wonghang opened a new issue #8658: mxnet random seed does not work for mx.init.Xavier on both CPU and GPU URL: https://github.com/apache/incubator-mxnet/issues/8658 ## Description mx.random.seed does not work for mx.init.Xavier on both CPU and GPU ## Environment info (Required) ``` ----------Python Info---------- Version : 3.5.2 Compiler : GCC 5.4.0 20160609 Build : ('default', 'Sep 14 2017 22:51:06') Arch : ('64bit', 'ELF') ------------Pip Info----------- Version : 8.1.1 Directory : /usr/lib/python3/dist-packages/pip ----------MXNet Info----------- Version : 0.12.0 Directory : /usr/local/lib/python3.5/dist-packages/mxnet-0.12.0-py3.5.egg/mxnet Hashtag not found. Not installed from pre-built package. ----------System Info---------- Platform : Linux-4.4.0-100-generic-x86_64-with-Ubuntu-16.04-xenial system : Linux node : cpce-dell release : 4.4.0-100-generic version : #123-Ubuntu SMP Thu Nov 2 10:16:13 UTC 2017 ----------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: 58 Model name: Intel(R) Core(TM) i7-3770 CPU @ 3.40GHz Stepping: 9 CPU MHz: 3701.882 CPU max MHz: 3900.0000 CPU min MHz: 1600.0000 BogoMIPS: 6784.34 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 8192K 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 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 cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm epb tpr_shadow vnmi flexpriority ept vpid fsgsbase smep erms xsaveopt dtherm ida arat pln pts ----------Network Test---------- Setting timeout: 10 Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0030 sec, LOAD: 0.0555 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0010 sec, LOAD: 0.7034 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0013 sec, LOAD: 0.2217 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0012 sec, LOAD: 0.6687 sec. Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0010 sec, LOAD: 1.3287 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0042 sec, LOAD: 0.0200 sec. ``` Package used (Python/R/Scala/Julia): (1) I am using numpy 1.13.3 (2) CUDA 9.0 (3) cuDNN 5.0.3 (4) OpenBLAS 0.2.10 (5) mxnet 0.12 from github `git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet --branch 0.12.0 ` ## Build info (Required if built from source) mxnet build with cuda, cudnn, openblas, openmp disabled, opencv disabled Compiler (gcc/clang/mingw/visual studio): ``` Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/5/lto-wrapper Target: x86_64-linux-gnu Configured with: ../src/configure -v --with-pkgversion='Ubuntu 5.4.0-6ubuntu1~16.04.5' --with-bugurl=file:///usr/share/doc/gcc-5/README.Bugs --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --prefix=/usr --program-suffix=-5 --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --with-sysroot=/ --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-vtable-verify --enable-libmpx --enable-plugin --with-system-zlib --disable-browser-plugin --enable-java-awt=gtk --enable-gtk-cairo --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-5-amd64/jre --enable-java-home --with-jvm-root-dir=/usr/lib/jvm/java-1.5.0-gcj-5-amd64 --with-jvm-jar-dir=/usr/lib/jvm-exports/java-1.5.0-gcj-5-amd64 --with-arch-directory=amd64 --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --enable-objc-gc --enable-multiarch --disable-werror --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --enable-multilib --with-tune=generic --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu Thread model: posix gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.5) ``` MXNet commit hash: 4f2af2d2e5216ab3a1faadcc117709b6836029dc Build config: ``` USE_CUDA=1 USE_OPENBLAS=openblas USE_OPENCV=0 USE_CUDNN=1 USE_OPENMP=0 USE_GPERFTOOLS = 1 USE_JEMALLOC = 1 ``` ## Minimum reproducible example ``` #!/usr/bin/python3 import mxnet as mx import numpy as np for device in ['cpu','gpu']: with mx.Context(device): np.random.seed(0) mx.random.seed(0) x = mx.sym.Variable('x') L1 = mx.sym.FullyConnected(data=x,num_hidden=100,flatten=False) L2 = mx.sym.FullyConnected(data=L1,num_hidden=100,flatten=False) y = mx.sym.FullyConnected(data=L2,num_hidden=1,flatten=False) mod = mx.mod.Module(y,data_names=["x"],label_names=None) mod.bind(data_shapes=[("x",(1,1))]) mod.init_params(initializer=mx.init.Xavier(rnd_type='gaussian')) #mod.init_params(initializer=mx.init.One()) one = mx.io.DataBatch(data=[ mx.nd.array(np.random.rand(1).reshape(1,1)) ]) mod.forward(one) output = mod.get_outputs()[0] output = output.asnumpy() print("[%s] Random from numpy=%g, from mxnet=%g" % (device,np.random.rand(),output.flatten()[0])) ``` ## Steps to reproduce (Paste the commands you ran that produced the error.) 1. python3 (the above script for several time), the output would be: ``` $ python3 test_random.py [cpu] Random from numpy=0.715189, from mxnet=0.216065 [gpu] Random from numpy=0.715189, from mxnet=0.214543 $ python3 test_random.py [cpu] Random from numpy=0.715189, from mxnet=-0.320229 [gpu] Random from numpy=0.715189, from mxnet=0.163189 $ python3 test_random.py [cpu] Random from numpy=0.715189, from mxnet=-0.320229 [gpu] Random from numpy=0.715189, from mxnet=-0.192892 $ ``` ## What have you tried to solve it? Do not use mod.init_params(initializer=mx.init.Xavier(rnd_type='gaussian')), but using mod.init_params(initializer=mx.init.One()) then the result is deterministic. But I need xavier..
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