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intel / ideep

Intel® Optimization for Chainer*, a Chainer module providing numpy like API and DNN acceleration using MKL-DNN.
MIT License
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mkl-dnn version used by PyTorch causes internal compiler error when built by latest VS2019 #44

Open malfet opened 4 years ago

malfet commented 4 years ago

See below:

C:\Users\circleci\project\build\win_tmp\bin\sccache-cl.exe   /TP -DDNNL_ENABLE_CONCURRENT_EXEC -DDNNL_ENABLE_MAX_CPU_ISA -DDNNL_X64=1 -DIDEEP_USE_MKL -DNOMINMAX -DONNXIFI_ENABLE_EXT=1 -DONNX_ML=1 -DONNX_NAMESPACE=onnx_torch -DTH_BLAS_MKL -DWIN32_LEAN_AND_MEAN -D_CRT_SECURE_NO_DEPRECATE=1 -D_OPENMP_NOFORCE_MANIFEST -D_WIN -D__STDC_CONSTANT_MACROS -D__STDC_LIMIT_MACROS -I..\cmake\..\third_party\benchmark\include -Icaffe2\contrib\aten -I..\third_party\onnx -Ithird_party\onnx -I..\third_party\foxi -Ithird_party\foxi -I..\third_party\ideep\mkl-dnn\include -Ithird_party\ideep\mkl-dnn\include -I..\third_party\ideep\mkl-dnn\src -I..\cmake\..\third_party\googletest\googlemock\include -I..\cmake\..\third_party\googletest\googletest\include -I..\third_party\protobuf\src -Iwin_tmp\mkl\include -I..\third_party -I..\cmake\..\third_party\eigen -IC:\Jenkins\Miniconda3\include -IC:\Jenkins\Miniconda3\lib\site-packages\numpy\core\include -I..\cmake\..\third_party\pybind11\include /DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -openmp:experimental -DNDEBUG -openmp:experimental  /MP    /wd4800 /wd4068 /wd4305 /wd4551 /wd4244  /MD /O2 /Ob2 /DNDEBUG /w /bigobj -DNDEBUG -DUSE_GCC_GET_CPUID -DUSE_AVX -DUSE_AVX2 -std:c++14 /showIncludes /Fothird_party\ideep\mkl-dnn\src\cpu\CMakeFiles\dnnl_cpu.dir\gemm_convolution_utils.cpp.obj /Fdthird_party\ideep\mkl-dnn\src\cpu\CMakeFiles\dnnl_cpu.dir\ /FS -c ..\third_party\ideep\mkl-dnn\src\cpu\gemm_convolution_utils.cpp
FAILED: third_party/ideep/mkl-dnn/src/cpu/CMakeFiles/dnnl_cpu.dir/gemm_convolution_utils.cpp.obj 
C:\Users\circleci\project\build\win_tmp\bin\sccache-cl.exe   /TP -DDNNL_ENABLE_CONCURRENT_EXEC -DDNNL_ENABLE_MAX_CPU_ISA -DDNNL_X64=1 -DIDEEP_USE_MKL -DNOMINMAX -DONNXIFI_ENABLE_EXT=1 -DONNX_ML=1 -DONNX_NAMESPACE=onnx_torch -DTH_BLAS_MKL -DWIN32_LEAN_AND_MEAN -D_CRT_SECURE_NO_DEPRECATE=1 -D_OPENMP_NOFORCE_MANIFEST -D_WIN -D__STDC_CONSTANT_MACROS -D__STDC_LIMIT_MACROS -I..\cmake\..\third_party\benchmark\include -Icaffe2\contrib\aten -I..\third_party\onnx -Ithird_party\onnx -I..\third_party\foxi -Ithird_party\foxi -I..\third_party\ideep\mkl-dnn\include -Ithird_party\ideep\mkl-dnn\include -I..\third_party\ideep\mkl-dnn\src -I..\cmake\..\third_party\googletest\googlemock\include -I..\cmake\..\third_party\googletest\googletest\include -I..\third_party\protobuf\src -Iwin_tmp\mkl\include -I..\third_party -I..\cmake\..\third_party\eigen -IC:\Jenkins\Miniconda3\include -IC:\Jenkins\Miniconda3\lib\site-packages\numpy\core\include -I..\cmake\..\third_party\pybind11\include /DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -openmp:experimental -DNDEBUG -openmp:experimental  /MP    /wd4800 /wd4068 /wd4305 /wd4551 /wd4244  /MD /O2 /Ob2 /DNDEBUG /w /bigobj -DNDEBUG -DUSE_GCC_GET_CPUID -DUSE_AVX -DUSE_AVX2 -std:c++14 /showIncludes /Fothird_party\ideep\mkl-dnn\src\cpu\CMakeFiles\dnnl_cpu.dir\gemm_convolution_utils.cpp.obj /Fdthird_party\ideep\mkl-dnn\src\cpu\CMakeFiles\dnnl_cpu.dir\ /FS -c ..\third_party\ideep\mkl-dnn\src\cpu\gemm_convolution_utils.cpp
C:\Users\circleci\project\third_party\ideep\mkl-dnn\src\cpu\gemm_convolution_utils.cpp(401) : fatal error C1001: Internal compiler error.
(compiler file 'd:\agent\_work\7\s\src\vctools\Compiler\Utc\src\p2\main.c', line 195)
 To work around this problem, try simplifying or changing the program near the locations listed above.
If possible please provide a repro here: https://developercommunity.visualstudio.com 
Please choose the Technical Support command on the Visual C++ 
 Help menu, or open the Technical Support help file for more information
  cl!RaiseException()+0x69
  cl!RaiseException()+0x69
  cl!CloseTypeServerPDB()+0x22e6b
  cl!CloseTypeServerPDB()+0xcd30a
Microsoft (R) C/C++ Optimizing Compiler Version 19.27.29111 for x64
Copyright (C) Microsoft Corporation.  All rights reserved.

Would it be possible to update version of MKL-DNN used by ideep:pytorch branch to include https://github.com/oneapi-src/oneDNN/pull/805

CaoZhongZ commented 4 years ago

hmmm, currently we update DNNL only when DNNL release. You could manually upgrade it, is that feasible?

N-Dekker commented 4 years ago

Please consider voting or leaving a comment at the bug report that I submitted, about the compiler bug that appears to have caused this issue: Visual Studio problem 1145942 - VS2019 Internal compiler error using __restrict keyword in Release build

CaoZhongZ commented 4 years ago

@N-Dekker Thank you!

Jianhui-Li commented 4 years ago

Ping Yong @uyongw

uyongw commented 4 years ago

I think next oneDNN release will include @N-Dekker's fixes we will then upgrade the library. Hi @vpirogov Is there a timeline for oneDNN 1.6.2?

vpirogov commented 4 years ago

@uyongw, the changes are already backported to rls-v1.6 branch. I'll get the patch release posted by the end of the week.

vpirogov commented 4 years ago

@uyongw,

oneDNN v1.6.2 is released.

uyongw commented 4 years ago

Thanks. We will submit the upgrade once validation done.