of the two networks progress with nearly the same values), and noting that the weights start to diverge significantly. The weight divergence only occurs on the Policy network (and occurs with or without the entropy term). ## Environment info (Required) Ubuntu 16.04 CPU: Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz MXNet version: 1.1.0 TensorFlow version: 1.4.1 Numpy version: 1.13.1 ``` 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: 63 Model name: Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz Stepping: 2 CPU MHz: 3490.156 CPU max MHz: 3600.0000 CPU min MHz: 1200.0000 BogoMIPS: 6984.53 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 10240K 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 dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm epb invpcid_single retpoline kaiser tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts ----------Python Info---------- Version : 3.5.2 Compiler : GCC 5.4.0 20160609 Build : ('default', 'Nov 23 2017 16:37:01') Arch : ('64bit', 'ELF') ------------Pip Info----------- Version : 9.0.3 Directory : /home/con/workspace/.../tenv/lib/python3.5/site-packages/pip ----------MXNet Info----------- Version : 1.1.0 Directory : /home/con/workspace/.../tenv/lib/python3.5/site-packages/mxnet Commit Hash : e29bb6f76365e45dd44e23941692c9d969959315 ----------System Info---------- Platform : Linux-4.4.0-116-generic-x86_64-with-Ubuntu-16.04-xenial system : Linux node : Conrad-Tower release : 4.4.0-116-generic version : #140-Ubuntu SMP Mon Feb 12 21:23:04 UTC 2018 ----------Hardware Info---------- machine : x86_64 processor : x86_64 ----------Network Test---------- Setting timeout: 10 Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0013 sec, LOAD: 0.4041 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0009 sec, LOAD: 0.0277 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0011 sec, LOAD: 0.0108 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0009 sec, LOAD: 0.0335 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0011 sec, LOAD: 0.3374 sec. Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0010 sec, LOAD: 0.5805 sec. ``` I'm using Python 3.5.2. ## Minimum reproducible example Provided is the script mnist.py (below). This file requires TensorFlow and MXNet. It uses the MNIST dataset to reproduce the weight divergence (when initialized to the exact same weight values) between a simple gluon network and an equivalent TensorFlow one. This example was carefully crafted so that the TensorFlow code contained is in essence equivalent to OpenAI baselines implementation of PPO (some non-contributing factors were whittled away for a more simple example). The code, understandably, may not actually be training a significant MNIST classifier. Instead, it is meant to mirror the PPO policy objective, and track the weight progression. Find the code [here.](https://gist.github.com/FoConrad/29a51cdfa58c51cdab4df8e902d10207) ## Steps to reproduce 1. ``python mnist.py # Shows weight divergence using Adam`` 2. ``python mnist.py --optimizer momentum # Shows weight divergence is insignificant with other optimizer`` ## What have you tried to solve it? 1. Using different optimizers: this solves the weight divergence, but performs worse than Adam. 2. Used simpler loss functions: this slows divergence, even with the Adam optimizer, possibly to the point where the divergence is insignificant. However, using a simpler loss function is not an option when implementing PPO. 3. Using the same initialization: in the debugging process we made sure both networks were initialized in the same fashion to ensure the difference in performance was not due to initialization. The minimal example also ensures that both networks are initialized the same. 4. Removed parts of the PPO network: we isolated the issue as coming from the policy network in PPO. The loss function, which contains an entropy term, seems to be significant also. When just using the entropy term, everything is fine. When using the policy surrogate loss (alone or with the entropy term), we start to see divergence in the weights. 5. Used a non-gluon implementation of softmax cross entropy loss: I calculate the softmax cross entropy loss in a few different ways, using more basic MXNet NDArray operations to ensure the problem wasn't with gluon.loss.SoftmaxCrossEntropyLoss (this was a suspicion as using a different loss (such as sigmoid for calculating the log probabilities) seems to mask the problem, in some cases). 6. Tried using GPU instead of CPU: we moved the MXNet network to the GPU to see if the different implementation would resolve the problem (it did not).
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