Optimizer issue in CPP Package

[chsin]

Description

Can't have more than one optimizer object in CPP Package. Fixed in pull request: #9809

Environment info (Required)

I didn't install the python package for this build, but this is the result from previous builds and should basically be the same.

----------Python Info----------
('Version      :', '2.7.13')
('Compiler     :', 'GCC 4.4.7 20120313 (Red Hat 4.4.7-18)')
('Build        :', ('default', 'Aug 11 2017 20:36:55'))
('Arch         :', ('64bit', 'ELF'))
------------Pip Info-----------
('Version      :', '9.0.1')
('Directory    :', '/scratch/cahsin/pythondir27/lib/python2.7/site-packages/pip')
----------MXNet Info-----------
('Version      :', '1.0.0')
('Directory    :', '/scratch/cahsin/repo/mxnet/python/mxnet')
Hashtag not found. Not installed from pre-built package.
----------System Info----------
('Platform     :', 'Linux-4.1.12-61.1.16.el6uek.x86_64-x86_64-with-redhat-6.9-Santiago')
('system       :', 'Linux')
('node         :', 'den02kge')
('release      :', '4.1.12-61.1.16.el6uek.x86_64')
('version      :', '#2 SMP Fri Oct 21 14:23:10 PDT 2016')
----------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):                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:                 63
Model name:            Intel(R) Xeon(R) CPU E5-2699 v3 @ 2.30GHz
Stepping:              2
CPU MHz:               2294.908
BogoMIPS:              4589.81
Hypervisor vendor:     Xen
Virtualization type:   full
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              46080K
NUMA node0 CPU(s):     0-3
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0050 sec, LOAD: 0.8736 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0001 sec, LOAD: 0.3412 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0000 sec, LOAD: 0.8275 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0012 sec, LOAD: 0.2533 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1606 sec, LOAD: 0.3929 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1115 sec, LOAD: 0.3883 sec.

Build info (Required if built from source)

Compiler (gcc/clang/mingw/visual studio):

bash-4.1$ gcc --version
gcc (GCC) 4.8.2 20140120 (Red Hat 4.8.2-15)
Copyright (C) 2013 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

MXNet commit hash:
(Paste the output of git rev-parse HEAD here.)
2700ddb

Build config:

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

#-------------------------------------------------------------------------------
#  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 = 1

# 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 = -L/scratch/cahsin/compilers_and_libraries_2017.4.196/linux/mkl/lib

# the additional compile flags you want to add
ADD_CFLAGS = -L/scratch/cahsin/compilers_and_libraries_2017.4.196/linux/mkl/include

#---------------------------------------------
# matrix computation libraries for CPU/GPU
#---------------------------------------------

# whether use CUDA during compile
USE_CUDA = 0

# 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 = NONE

# whether to enable CUDA runtime compilation
ENABLE_CUDA_RTC = 1

# whether use CuDNN R3 library
USE_CUDNN = 0

#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 = atlas
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 =

# add path to intel library, you may need it for MKL, if you did not add the path
# to environment variable
USE_INTEL_PATH = /scratch/cahsin/compilers_and_libraries_2017.4.196/linux

# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
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:

bash-4.1$ ./mlp_cpu 
[14:20:07] src/io/iter_mnist.cc:110: MNISTIter: load 60000 images, shuffle=1, shape=(100,784)
[14:20:08] src/io/iter_mnist.cc:110: MNISTIter: load 10000 images, shuffle=1, shape=(100,784)
[14:20:11] mlp_cpu.cpp:134: Epoch: 0 19966.7 samples/sec Accuracy: 0.1135
[14:20:14] mlp_cpu.cpp:134: Epoch: 1 19756.3 samples/sec Accuracy: 0.6003
[14:20:18] mlp_cpu.cpp:134: Epoch: 2 19607.8 samples/sec Accuracy: 0.8247
[14:20:22] src/io/iter_mnist.cc:110: MNISTIter: load 60000 images, shuffle=1, shape=(100,784)
[14:20:23] src/io/iter_mnist.cc:110: MNISTIter: load 10000 images, shuffle=1, shape=(100,784)
terminate called after throwing an instance of 'dmlc::Error'
  what():  [14:20:23] /scratch/cahsin/repo/mxnet_master/cpp-package/include/mxnet-cpp/optimizer.hpp:141: Check failed: cmap().count(name) == 0 (1 vs. 0) sgd already registered

Stack trace returned 8 entries:
[bt] (0) ./mlp_cpu(_ZN4dmlc10StackTraceEv+0x42) [0x418f56]
[bt] (1) ./mlp_cpu(_ZN4dmlc15LogMessageFatalD1Ev+0x1b) [0x4191c7]
[bt] (2) ./mlp_cpu() [0x420d91]
[bt] (3) ./mlp_cpu() [0x4207ea]
[bt] (4) ./mlp_cpu() [0x417310]
[bt] (5) ./mlp_cpu() [0x418135]
[bt] (6) /lib64/libc.so.6(__libc_start_main+0xfd) [0x3449c1ed1d]
[bt] (7) ./mlp_cpu() [0x415d09]


Aborted (core dumped)

Minimum reproducible example

https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/mlp_cpu.cpp
put main into a train() and run it twice.

Steps to reproduce

1. Make and run.

What have you tried to solve it?

1. Rebuilt Mxnet master (was on 1.0.0)
2. see pull request fix optimizer bug in CPP-Package #9809

[chsin]

Closed with merge of #9809