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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/31 12:59:16 UTC

[GitHub] CoinCheung opened a new issue #9267: compiling c++ api error

CoinCheung opened a new issue #9267: compiling c++ api error
URL: https://github.com/apache/incubator-mxnet/issues/9267
 
 
   
   ## Description
   I try to compile the c++ api from the source code and met some error about cuda
   
   ## Environment info (Required)
   
   ```
   ----------Python Info----------
   Version      : 3.6.3
   Compiler     : GCC 7.2.0
   Build        : ('default', 'Oct 24 2017 14:48:20')
   Arch         : ('64bit', '')
   ------------Pip Info-----------
   Version      : 9.0.1
   Directory    : /usr/lib/python3.6/site-packages/pip
   ----------MXNet Info-----------
   No MXNet installed.
   ----------System Info----------
   Platform     : Linux-4.13.12-1-ARCH-x86_64-with-arch
   system       : Linux
   node         : Arch-R720
   release      : 4.13.12-1-ARCH
   version      : #1 SMP PREEMPT Wed Nov 8 11:54:06 CET 2017
   ----------Hardware Info----------
   machine      : x86_64
   processor    : 
   Architecture:        x86_64
   CPU op-mode(s):      32-bit, 64-bit
   Byte Order:          Little Endian
   CPU(s):              4
   On-line CPU(s) list: 0-3
   Thread(s) per core:  1
   Core(s) per socket:  4
   Socket(s):           1
   NUMA node(s):        1
   Vendor ID:           GenuineIntel
   CPU family:          6
   Model:               158
   Model name:          Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
   Stepping:            9
   CPU MHz:             3208.493
   CPU max MHz:         3500.0000
   CPU min MHz:         800.0000
   BogoMIPS:            4993.00
   Virtualization:      VT-x
   L1d cache:           32K
   L1i cache:           32K
   L2 cache:            256K
   L3 cache:            6144K
   NUMA node0 CPU(s):   0-3
   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 art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault intel_pt tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0247 sec, LOAD: 3.3866 sec.
   Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0097 sec, LOAD: 1.3392 sec.
   Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0586 sec, LOAD: 11.4271 sec.
   Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0092 sec, LOAD: 1.6135 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0200 sec, LOAD: 1.1366 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0750 sec, LOAD: 0.8653 sec.
   
   ```
   
   Package used (Python/R/Scala/Julia):
   (I'm using C++.)
   
   
   
   ## Build info (Required if built from source)
   
   Compiler (gcc/clang/mingw/visual studio): gcc
   
   MXNet commit hash:
   e4128ec76f919db46d5f401b411283cab0635e29
   
   
   Build config:
   #-------------------------------------------------------------------------------
   #  Template configuration for compiling mxnet
   #
   #  If you want to change the configuration, please use the following
   #  steps. Assume you are on the root directory of mxnet. First copy the this
   #  file so that any local changes will be ignored by git
   #
   #  $ cp make/config.mk .
   #
   #  Next modify the according entries, and then compile by
   #
   #  $ make
   #
   #  or build in parallel with 8 threads
   #
   #  $ make -j8
   #-------------------------------------------------------------------------------
   
   #---------------------
   # choice of compiler
   #--------------------
   
   export CC = gcc
   export CXX = g++
   export NVCC = nvcc
   
   # whether compile with options for MXNet developer
   DEV = 0
   
   # whether compile with debug
   DEBUG = 0
   
   # whether compile with profiler
   USE_PROFILER =
   
   # whether to turn on segfault signal handler to log the stack trace
   USE_SIGNAL_HANDLER =
   
   # the additional link flags you want to add
   ADD_LDFLAGS =
   
   # the additional compile flags you want to add
   ADD_CFLAGS =
   
   #---------------------------------------------
   # matrix computation libraries for CPU/GPU
   #---------------------------------------------
   
   # whether use CUDA during compile
   USE_CUDA = 1
   
   # add the path to CUDA library to link and compile flag
   # if you have already add them to environment variable, leave it as NONE
   # USE_CUDA_PATH = /usr/local/cuda
   USE_CUDA_PATH = /opt/cuda
   
   # whether use CuDNN R3 library
   USE_CUDNN = 1
   
   #whether to use NCCL library
   USE_NCCL = 0
   #add the path to NCCL library
   USE_NCCL_PATH = NONE
   
   # whether use opencv during compilation
   # you can disable it, however, you will not able to use
   # imbin iterator
   USE_OPENCV = 1
   
   #whether use libjpeg-turbo for image decode without OpenCV wrapper
   USE_LIBJPEG_TURBO = 0
   #add the path to libjpeg-turbo library
   USE_LIBJPEG_TURBO_PATH = NONE
   
   # use openmp for parallelization
   USE_OPENMP = 1
   
   # MKL ML Library for Intel CPU/Xeon Phi
   # Please refer to MKL_README.md for details
   
   # MKL ML Library folder, need to be root for /usr/local
   # Change to User Home directory for standard user
   # For USE_BLAS!=mkl only
   MKLML_ROOT=/usr/local
   
   # whether use MKL2017 library
   USE_MKL2017 = 0
   
   # whether use MKL2017 experimental feature for high performance
   # Prerequisite USE_MKL2017=1
   USE_MKL2017_EXPERIMENTAL = 0
   
   # whether use NNPACK library
   USE_NNPACK = 0
   
   # choose the version of blas you want to use
   # can be: mkl, blas, atlas, openblas
   # in default use atlas for linux while apple for osx
   UNAME_S := $(shell uname -s)
   ifeq ($(UNAME_S), Darwin)
   USE_BLAS = apple
   else
   USE_BLAS = openblas
   endif
   
   # whether use lapack during compilation
   # only effective when compiled with blas versions openblas/apple/atlas/mkl
   USE_LAPACK = 1
   
   # path to lapack library in case of a non-standard installation
   USE_LAPACK_PATH =
   
   # by default, disable lapack when using MKL
   # switch on when there is a full installation of MKL available (not just MKL2017/MKL_ML)
   ifeq ($(USE_BLAS), mkl)
   USE_LAPACK = 0
   endif
   
   # add path to intel library, you may need it for MKL, if you did not add the path
   # to environment variable
   USE_INTEL_PATH = NONE
   
   # If use MKL only for BLAS, choose static link automatically to allow python wrapper
   ifeq ($(USE_MKL2017), 0)
   ifeq ($(USE_BLAS), mkl)
   USE_STATIC_MKL = 1
   endif
   else
   USE_STATIC_MKL = NONE
   endif
   
   #----------------------------
   # Settings for power and arm arch
   #----------------------------
   ARCH := $(shell uname -a)
   ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
   	USE_SSE=0
   else
   	USE_SSE=1
   endif
   
   #----------------------------
   # distributed computing
   #----------------------------
   
   # whether or not to enable multi-machine supporting
   USE_DIST_KVSTORE = 0
   
   # whether or not allow to read and write HDFS directly. If yes, then hadoop is
   # required
   USE_HDFS = 0
   
   # path to libjvm.so. required if USE_HDFS=1
   LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
   
   # whether or not allow to read and write AWS S3 directly. If yes, then
   # libcurl4-openssl-dev is required, it can be installed on Ubuntu by
   # sudo apt-get install -y libcurl4-openssl-dev
   USE_S3 = 0
   
   #----------------------------
   # performance settings
   #----------------------------
   # Use operator tuning
   USE_OPERATOR_TUNING = 1
   
   # Use gperftools if found
   USE_GPERFTOOLS = 1
   
   # Use JEMalloc if found, and not using gperftools
   USE_JEMALLOC = 1
   
   #----------------------------
   # additional operators
   #----------------------------
   
   # path to folders containing projects specific operators that you don't want to put in src/operators
   EXTRA_OPERATORS =
   
   #----------------------------
   # other features
   #----------------------------
   
   # Create C++ interface package
   USE_CPP_PACKAGE = 1
   
   #----------------------------
   # plugins
   #----------------------------
   
   # whether to use caffe integration. This requires installing caffe.
   # You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
   # CAFFE_PATH = $(HOME)/caffe
   # MXNET_PLUGINS += plugin/caffe/caffe.mk
   
   # WARPCTC_PATH = $(HOME)/warp-ctc
   # MXNET_PLUGINS += plugin/warpctc/warpctc.mk
   
   # whether to use sframe integration. This requires build sframe
   # git@github.com:dato-code/SFrame.git
   # SFRAME_PATH = $(HOME)/SFrame
   # MXNET_PLUGINS += plugin/sframe/plugin.mk
   
   
   ## Error Message:
   Running CUDA_ARCH: -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=[sm_70,compute_70] --fatbin-options -compress-all
   g++ -std=c++11 -c -DMSHADOW_FORCE_STREAM -Wall -Wsign-compare -O3 -DNDEBUG=1 -I/home/coin/build/incubator-mxnet/mshadow/ -I/home/coin/build/incubator-mxnet/dmlc-core/include -fPIC -I/home/coin/build/incubator-mxnet/nnvm/include -I/home/coin/build/incubator-mxnet/dlpack/include -Iinclude -funroll-loops -Wno-unused-variable -Wno-unused-parameter -Wno-unknown-pragmas -Wno-unused-local-typedefs -msse3 -I/opt/cuda/include -DMSHADOW_USE_CBLAS=1 -DMSHADOW_USE_MKL=0 -DMSHADOW_RABIT_PS=0 -DMSHADOW_DIST_PS=0 -DMSHADOW_USE_PASCAL=0 -DMXNET_USE_OPENCV=1 -I/usr/include/opencv -fopenmp -DMXNET_USE_OPERATOR_TUNING=1 -DMXNET_USE_LAPACK -DMSHADOW_USE_CUDNN=1 -fno-builtin-malloc -fno-builtin-calloc -fno-builtin-realloc -fno-builtin-free  -I/home/coin/build/incubator-mxnet/3rdparty/cub -DMXNET_USE_NCCL=0 -DMXNET_USE_LIBJPEG_TURBO=0 -MMD -c src/operator/nn/cudnn/cudnn_algoreg.cc -o build/src/operator/nn/cudnn/cudnn_algoreg.o
   In file included from include/mxnet/./resource.h:31:0,
                    from include/mxnet/operator.h:39,
                    from src/operator/nn/cudnn/./../convolution-inl.h:33,
                    from src/operator/nn/cudnn/./cudnn_algoreg-inl.h:34,
                    from src/operator/nn/cudnn/cudnn_algoreg.cc:26:
   include/mxnet/./../../src/common/random_generator.h: In static member function static void mxnet::common::random::RandGenerator<mshadow::gpu, DType>::AllocState(mxnet::common::random::RandGenerator<mshadow::gpu, DType>*)?:
   include/mxnet/./../../src/common/random_generator.h:156:5: error: there are no arguments to ?CUDA_CALL? that depend on a template parameter, so a declaration of ?CUDA_CALL? must be available [-fpermissive]
        CUDA_CALL(cudaMalloc(&inst->states_,
        ^~~~~~~~~
   include/mxnet/./../../src/common/random_generator.h:156:5: note: (if you use ?-fpermissive?, G++ will accept your code, but allowing the use of an undeclared name is deprecated)
   include/mxnet/./../../src/common/random_generator.h: In static member function static void mxnet::common::random::RandGenerator<mshadow::gpu, DType>::FreeState(mxnet::common::random::RandGenerator<mshadow::gpu, DType>*)?:
   include/mxnet/./../../src/common/random_generator.h:161:5: error: there are no arguments to ?CUDA_CALL? that depend on a template parameter, so a declaration of ?CUDA_CALL? must be available [-fpermissive]
        CUDA_CALL(cudaFree(inst->states_));
        ^~~~~~~~~
   make: *** [Makefile:373: build/src/operator/nn/cudnn/cudnn_algoreg.o] Error 1
   
   
   ## Minimum reproducible example
   (If you are using your own code, please provide a short script that reproduces the error. Otherwise, please provide link to the existing example.)
   
   ## Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1. make clean
   2. make
   
   ## What have you tried to solve it?
   
   1.
   2.
   

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