Soonhwan-Kwon opened a new issue #13671: Error when set export 
MXNET_SUBGRAPH_BACKEND=MKLDNN
URL: https://github.com/apache/incubator-mxnet/issues/13671
 
 
   ## Description
   I tried to use the newly added Graph Optimization in MKDNN backend: Graph 
optimization and Quantization (experimental), but it makes error which is fine 
without  set export MXNET_SUBGRAPH_BACKEND=MKLDNN.
   
   ## Environment info (Required)
   ----------Python Info----------
   ('Version      :', '2.7.15')
   ('Compiler     :', 'GCC 7.2.0')
   ('Build        :', ('default', 'May  1 2018 23:32:55'))
   ('Arch         :', ('64bit', ''))
   ------------Pip Info-----------
   ('Version      :', '10.0.1')
   ('Directory    :', 
'/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/pip')
   ----------MXNet Info-----------
   ('Version      :', '1.5.0')
   ('Directory    :', 
'/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet')
   ('Commit Hash   :', '655f1c6f7a0706dd622f73db9af2e6df895ca213')
   ----------System Info----------
   ('Platform     :', 'Linux-4.4.0-1072-aws-x86_64-with-debian-stretch-sid')
   ('system       :', 'Linux')
   ('node         :', 'ip-172-31-10-142')
   ('release      :', '4.4.0-1072-aws')
   ('version      :', '#82-Ubuntu SMP Fri Nov 2 15:00:21 UTC 2018')
   ----------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:                 85
   Model name:            Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz
   Stepping:              3
   CPU MHz:               3000.000
   BogoMIPS:              6000.00
   Hypervisor vendor:     KVM
   Virtualization type:   full
   L1d cache:             32K
   L1i cache:             32K
   L2 cache:              1024K
   L3 cache:              25344K
   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 ss ht syscall nx pdpe1gb rdtscp lm 
constant_tsc rep_good nopl xtopology nonstop_tsc 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 invpcid_single kaiser 
fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f rdseed 
adx smap clflushopt clwb avx512cd xsaveopt xsavec xgetbv1 ida arat pku
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0014 
sec, LOAD: 0.5628 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0022 sec, LOAD: 
0.1005 sec.
   Timing for FashionMNIST: 
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
 DNS: 0.0281 sec, LOAD: 0.1317 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0025 sec, 
LOAD: 0.0809 sec.
   Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0474 sec, LOAD: 
0.2117 sec.
   Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1398 sec, LOAD: 
0.5508 sec.
   
   ## Error Message:
   [07:06:46] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start 
to execute MKLDNN Convolution optimization pass.
   Traceback (most recent call last):
     File "ecgdetector_rnn.py", line 14, in <module>
       rnn_result , shape_main, shape_cfg, time_infos, args = 
rnn_process(parser)
     File "/data/cardio_workspace/cardio_deploy/rnn_process.py", line 64, in 
rnn_process
       mxnet = MxnetModel(args.configfile, args.archfile, edf_category, 
is_hrnn, buckets)
     File "/data/cardio_workspace/cardio_deploy/ecg_script_frequency.py", line 
282, in __init__
       for_training=True)
     File 
"/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/module/bucketing_module.py",
 line 343, in bind
       force_rebind=False, shared_module=None, grad_req=grad_req)
     File 
"/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/module/module.py",
 line 429, in bind
       state_names=self._state_names)
     File 
"/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/module/executor_group.py",
 line 279, in __init__
       self.bind_exec(data_shapes, label_shapes, shared_group)
     File 
"/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/module/executor_group.py",
 line 375, in bind_exec
       shared_group))
     File 
"/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/module/executor_group.py",
 line 662, in _bind_ith_exec
       shared_buffer=shared_data_arrays, **input_shapes)
     File 
"/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/symbol/symbol.py",
 line 1529, in simple_bind
       raise RuntimeError(error_msg)
   RuntimeError: simple_bind error. Arguments:
   category: (900,)
   data: (900, 137, 9)
   label: (900, 2)
   [07:06:46] src/pass/gradient.cc:192: Operator _sg_mkldnn_conv is 
non-differentiable because it didn't register FGradient attribute.
   
   Stack trace returned 10 entries:
   [bt] (0) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x21f5a4)
 [0x7fd1060045a4]
   [bt] (1) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x21f981)
 [0x7fd106004981]
   [bt] (2) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x37553df)
 [0x7fd10953a3df]
   [bt] (3) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2d7ce11)
 [0x7fd108b61e11]
   [bt] (4) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x373d6ed)
 [0x7fd1095226ed]
   [bt] (5) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2ac0197)
 [0x7fd1088a5197]
   [bt] (6) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2b41d16)
 [0x7fd108926d16]
   [bt] (7) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2b42c8e)
 [0x7fd108927c8e]
   [bt] (8) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2b4372f)
 [0x7fd10892872f]
   [bt] (9) 
/home/ubuntu/anaconda3/envs/mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::exec::GraphExecutor::Init(nnvm::Symbol,
 mxnet::Context const&, std::map<std::string, mxnet::Context, 
std::less<std::string>, std::allocator<std::pair<std::string const, 
mxnet::Context> > > const&, std::vector<mxnet::Context, 
std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, 
std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, 
std::allocator<mxnet::Context> > const&, std::unordered_map<std::string, 
nnvm::TShape, std::hash<std::string>, std::equal_to<std::string>, 
std::allocator<std::pair<std::string const, nnvm::TShape> > > const&, 
std::unordered_map<std::string, int, std::hash<std::string>, 
std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > 
> const&, std::unordered_map<std::string, int, std::hash<std::string>, 
std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > 
> const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > 
const&, std::unordered_set<std::string, std::hash<std::string>, 
std::equal_to<std::string>, std::allocator<std::string> > const&, 
std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, 
std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, 
std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, 
std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, 
std::equal_to<std::string>, std::allocator<std::pair<std::string const, 
mxnet::NDArray> > >*, mxnet::Executor*, std::unordered_map<nnvm::NodeEntry, 
mxnet::NDArray, nnvm::NodeEntryHash, nnvm::NodeEntryEqual, 
std::allocator<std::pair<nnvm::NodeEntry const, mxnet::NDArray> > > 
const&)+0x9b) [0x7fd10892c7fb]

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