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conda-forge / cppyy-feedstock

A conda-smithy repository for cppyy.
BSD 3-Clause "New" or "Revised" License
3 stars 4 forks source link

allDict.cxx.pch is created in the wrong place #58

Open saraedum opened 2 years ago

saraedum commented 2 years ago

Solution to issue cannot be found in the documentation.

Issue

The allDict.cxx.pch is not created inside the environment but in the user's home.

$ find / -name 'allDict.cxx.pch.*' 2>/dev/null
[no output]
$ python -c 'import cppyy'
(Re-)building pre-compiled headers (options: -O2 -mavx); this may take a minute ...
$ find / -name 'allDict.cxx.pch.*' 2>/dev/null
/home/jule/.local/lib/python3.9/site-packages/cppyy_backend/etc/allDict.cxx.pch.6.25.2

Installed packages

_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       1_gnu    conda-forge
binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
binutils_linux-64         2.36                 hf3e587d_8    conda-forge
bzip2                     1.0.8                h7f98852_4    conda-forge
ca-certificates           2021.10.8            ha878542_0    conda-forge
cppyy                     2.3.0           py310h8a6ca7b_0    conda-forge
cppyy-backend             1.14.8          py310hbf28c38_0    conda-forge
cppyy-cling               6.25.3          py310hd64a29c_0    conda-forge
cpycppyy                  1.12.9          py310hbf28c38_0    conda-forge
gcc_impl_linux-64         10.3.0              hf2f2afa_14    conda-forge
gcc_linux-64              10.3.0               hc39de41_8    conda-forge
gxx_impl_linux-64         10.3.0              hf2f2afa_14    conda-forge
gxx_linux-64              10.3.0               h2593f52_8    conda-forge
kernel-headers_linux-64   2.6.32              he073ed8_15    conda-forge
ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
libffi                    3.4.2                h7f98852_5    conda-forge
libgcc-devel_linux-64     10.3.0              he6cfe16_14    conda-forge
libgcc-ng                 11.2.0              h1d223b6_14    conda-forge
libgomp                   11.2.0              h1d223b6_14    conda-forge
libllvm9                  9.0.1           default_hc23dcda_7    conda-forge
libnsl                    2.0.0                h7f98852_0    conda-forge
libsanitizer              10.3.0              h26c7422_14    conda-forge
libstdcxx-devel_linux-64  10.3.0              he6cfe16_14    conda-forge
libstdcxx-ng              11.2.0              he4da1e4_14    conda-forge
libuuid                   2.32.1            h7f98852_1000    conda-forge
libzlib                   1.2.11            h36c2ea0_1013    conda-forge
ncurses                   6.3                  h9c3ff4c_0    conda-forge
openssl                   3.0.0                h7f98852_2    conda-forge
python                    3.10.2          hc74c709_4_cpython    conda-forge
python_abi                3.10                    2_cp310    conda-forge
readline                  8.1                  h46c0cb4_0    conda-forge
sqlite                    3.37.1               h4ff8645_0    conda-forge
sysroot_linux-64          2.12                he073ed8_15    conda-forge
tk                        8.6.12               h27826a3_0    conda-forge
tzdata                    2022a                h191b570_0    conda-forge
xz                        5.2.5                h516909a_1    conda-forge
zlib                      1.2.11            h36c2ea0_1013    conda-forge

Environment info

active environment : deleteme
    active env location : /home/jule/proj/umamba/envs/deleteme
            shell level : 2
       user config file : /home/jule/.condarc
 populated config files : /home/jule/.condarc
          conda version : 4.11.0
    conda-build version : 3.21.8
         python version : 3.9.10.final.0
       virtual packages : __linux=5.16.14=0
                          __glibc=2.35=0
                          __unix=0=0
                          __archspec=1=x86_64
       base environment : /home/jule/proj/umamba  (writable)
      conda av data dir : /home/jule/proj/umamba/etc/conda
  conda av metadata url : None
           channel URLs : https://conda.anaconda.org/conda-forge/linux-64
                          https://conda.anaconda.org/conda-forge/noarch
                          https://repo.anaconda.com/pkgs/main/linux-64
                          https://repo.anaconda.com/pkgs/main/noarch
                          https://repo.anaconda.com/pkgs/r/linux-64
                          https://repo.anaconda.com/pkgs/r/noarch
          package cache : /home/jule/proj/umamba/pkgs
                          /home/jule/.conda/pkgs
       envs directories : /home/jule/proj/umamba/envs
                          /home/jule/.conda/envs
               platform : linux-64
             user-agent : conda/4.11.0 requests/2.27.1 CPython/3.9.10 Linux/5.16.14-zen1-1-zen arch/ glibc/2.35
                UID:GID : 1000:1000
             netrc file : None
           offline mode : False
saraedum commented 2 years ago

@wlav in earlier versions, the allDict.cxx.pch was created inside the environment as e.g.,

/home/jule/proj/umamba/envs/test/lib/python3.7/site-packages/cppyy_backend/etc/allDict.cxx.pch.6.25.1

I don't really understand what this file is doing exactly, but it seems to depend on the headers installed into the conda environment so it should be created inside the environment and not be shared in the home directory it seems?

Is this something that we are doing wrong in our build of cppyy?

saraedum commented 2 years ago

Can we set the CLING_STANDARD_PCH when activating cppyy to point to a place inside the environment?

wlav commented 2 years ago

I don't really understand what this file is doing exactly

The PCH (Pre-Compiled Header) is LLVM bitcode of parsed headers. It (considerably) speeds up loading standard and system headers. It's not functionally necessary, although with it, cppyy can actually run w/o a local compiler install, which can be convenient for deployment (upstream does this, but with precompiled modules instead of a single PCH). You can actually add your own headers to allHeaders.txt and see a speedup for your own projects.

One problem with it, is that some features get "baked in." Notably, AVX, OpenMP, and CUDA support.

What changed in 2.3.0 (which now supports, experimentally, CUDA), that depending on the flags EXTRA_CLING_ARGS envar (in particular -mavx which is enabled based on CPU support for AVX), the PCH is labeled for these features.

My guess of what is going on here, is that there's a PCH with .avx in the name from the Conda build, but locally does not support AVX and it gets switched off, or v.v. (OpenMP and CUDA are not automatic, although if CUDA is enabled, you get two PCHs). As of 2.3.0, that will cause a rebuild to guarantee consistency.

Yes, you can set CLING_STANDARD_PCH to some file and that will overwrite other choices. However, that PCH may not work properly if that "up-to-date" file exists but was previously build with conflicting EXTRA_CLING_ARGS. E.g. class sizes (such as in Eigen for example) can differ depending on whether AVX is enabled, resulting in trouble (segfaults or worse) if a library built with -mavx is used through Cling with a PCH w/o -mavx.

saraedum commented 2 years ago

Thanks for the explanation. I'll see if something has changed in our build as you indicate.

It's not functionally necessary

That's strange. I have a project here that crashes when running with CLING_STANDARD_PCH=none, namely I get:

input_line_48:2:19: error: no type named 'TStreamerInfo' in namespace 'CppyyLegacy'
 new CppyyLegacy::TStreamerInfo{}
saraedum commented 2 years ago

The stacktrace (probably not too enlightening):

/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cysignals/signals.cpython-39-x86_64-linux-gnu.so(+0x6f4d)[0x7f4e244edf4d]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cysignals/signals.cpython-39-x86_64-linux-gnu.so(+0x714c)[0x7f4e244ee14c]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cysignals/signals.cpython-39-x86_64-linux-gnu.so(+0x972c)[0x7f4e244f072c]
/lib/x86_64-linux-gnu/libpthread.so.0(+0x12730)[0x7f4e2efd0730]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cppyy_backend/lib/libCoreLegacy.so(_ZN11CppyyLegacy6TClass4InitEPKcsPKSt9type_infoPNS_16TVirtualIsAProxyES2_S2_iiPNS_11ClassInfo_tEb+0x5ea)[0x7f4e2de8d3da]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cppyy_backend/lib/libCoreLegacy.so(_ZN11CppyyLegacy6TClassC1EPKcsb+0x236)[0x7f4e2de94a36]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cppyy_backend/lib/libCling.so(+0x40cb26)[0x7f4e2b89fb26]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cppyy_backend/lib/libCoreLegacy.so(_ZN11CppyyLegacy6TClass8GetClassEPKcbb+0x557)[0x7f4e2de8ea87]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/cppyy_backend/lib/libcppyy_backend.so(_ZN5Cppyy8GetScopeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x104)[0x7f4e26679c74]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/libcppyy.cpython-39-x86_64-linux-gnu.so(_ZN8CPyCppyy14CreateExecutorERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_10DimensionsE+0x489)[0x7f4e261d6f19]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/libcppyy.cpython-39-x86_64-linux-gnu.so(_ZN8CPyCppyy9CPPMethod13InitExecutor_ERPNS_8ExecutorEPNS_11CallContextE+0x47)[0x7f4e2619ee37]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/libcppyy.cpython-39-x86_64-linux-gnu.so(_ZN8CPyCppyy9CPPMethod10InitializeEPNS_11CallContextE+0x43)[0x7f4e261a0783]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/libcppyy.cpython-39-x86_64-linux-gnu.so(_ZN8CPyCppyy9CPPMethod4CallERPNS_11CPPInstanceEPKP7_objectmS5_PNS_11CallContextE+0x201)[0x7f4e261a0fa1]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/libcppyy.cpython-39-x86_64-linux-gnu.so(+0x4930f)[0x7f4e261a730f]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyEval_EvalCodeWithName+0x47)[0x564d1eb08257]
python(PyEval_EvalCodeEx+0x39)[0x564d1eb08299]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/categories/map.cpython-39-x86_64-linux-gnu.so(+0xc4ce)[0x7f4e2311a4ce]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/categories/map.cpython-39-x86_64-linux-gnu.so(+0xea52)[0x7f4e2311ca52]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/categories/map.cpython-39-x86_64-linux-gnu.so(+0x1d253)[0x7f4e2312b253]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/coerce_actions.cpython-39-x86_64-linux-gnu.so(+0x1599a)[0x7f4e22e8799a]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/categories/action.cpython-39-x86_64-linux-gnu.so(+0xb328)[0x7f4e22eb0328]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/categories/action.cpython-39-x86_64-linux-gnu.so(+0x8751)[0x7f4e22ead751]
python(+0x16cce5)[0x564d1ea49ce5]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/coerce_actions.cpython-39-x86_64-linux-gnu.so(+0x20b14)[0x7f4e22e92b14]
python(+0x153645)[0x564d1ea30645]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/coerce_actions.cpython-39-x86_64-linux-gnu.so(+0x17af5)[0x7f4e22e89af5]
python(+0x16cce5)[0x564d1ea49ce5]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/parent.cpython-39-x86_64-linux-gnu.so(+0xcfaf)[0x7f4e23161faf]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/parent.cpython-39-x86_64-linux-gnu.so(+0x37776)[0x7f4e2318c776]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/parent.cpython-39-x86_64-linux-gnu.so(+0x1ed97)[0x7f4e23173d97]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/coerce.cpython-39-x86_64-linux-gnu.so(+0x385b0)[0x7f4e22ff45b0]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/coerce.cpython-39-x86_64-linux-gnu.so(+0x12835)[0x7f4e22fce835]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/coerce.cpython-39-x86_64-linux-gnu.so(+0x2239c)[0x7f4e22fde39c]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/structure/element.cpython-39-x86_64-linux-gnu.so(+0x1865a)[0x7f4e2336865a]
python(PyNumber_Multiply+0x157)[0x564d1ea7c217]
python(_PyEval_EvalFrameDefault+0xeb3)[0x564d1eac91a3]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyEval_EvalCodeWithName+0x47)[0x564d1eb08257]
python(PyEval_EvalCodeEx+0x39)[0x564d1eb08299]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/misc/cachefunc.cpython-39-x86_64-linux-gnu.so(+0x123de)[0x7f4e2e3ce3de]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/misc/cachefunc.cpython-39-x86_64-linux-gnu.so(+0x1d248)[0x7f4e2e3d9248]
python(_PyObject_MakeTpCall+0x4e7)[0x564d1ea2b297]
python(_PyEval_EvalFrameDefault+0x5301)[0x564d1eacd5f1]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/misc/cachefunc.cpython-39-x86_64-linux-gnu.so(+0xfbb2)[0x7f4e2e3cbbb2]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/misc/cachefunc.cpython-39-x86_64-linux-gnu.so(+0x12447)[0x7f4e2e3ce447]
/tmp/ruth/micromamba/envs/flatsurvey-build/lib/python3.9/site-packages/sage/misc/cachefunc.cpython-39-x86_64-linux-gnu.so(+0x1d248)[0x7f4e2e3d9248]
python(_PyObject_MakeTpCall+0x4e7)[0x564d1ea2b297]
python(_PyEval_EvalFrameDefault+0x5301)[0x564d1eacd5f1]
python(+0x1412b0)[0x564d1ea1e2b0]
python(+0x1acd31)[0x564d1ea89d31]
python(PyObject_GetAttr+0x26f)[0x564d1ea9b67f]
python(_PyEval_EvalFrameDefault+0x6db)[0x564d1eac89cb]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(PyObject_Call+0x1c2)[0x564d1ea4a1a2]
python(_PyEval_EvalFrameDefault+0x2fba)[0x564d1eacb2aa]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(PyObject_Call+0x1c2)[0x564d1ea4a1a2]
python(_PyEval_EvalFrameDefault+0x2fba)[0x564d1eacb2aa]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(+0x1b7464)[0x564d1ea94464]
python(_PyObject_MakeTpCall+0x33b)[0x564d1ea2b0eb]
python(_PyEval_EvalFrameDefault+0x5614)[0x564d1eacd904]
python(+0x140a80)[0x564d1ea1da80]
python(+0x1ac00a)[0x564d1ea8900a]
python(PyObject_Call+0xb8)[0x564d1ea4a098]
python(_PyEval_EvalFrameDefault+0x2fba)[0x564d1eacb2aa]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x4cb7)[0x564d1eaccfa7]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x4cb7)[0x564d1eaccfa7]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x4cb7)[0x564d1eaccfa7]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x4cb7)[0x564d1eaccfa7]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x4cb7)[0x564d1eaccfa7]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
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python(+0x1412b0)[0x564d1ea1e2b0]
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python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x140a80)[0x564d1ea1da80]
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python(+0x1412b0)[0x564d1ea1e2b0]
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python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x4cb7)[0x564d1eaccfa7]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x140a80)[0x564d1ea1da80]
python(+0x1ac00a)[0x564d1ea8900a]
python(_PyEval_EvalFrameDefault+0x11ba)[0x564d1eac94aa]
python(_PyFunction_Vectorcall+0x19a)[0x564d1ea887da]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(PyObject_Call+0xb8)[0x564d1ea4a098]
python(_PyEval_EvalFrameDefault+0x2fba)[0x564d1eacb2aa]
python(+0x140a80)[0x564d1ea1da80]
python(+0x1ac00a)[0x564d1ea8900a]
python(PyObject_Call+0xb8)[0x564d1ea4a098]
python(_PyEval_EvalFrameDefault+0x2fba)[0x564d1eacb2aa]
python(+0x1412b0)[0x564d1ea1e2b0]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x60e)[0x564d1eac88fe]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(+0x1ac1e3)[0x564d1ea891e3]
python(PyObject_Call+0x1c2)[0x564d1ea4a1a2]
python(_PyEval_EvalFrameDefault+0x2fba)[0x564d1eacb2aa]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyObject_FastCallDictTstate+0x65)[0x564d1ea49dc5]
python(_PyObject_Call_Prepend+0x6a)[0x564d1ea49f1a]
python(+0x265f25)[0x564d1eb42f25]
python(_PyObject_MakeTpCall+0x4e7)[0x564d1ea2b297]
python(_PyEval_EvalFrameDefault+0x4de7)[0x564d1eacd0d7]
python(+0x140a80)[0x564d1ea1da80]
python(_PyEval_EvalCodeWithName+0x47)[0x564d1eb08257]
python(PyEval_EvalCodeEx+0x39)[0x564d1eb08299]
python(PyEval_EvalCode+0x1b)[0x564d1eb082bb]
python(+0x2a017d)[0x564d1eb7d17d]
python(+0x1a4e6a)[0x564d1ea81e6a]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(_PyEval_EvalFrameDefault+0x3c1)[0x564d1eac86b1]
python(+0x140a80)[0x564d1ea1da80]
python(_PyFunction_Vectorcall+0x336)[0x564d1ea88976]
python(PyObject_Call+0x1c2)[0x564d1ea4a1a2]
python(+0x29b52b)[0x564d1eb7852b]
python(Py_RunMain+0xc5)[0x564d1eb7ca25]
python(Py_BytesMain+0x39)[0x564d1eb7ce39]
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python(+0x218321)[0x564d1eaf5321]\
wlav commented 2 years ago

Actually, that stack trace is very useful. :)

That code is the old "plugin" setup of upstream. It executes a line on Cling to instantiate a class through the autoloader, then takes the result to cast it. This way, the compiled code does not need to #Include the headers of these classes and thus not depend on the library implementing them. There's actually no value to this, but I think upstream at some point wanted to have different implementations, just that that never happened.

With the PCH, TStreamerInfo.h is contained in it. W/o, it must be loaded explicitly. That is either not happening for reasons that I don't understand or it is not available through the Cling include directories.

It's another "this works for me" (with both conda and PyPI) cases ...

That said, when I run:

$ strace python -c "import cppyy" |& grep TStreamerInfo

with export CLING_STANDARD_PCH=none, there is no output, whereas there is when that envar is unset. I would have expected the opposite (to be sure, the output only shows a stat, not a read).

Will do some digging.

saraedum commented 2 years ago

Just to clarify, CLING_STANDARD_PCH=none python -c 'import cppyy' actually works fine. It's a more involved program where things fail.

saraedum commented 2 years ago

Unfortunately, there is no strace available on the system where I am seeing this issue. I am not sure whether it can be installed, I could ask.

On my local system, I see the following:

$ python -m flatsurvey.worker ngon -a 2 -a 3 -a 6  # works
$ CLING_STANDARD_PCH=none python -m flatsurvey.worker ngon -a 2 -a 3 -a 6
Traceback (most recent call last):
  File "/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/jule/proj/eskin/flatsurvey/flatsurvey/worker/__main__.py", line 78, in <module>
    import flatsurvey.jobs
  File "/home/jule/proj/eskin/flatsurvey/flatsurvey/jobs/__init__.py", line 35, in <module>
    from .undetermined_interval_exchange_transformation import (
  File "/home/jule/proj/eskin/flatsurvey/flatsurvey/jobs/undetermined_interval_exchange_transformation.py", line 48, in <module>
    import pyeantic
  File "/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/site-packages/pyeantic/__init__.py", line 26, in <module>
    from .cppyy_eantic import eantic
  File "/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/site-packages/pyeantic/cppyy_eantic.py", line 59, in <module>
    from cppyythonizations.pickling.cereal import enable_cereal
  File "/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/site-packages/cppyythonizations/pickling/cereal.py", line 115, in <module>
    cppyy.include(os.path.join(os.path.dirname(__file__), '..', 'include/cereal/details/static_object.hpp'))
  File "/home/jule/.local/lib/python3.9/site-packages/cppyy/__init__.py", line 235, in include
    raise ImportError('Failed to load header file "%s"%s' % (header, err.err))
ImportError: Failed to load header file "/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/site-packages/cppyythonizations/pickling/../include/cereal/details/static_object.hpp"
In file included from input_line_18:1:
/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/site-packages/cppyythonizations/pickling/../include/cereal/details/static_object.hpp:71:5: error: 'auto' return without trailing return type; deduced return types are a C++14 extension
    auto& getStatics() {
    ^
/home/jule/proj/umamba/envs/flatsurf/lib/python3.9/site-packages/cppyythonizations/pickling/../include/cereal/details/static_object.hpp:73:14: error: no viable conversion from returned value of type 'std::unordered_map<std::type_index, void *>' to function return type 'int'
      return statics;
$ conda list |grep cppyy                                                                
cppyy                     2.3.0            py39hd14de60_0    conda-forge
cppyy-backend             1.14.8           py39hf939315_0    conda-forge
cppyy-cling               6.25.3           py39h3d66fe8_0    conda-forge
cppyythonizations         1.2.1              pyhd8ed1ab_0    conda-forge
cpycppyy                  1.12.9           py39hf939315_0    conda-forge
$ export |grep -E 'FLAGS|CLING'  # nothing overly suspicious here, note that the CXXFLAGS contain -std=c++17                                             
CFLAGS='-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/jule/proj/umamba/envs/flatsurf/include'
CPPFLAGS='-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/jule/proj/umamba/envs/flatsurf/include'
CXXFLAGS='-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/jule/proj/umamba/envs/flatsurf/include'
DEBUG_CFLAGS='-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /home/jule/proj/umamba/envs/flatsurf/include'
DEBUG_CPPFLAGS='-D_DEBUG -D_FORTIFY_SOURCE=2 -Og -isystem /home/jule/proj/umamba/envs/flatsurf/include'
DEBUG_CXXFLAGS='-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /home/jule/proj/umamba/envs/flatsurf/include'
LDFLAGS='-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,--allow-shlib-undefined -Wl,-rpath,/home/jule/proj/umamba/envs/flatsurf/lib -Wl,-rpath-link,/home/jule/proj/umamba/envs/flatsurf/lib -L/home/jule/proj/umamba/envs/flatsurf/lib'
$ EXTRA_CLING_ARGS=-std=c++17 CLING_STANDARD_PCH=none python -m flatsurvey.worker ngon -a 2 -a 3 -a 6  # works

So, I guess this is unrelated and somehow expected. Anyway, setting EXTRA_CLING_ARGS=-std=c++17 on the system where I see the no type named 'TStreamerInfo' in namespace 'CppyyLegacy' does not fix that problem.

wlav commented 2 years ago

For building cppyy-cling, the flag that sets the standard during build (this gets baked in) is STDCXX. For PyPI (i.e. manylinux builds), it actually defaults to C++11 and that would explain the behavior. However, the default should be C++17 for other builds (see setup.py; the checking for "manylinux" is actually kinda dumb, so maybe the conda build system is incorrectly flagged as a "manylinux" box leading to a C++11 default):

_is_manylinux = None
def is_manylinux():
    global _is_manylinux
    if _is_manylinux is None:
        _is_manylinux = False
        try:
            for line in open('/etc/redhat-release').readlines():
              # mark manylinux1, manylinux2010, or manylinux2014
                if 'CentOS release 5.11 (Final)' in line or \
                   'CentOS release 6.10 (Final)' in line or \
                   'CentOS Linux release 7.9.2009 (Core)' in line:
                    _is_manylinux = True
                    break
        except (OSError, IOError):
            pass
    return _is_manylinux

And yes, the flags get "fixed up" when building the PCH, either from the STDCXX envar or from the EXTRA_CLING_ARGS one (if provided). This happens in makepch.py.

Note that if EXTRA_CLING_ARGS is set, the defaults (-O2 and -mavx depending on processor capability) are not added. Otherwise, I would not expect it to make a difference.

For the original error, I'm trying to reproduce the error and am so far not able. I'm thinking that if the header is available (which I expect it to be if there are no other installation issues), then it must mean that somehow in the initialization order, the header is not included before first use of the class. The proper fix is then probably to add its inclusion in the ApplicationStarter in clingwrapper.cxx.

saraedum commented 2 years ago

For building cppyy-cling, the flag that sets the standard during build (this gets baked in) is STDCXX. For PyPI (i.e. manylinux builds), it actually defaults to C++11 and that would explain the behavior. However, the default should be C++17 for other builds (see setup.py; the checking for "manylinux" is actually kinda dumb, so maybe the conda build system is incorrectly flagged as a "manylinux" box leading to a C++11 default):

We don't really use the setup.py on conda-forge but invoke cmake directly, see https://github.com/conda-forge/cppyy-cling-feedstock/blob/main/recipe/use-dumb-setup.py.patch. If you think that that's an issue, we could try to go back to using the setup.py but it seemed to be easier to hard-code the flags that we set instead of having setup.py decide things for us.

Anyway, we set -DCMAKE_CXX_STANDARD=17

Looking at the cmake flags that the setup.py is setting, I find the following differences:

We also set lots of flag that we found in our build of root, if any of these look wrong to you, please let us know :)

wlav commented 2 years ago

setup.py sets -Druntime_cxxmodules=OFF, we don't.

Modules and the PCH are targeting the same feature: they both contain precompiled bitcode, just that the packaging of modules is, well, modular and can thus be used for non-system code. (They also deserialize less code when Cling is looking for just a single class or function, so reducing memory use.) I'd recommend using only one or the other. Both? I don't know what will happen.

I've not looked in a (too) long time into modules, but last time around they were only fully functional on Linux: I had trouble with debug builds on Mac and even upstream said that support for Windows was minimal. Upstream cares less about Windows, hence they were happy to move. I don't have that luxury.