leopd opened a new issue #10158: Assertion fail when creating very large matrix URL: https://github.com/apache/incubator-mxnet/issues/10158 ## Description MXNet crashes with assertion failure when creating matrix with more than 4 billion entries. ``` MXNetError: [17:43:16] include/mxnet/././tensor_blob.h:276: Check failed: this->shape_.Size() == shape.Size() (4352000000 vs. 57032704) TBlob.get_with_shape: new and old shape do not match total elements ``` ## Environment info (Required) ``` ----------Python Info---------- Version : 3.6.4 Compiler : GCC 7.2.0 Build : ('default', 'Jan 16 2018 18:10:19') Arch : ('64bit', '') ------------Pip Info----------- Version : 9.0.1 Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.1.0 Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet Commit Hash : 07a83a0325a3d782513a04f47d711710972cb144 ----------System Info---------- Platform : Linux-4.4.0-1052-aws-x86_64-with-debian-stretch-sid system : Linux node : ip-172-31-14-183 release : 4.4.0-1052-aws version : #61-Ubuntu SMP Mon Feb 12 23:05:58 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): 32 On-line CPU(s) list: 0-31 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz Stepping: 1 CPU MHz: 2699.984 CPU max MHz: 3000.0000 CPU min MHz: 1200.0000 BogoMIPS: 4600.10 Hypervisor vendor: Xen Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 46080K NUMA node0 CPU(s): 0-31 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu 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 retpoline kaiser fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0018 sec, LOAD: 1.3588 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0643 sec, LOAD: 0.1102 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.2234 sec, LOAD: 0.1722 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0266 sec, LOAD: 0.1238 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0093 sec, LOAD: 0.1161 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0105 sec, LOAD: 0.0586 sec. Package used (Python/R/Scala/Julia): (I'm using ...) For Scala user, please provide: 1. Java version: (`java -version`) 2. Maven version: (`mvn -version`) 3. Scala runtime if applicable: (`scala -version`) ``` For R user, please provide R `sessionInfo()`: ## Error Message: ``` --------------------------------------------------------------------------- MXNetError Traceback (most recent call last) <ipython-input-2-4d3e062d9a75> in <module>() ----> 1 print(mx.nd.zeros(shape=(34000000,128))) ~/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py in __repr__(self) 180 """Returns a string representation of the array.""" 181 shape_info = 'x'.join(['%d' % x for x in self.shape]) --> 182 return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()), 183 self.__class__.__name__, 184 shape_info, self.context) ~/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py in asnumpy(self) 1791 self.handle, 1792 data.ctypes.data_as(ctypes.c_void_p), -> 1793 ctypes.c_size_t(data.size))) 1794 return data 1795 ~/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/base.py in check_call(ret) 144 """ 145 if ret != 0: --> 146 raise MXNetError(py_str(_LIB.MXGetLastError())) 147 148 MXNetError: [17:43:16] include/mxnet/././tensor_blob.h:276: Check failed: this->shape_.Size() == shape.Size() (4352000000 vs. 57032704) TBlob.get_with_shape: new and old shape do not match total elements Stack trace returned 10 entries: [bt] (0) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x276938) [0x7f2820f26938] [bt] (1) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x276d48) [0x7f2820f26d48] [bt] (2) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x2a61c8) [0x7f2820f561c8] [bt] (3) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x496e80) [0x7f2821146e80] [bt] (4) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x25cbd8c) [0x7f282327bd8c] [bt] (5) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x259f54d) [0x7f282324f54d] [bt] (6) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so(MXNDArraySyncCopyToCPU+0xa) [0x7f282303dd3a] [bt] (7) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7f28b2a06ec0] [bt] (8) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7f28b2a0687d] [bt] (9) /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce) [0x7f28b2c1bdee] ``` ## Minimum reproducible example ``` print(mx.nd.zeros(shape=(34000000,128))) ``` ## Steps to reproduce Seems to be a problem instantiating a matrix with more than 4B entries. I've tried `mx.nd.zeros`, and `mx.random.uniform` -- both do about the same thing. If the number of entries is less than 2^32 it's fine.
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