caishanli opened a new issue #17890: ndarray.cc:640 Check failed: !is_view URL: https://github.com/apache/incubator-mxnet/issues/17890 ## Description I install anaconda and create two venvs: mxnet-cu102 and mxnet-cu102mkl. with mxnet-cu102mkl, code in reproduce fail, but ok in mxnet-cu102 ### Error Message ``` Traceback (most recent call last): File "test_mkl.py", line 34, in <module> mx.nd.waitall() File "/home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/ndarray/ndarray.py", line 200, in waitall check_call(_LIB.MXNDArrayWaitAll()) File "/home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/base.py", line 255, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: [13:47:16] src/ndarray/ndarray.cc:640: Check failed: !is_view: Stack trace: [bt] (0) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x6d554b) [0x7fd69a35c54b] [bt] (1) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::NDArray::GetMKLDNNData() const+0x13c) [0x7fd69d7565cc] [bt] (2) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::op::MKLDNNTransposeForward(nnvm::NodeAttrs const&, mxnet::OpContext const&, mxnet::NDArray const&, mxnet::OpReqType const&, mxnet::NDArray const&)+0x408) [0x7fd69a411348] [bt] (3) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x36232d4) [0x7fd69d2aa2d4] [bt] (4) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::imperative::PushFComputeEx(std::function<void (nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&)> const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource, std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&)::{lambda(mxnet::RunContext)#1}::operator()(mxnet::RunContext) const+0x3c4) [0x7fd69d5fdf74] [bt] (5) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x3896084) [0x7fd69d51d084] [bt] (6) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x38a4091) [0x7fd69d52b091] [bt] (7) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x38a74e4) [0x7fd69d52e4e4] [bt] (8) /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x38a27e4) [0x7fd69d5297e4] ``` ## To Reproduce The test code is a mini version from gluoncv's train_yolo_v3.py, as below: ``` import warnings import mxnet as mx from mxnet import gluon import gluoncv as gcv gcv.utils.check_version('0.6.0') from gluoncv import data as gdata from gluoncv.data.transforms.presets.yolo import YOLO3DefaultTrainTransform from gluoncv.model_zoo.yolo import get_yolov3 from gluoncv.model_zoo.mobilenet import get_mobilenet classes = [str(i) for i in range(67)] ctx = mx.cpu() batch_size = 2 width, height = 320, 320 base_net = get_mobilenet(multiplier=1, pretrained=True) stages = [base_net.features[:33], base_net.features[33:69], base_net.features[69:-2]] anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]] strides = [8, 16, 32] net = get_yolov3('mobilenet1.0', stages, [512, 256, 128], anchors, strides, classes, 'voc', pretrained=False) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") net.initialize(ctx=ctx)` train_dataset = gdata.VOCDetection() train_loader = gluon.data.DataLoader( train_dataset.transform(YOLO3DefaultTrainTransform(width, height, net)), batch_size, True) for epoch in range(10): mx.nd.waitall() for i, data in enumerate(train_loader): print('ok') ``` ### Steps to reproduce #### conda venv: mxnet-cu102 1.set classes to 67 ok 2.set classes to 65, 66, 68, 69 ok #### conda venv: mxnet-cu102mkl 1.set classes to 67 fail 2.set classes to 65, 66, 68, 69 ok ## Environment We recommend using our script for collecting the diagnositc information. Run the following command and paste the outputs below: ``` curl --retry 10 -s https://raw.githubusercontent.com/dmlc/gluon-nlp/master/tools/diagnose.py | python ----------Python Info---------- Version : 3.7.6 Compiler : GCC 7.3.0 Build : ('default', 'Jan 8 2020 19:59:22') Arch : ('64bit', 'ELF') ------------Pip Info----------- Version : 20.0.2 Directory : /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/pip ----------MXNet Info----------- Version : 1.6.0 Directory : /home/caishanli/.conda/envs/mxnet_mkl/lib/python3.7/site-packages/mxnet Num GPUs : 2 Commit Hash : 6eec9da55c5096079355d1f1a5fa58dcf35d6752 ----------System Info---------- Platform : Linux-5.5.9-arch1-2-x86_64-with-arch-Arch-Linux system : Linux node : archlinux release : 5.5.9-arch1-2 version : #1 SMP PREEMPT Thu, 12 Mar 2020 23:01:33 +0000 ----------Hardware Info---------- machine : x86_64 processor : 架构： x86_64 CPU 运行模式： 32-bit, 64-bit 字节序： Little Endian Address sizes: 39 bits physical, 48 bits virtual CPU: 8 在线 CPU 列表： 0-7 每个核的线程数： 2 每个座的核数： 4 座： 1 NUMA 节点： 1 厂商 ID： GenuineIntel CPU 系列： 6 型号： 60 型号名称： Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz 步进： 3 CPU MHz： 3479.679 CPU 最大 MHz： 3900.0000 CPU 最小 MHz： 800.0000 BogoMIPS： 6787.05 虚拟化： VT-x L1d 缓存： 128 KiB L1i 缓存： 128 KiB L2 缓存： 1 MiB L3 缓存： 8 MiB NUMA 节点0 CPU： 0-7 Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Full generic retpoline, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling Vulnerability Tsx async abort: Not affected 标记： 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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf p ni pclmulqdq dtes64 monitor ds_cpl vmx smx 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 cpuid_fault invpcid_single pti ssbd ibrs ibpb stibp tpr_shado w vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt dtherm ida arat pln p ts md_clear flush_l1d ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0069 sec, LOAD: 4.6050 sec. Timing for GluonNLP GitHub: https://github.com/dmlc/gluon-nlp, DNS: 0.0013 sec, LOAD: 2.4630 sec. Timing for GluonNLP: http://gluon-nlp.mxnet.io, DNS: 0.0437 sec, LOAD: 2.5802 sec. Timing for D2L: http://d2l.ai, DNS: 0.0399 sec, LOAD: 1.0459 sec. Timing for D2L (zh-cn): http://zh.d2l.ai, DNS: 0.0392 sec, LOAD: 0.9735 sec. Timing for FashionMNIST: https://repo.mxnet.io/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.3429 sec, LOAD: 2.1664 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.5987 sec, LOAD: 7.1233 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0376 sec, LOAD: 1.3676 sec. ```
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