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Updates LLVM usage to match
[55d2fffdae55](https://github.com/llvm/llvm-project/commit/55d2fffdae55)
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] | null | [] | "2024-06-06T02:19:15" | "2024-06-06T08:41:06" | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
latest from git
### Custom code
No
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
Visual Studio 2022
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Build failed for Windows, reporting several errors:
- Code require C++ 20 but cmake set std to 17
- conflict `MT_StaticRelease` and `MD_DynamicRelease` flag during linking
- symbol redefinition
### Standalone code to reproduce the issue
```shell
git clone https://github.com/tensorflow/tensorflow.git tensorflow-src
cd .\tensorflow-src\
mkdir build_tflite
cd .\build_tflite\
cmake ../tensorflow/lite -DCMAKE_BUILD_TYPE=RelWithDebInfo -DTFLITE_ENABLE_XNNPACK=ON
cmake --build . --config RelWithDebInfo -j -t benchmark_model
```
### Relevant log output
```shell
libprotobuf.lib(descriptor.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(ben
chmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(arenastring.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(be
nchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(message_lite.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(b
enchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(repeated_ptr_field.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRele
ase”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(text_format.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(be
nchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(message.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchm
ark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(generated_message_util.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配 值“MD_Dynamic
Release”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(coded_stream.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(b
enchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(arena.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmar
k_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(wire_format_lite.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicReleas
e”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(parse_context.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(
benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(unknown_field_set.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelea
se”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(generated_message_reflection.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease” 不匹配值“MD_D
ynamicRelease”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxpro
j]
libprotobuf.lib(wire_format.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(be
nchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(stringpiece.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(be
nchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(common.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchma
rk_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(strutil.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchm
ark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(repeated_field.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”
(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(descriptor.pb.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(
benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(tokenizer.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benc
hmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(stringprintf.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(b
enchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(substitute.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(ben
chmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(descriptor_database.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRel
ease”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(dynamic_message.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease
”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(strtod.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchma
rk_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(any_lite.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(bench
mark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(zero_copy_stream.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicReleas
e”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(zero_copy_stream_impl_lite.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不 匹配值“MD_Dyn
amicRelease”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(zero_copy_stream_impl.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicR
elease”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(implicit_weak_message.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicR
elease”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(any.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(map_field.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benc
hmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(reflection_ops.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”
(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(extension_set.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(
benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(structurally_valid.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRele
ase”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(extension_set_heavy.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRel
ease”(benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(status.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchma
rk_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(int128.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchma
rk_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(io_win32.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(bench
mark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libprotobuf.lib(map.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: virtual __cdecl std::basic_streambuf<char,struct std::char_traits<cha
r> >::~basic_streambuf<char,struct std::char_traits<char> >(void)" (??1?$basic_streambuf@DU?$char_traits@D@std@@@std@@UEA
A@XZ) 已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_mod
el.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: virtual __cdecl std::basic_ios<char,struct std::char_traits<char> >::
~basic_ios<char,struct std::char_traits<char> >(void)" (??1?$basic_ios@DU?$char_traits@D@std@@@std@@UEAA@XZ) 已经在 libproto
buf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: void __cdecl std::basic_ostream<char,struct std::char_traits<char> >:
:_Osfx(void)" (?_Osfx@?$basic_ostream@DU?$char_traits@D@std@@@std@@QEAAXXZ) 已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\g
it_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: class std::basic_ostream<char,struct std::char_traits<char> > & __cde
cl std::basic_ostream<char,struct std::char_traits<char> >::flush(void)" (?flush@?$basic_ostream@DU?$char_traits@D@std@@@
std@@QEAAAEAV12@XZ) 已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark
\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: virtual __cdecl std::basic_iostream<char,struct std::char_traits<char
> >::~basic_iostream<char,struct std::char_traits<char> >(void)" (??1?$basic_iostream@DU?$char_traits@D@std@@@std@@UEAA@X
Z) 已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.
vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: class std::basic_ostream<char,struct std::char_traits<char> > & __cde
cl std::basic_ostream<char,struct std::char_traits<char> >::operator<<(int)" (??6?$basic_ostream@DU?$char_traits@D@std@@@
std@@QEAAAEAV01@H@Z) 已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmar
k\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "protected: __int64 __cdecl std::basic_streambuf<char,struct std::char_traits<
char> >::_Gnavail(void)const " (?_Gnavail@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEBA_JXZ) 已经在 libprotobuf.lib(de
scriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "protected: __int64 __cdecl std::basic_streambuf<char,struct std::char_traits<
char> >::_Pnavail(void)const " (?_Pnavail@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEBA_JXZ) 已经在 libprotobuf.lib(de
scriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "protected: virtual __int64 __cdecl std::basic_streambuf<char,struct std::char
_traits<char> >::xsgetn(char *,__int64)" (?xsgetn@?$basic_streambuf@DU?$char_traits@D@std@@@std@@MEAA_JPEAD_J@Z) 已经在 libp
rotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "protected: virtual __int64 __cdecl std::basic_streambuf<char,struct std::char
_traits<char> >::xsputn(char const *,__int64)" (?xsputn@?$basic_streambuf@DU?$char_traits@D@std@@@std@@MEAA_JPEBD_J@Z) 已 经
在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxpr
oj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: virtual __cdecl std::basic_ostream<char,struct std::char_traits<char>
>::~basic_ostream<char,struct std::char_traits<char> >(void)" (??1?$basic_ostream@DU?$char_traits@D@std@@@std@@UEAA@XZ)
已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcx
proj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: class std::basic_ostream<char,struct std::char_traits<char> > & __cde
cl std::basic_ostream<char,struct std::char_traits<char> >::operator<<(unsigned __int64)" (??6?$basic_ostream@DU?$char_tr
aits@D@std@@@std@@QEAAAEAV01@_K@Z) 已经在 libprotobuf.lib(int128.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tool
s\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: class std::basic_ostream<char,struct std::char_traits<char> > & __cde
cl std::basic_ostream<char,struct std::char_traits<char> >::write(char const *,__int64)" (?write@?$basic_ostream@DU?$char
_traits@D@std@@@std@@QEAAAEAV12@PEBD_J@Z) 已经在 libprotobuf.lib(stringpiece.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build
_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: virtual __cdecl std::basic_istream<char,struct std::char_traits<char>
>::~basic_istream<char,struct std::char_traits<char> >(void)" (??1?$basic_istream@DU?$char_traits@D@std@@@std@@UEAA@XZ)
已经在 libprotobuf.lib(descriptor.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcx
proj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: bool __cdecl std::basic_istream<char,struct std::char_traits<char> >:
:_Ipfx(bool)" (?_Ipfx@?$basic_istream@DU?$char_traits@D@std@@@std@@QEAA_N_N@Z) 已经在 libprotobuf.lib(zero_copy_stream_impl.
obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
msvcprt.lib(MSVCP140.dll) : error LNK2005: "public: class std::basic_istream<char,struct std::char_traits<char> > & __cde
cl std::basic_istream<char,struct std::char_traits<char> >::read(char *,__int64)" (?read@?$basic_istream@DU?$char_traits@
D@std@@@std@@QEAAAEAV12@PEAD_J@Z) 已经在 libprotobuf.lib(zero_copy_stream_impl.obj) 中定义 [D:\git_repo\temp\tensorflow-src\bui
ld_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "public: class std::_Yarn<char> & __cdecl std::_Yarn<char>::operator=(char cons
t *)" (??4?$_Yarn@D@std@@QEAAAEAV01@PEBD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tfli
te\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "void __cdecl std::_Facet_Register(class std::_Facet_base *)" (?_Facet_Register
@std@@YAXPEAV_Facet_base@1@@Z) 已经在 msvcprt.lib(locale0_implib.obj) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tool
s\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "private: static class std::locale::_Locimp * __cdecl std::locale::_Getgloballo
cale(void)" (?_Getgloballocale@locale@std@@CAPEAV_Locimp@12@XZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tenso
rflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "private: static class std::locale::_Locimp * __cdecl std::locale::_Init(bool)"
(?_Init@locale@std@@CAPEAV_Locimp@12@_N@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tfli
te\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "public: static void __cdecl std::_Locinfo::_Locinfo_ctor(class std::_Locinfo *
,char const *)" (?_Locinfo_ctor@_Locinfo@std@@SAXPEAV12@PEBD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tenso
rflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "public: static void __cdecl std::_Locinfo::_Locinfo_dtor(class std::_Locinfo *
)" (?_Locinfo_dtor@_Locinfo@std@@SAXPEAV12@@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_t
flite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale0.obj) : error LNK2005: "private: static class std::locale::_Locimp * __cdecl std::locale::_Locimp::_Ne
w_Locimp(class std::locale::_Locimp const &)" (?_New_Locimp@_Locimp@locale@std@@CAPEAV123@AEBV123@@Z) 已经在 msvcprt.lib(MSV
CP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_m
ain.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: class std::_Yarn<char> & __cdecl std::_Yarn<char>::operator=(char const
*)" (??4?$_Yarn@D@std@@QEAAAEAV01@PEBD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflit
e\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: __cdecl std::locale::id::operator unsigned __int64(void)" (??Bid@locale
@std@@QEAA_KXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark
_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: static unsigned __int64 __cdecl std::codecvt<char,char,struct _Mbstatet
>::_Getcat(class std::locale::facet const * *,class std::locale const *)" (?_Getcat@?$codecvt@DDU_Mbstatet@@@std@@SA_KPEA
PEBVfacet@locale@2@PEBV42@@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\bench
mark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: struct _Cvtvec __cdecl std::_Locinfo::_Getcvt(void)const " (?_Getcvt@_L
ocinfo@std@@QEBA?AU_Cvtvec@@XZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\ben
chmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "protected: char * __cdecl std::basic_streambuf<char,struct std::char_traits<cha
r> >::_Gninc(void)" (?_Gninc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEAAPEADXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义
[D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "private: static void __cdecl std::locale::_Locimp::_Locimp_Addfac(class std::lo
cale::_Locimp *,class std::locale::facet *,unsigned __int64)" (?_Locimp_Addfac@_Locimp@locale@std@@CAXPEAV123@PEAVfacet@2
3@_K@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.v
cxproj]
libcpmt.lib(locale.obj) : error LNK2005: "protected: char * __cdecl std::basic_streambuf<char,struct std::char_traits<cha
r> >::_Pninc(void)" (?_Pninc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEAAPEADXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义
[D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: int __cdecl std::ios_base::flags(void)const " (?flags@ios_base@std@@QEB
AHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcx
proj]
libcpmt.lib(locale.obj) : error LNK2005: "protected: char * __cdecl std::basic_streambuf<char,struct std::char_traits<cha
r> >::gptr(void)const " (?gptr@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEBAPEADXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中
定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: int __cdecl std::basic_streambuf<char,struct std::char_traits<char> >::
sbumpc(void)" (?sbumpc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@QEAAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_
repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: int __cdecl std::basic_streambuf<char,struct std::char_traits<char> >::
sgetc(void)" (?sgetc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@QEAAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_re
po\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: int __cdecl std::basic_streambuf<char,struct std::char_traits<char> >::
sputc(char)" (?sputc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@QEAAHD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_r
epo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: __int64 __cdecl std::ios_base::width(__int64)" (?width@ios_base@std@@QE
AA_J_J@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model
.vcxproj]
libcpmt.lib(locale.obj) : error LNK2005: "public: __int64 __cdecl std::ios_base::width(void)const " (?width@ios_base@std@
@QEBA_JXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_mode
l.vcxproj]
libcpmt.lib(iosptrs.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::~codec
vt<wchar_t,char,struct _Mbstatet>(void)" (??1?$codecvt@_WDU_Mbstatet@@@std@@MEAA@XZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D
:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: class std::_Yarn<char> & __cdecl std::_Yarn<char>::operator=(char cons
t *)" (??4?$_Yarn@D@std@@QEAAAEAV01@PEBD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tfli
te\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: __cdecl std::locale::id::operator unsigned __int64(void)" (??Bid@local
e@std@@QEAA_KXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmar
k_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: struct _Cvtvec __cdecl std::_Locinfo::_Getcvt(void)const " (?_Getcvt@_
Locinfo@std@@QEBA?AU_Cvtvec@@XZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\be
nchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: unsigned short const * __cdecl std::_Locinfo::_W_Getdays(void)const "
(?_W_Getdays@_Locinfo@std@@QEBAPEBGXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\to
ols\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: unsigned short const * __cdecl std::_Locinfo::_W_Getmonths(void)const
" (?_W_Getmonths@_Locinfo@std@@QEBAPEBGXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflit
e\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual bool __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::d
o_always_noconv(void)const " (?do_always_noconv@?$codecvt@_WDU_Mbstatet@@@std@@MEBA_NXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定
义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual int __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::do
_encoding(void)const " (?do_encoding@?$codecvt@_WDU_Mbstatet@@@std@@MEBAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_re
po\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual int __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::do
_in(struct _Mbstatet &,char const *,char const *,char const * &,wchar_t *,wchar_t *,wchar_t * &)const " (?do_in@?$codecvt
@_WDU_Mbstatet@@@std@@MEBAHAEAU_Mbstatet@@PEBD1AEAPEBDPEA_W3AEAPEA_W@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\te
mp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual int __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::do
_length(struct _Mbstatet &,char const *,char const *,unsigned __int64)const " (?do_length@?$codecvt@_WDU_Mbstatet@@@std@@
MEBAHAEAU_Mbstatet@@PEBD1_K@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benc
hmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual int __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::do
_max_length(void)const " (?do_max_length@?$codecvt@_WDU_Mbstatet@@@std@@MEBAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\gi
t_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual int __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::do
_out(struct _Mbstatet &,wchar_t const *,wchar_t const *,wchar_t const * &,char *,char *,char * &)const " (?do_out@?$codec
vt@_WDU_Mbstatet@@@std@@MEBAHAEAU_Mbstatet@@PEB_W1AEAPEB_WPEAD3AEAPEAD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\
temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "protected: virtual int __cdecl std::codecvt<wchar_t,char,struct _Mbstatet>::do
_unshift(struct _Mbstatet &,char *,char *,char * &)const " (?do_unshift@?$codecvt@_WDU_Mbstatet@@@std@@MEBAHAEAU_Mbstatet
@@PEAD1AEAPEAD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchma
rk_model.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: int __cdecl std::ios_base::flags(void)const " (?flags@ios_base@std@@QE
BAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vc
xproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: __int64 __cdecl std::ios_base::width(__int64)" (?width@ios_base@std@@Q
EAA_J_J@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_mode
l.vcxproj]
libcpmt.lib(wlocale.obj) : error LNK2005: "public: __int64 __cdecl std::ios_base::width(void)const " (?width@ios_base@std
@@QEBA_JXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_mod
el.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: class std::_Yarn<char> & __cdecl std::_Yarn<char>::operator=(char cons
t *)" (??4?$_Yarn@D@std@@QEAAAEAV01@PEBD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tfli
te\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: __cdecl std::locale::id::operator unsigned __int64(void)" (??Bid@local
e@std@@QEAA_KXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmar
k_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: struct _Cvtvec __cdecl std::_Locinfo::_Getcvt(void)const " (?_Getcvt@_
Locinfo@std@@QEBA?AU_Cvtvec@@XZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\be
nchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "protected: char * __cdecl std::basic_streambuf<char,struct std::char_traits<ch
ar> >::_Gninc(void)" (?_Gninc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEAAPEADXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定
义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "protected: char * __cdecl std::basic_streambuf<char,struct std::char_traits<ch
ar> >::_Pninc(void)" (?_Pninc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEAAPEADXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定
义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: unsigned short const * __cdecl std::_Locinfo::_W_Getdays(void)const "
(?_W_Getdays@_Locinfo@std@@QEBAPEBGXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\to
ols\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: unsigned short const * __cdecl std::_Locinfo::_W_Getmonths(void)const
" (?_W_Getmonths@_Locinfo@std@@QEBAPEBGXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflit
e\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: int __cdecl std::ios_base::flags(void)const " (?flags@ios_base@std@@QE
BAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vc
xproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "protected: char * __cdecl std::basic_streambuf<char,struct std::char_traits<ch
ar> >::gptr(void)const " (?gptr@?$basic_streambuf@DU?$char_traits@D@std@@@std@@IEBAPEADXZ) 已经在 msvcprt.lib(MSVCP140.dll)
中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: int __cdecl std::basic_streambuf<char,struct std::char_traits<char> >:
:sbumpc(void)" (?sbumpc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@QEAAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git
_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: int __cdecl std::basic_streambuf<char,struct std::char_traits<char> >:
:sgetc(void)" (?sgetc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@QEAAHXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_r
epo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: int __cdecl std::basic_streambuf<char,struct std::char_traits<char> >:
:sputc(char)" (?sputc@?$basic_streambuf@DU?$char_traits@D@std@@@std@@QEAAHD@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_
repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: __int64 __cdecl std::ios_base::width(__int64)" (?width@ios_base@std@@Q
EAA_J_J@Z) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_mode
l.vcxproj]
libcpmt.lib(xlocale.obj) : error LNK2005: "public: __int64 __cdecl std::ios_base::width(void)const " (?width@ios_base@std
@@QEBA_JXZ) 已经在 msvcprt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_mod
el.vcxproj]
libcpmt.lib(xstol.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_ma
in.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xstoul.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_m
ain.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xstoll.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_m
ain.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xstoull.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_
main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlock.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_ma
in.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlock.obj) : error LNK2005: "public: __cdecl std::_Lockit::_Lockit(int)" (??0_Lockit@std@@QEAA@H@Z) 已经在 msvcp
rt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xlock.obj) : error LNK2005: "public: __cdecl std::_Lockit::~_Lockit(void)" (??1_Lockit@std@@QEAA@XZ) 已经在 msvc
prt.lib(MSVCP140.dll) 中定义 [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xstrcoll.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark
_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xdateord.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark
_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xwcscoll.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark
_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xwcsxfrm.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark
_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xstrxfrm.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark
_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(xmtx.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(benchmark_mai
n.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(StlCompareStringA.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(
benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(StlCompareStringW.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(
benchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(StlLCMapStringW.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(be
nchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
libcpmt.lib(StlLCMapStringA.obj) : error LNK2038: 检测到“RuntimeLibrary”的不匹配项: 值“MT_StaticRelease”不匹配值“MD_DynamicRelease”(be
nchmark_main.obj 中) [D:\git_repo\temp\tensorflow-src\build_tflite\tools\benchmark\benchmark_model.vcxproj]
```
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} | Remove support for waiting on multiple streams for `WaitForStreamsThunk` and `BarrierCmd`.
Now that we no longer need to emit barriers for `operation_queue_ids`, we can remove this functionality and simplify much of the code.
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/e3f79a407fd9bb3411ccda2d94893efe/69290_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69290\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69290\">No</a>\n"
] | "2024-06-06T01:34:07" | "2024-06-10T06:04:08" | "2024-06-07T00:27:51" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyAddSign triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyAddSign(var=var.handle, m=accum.handle, lr=lr, alpha=1,
sign_decay=1, beta=1, grad=grad,
use_locking=False)
```
### Relevant log output
```shell
2024-06-06 01:33:21.409990: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:33:21.443054: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:33:22.498992: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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] | null | [] | "2024-06-06T01:31:52" | "2024-06-10T07:29:12" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyAdaMax/tf.raw_ops.ResourceApplyAdam/tf.raw_ops.ResourceApplyAdamWithAmsgrad triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyAdaMax(var=var.handle, m=accum.handle, v=accum2.handle, beta1_power=10,
lr=lr, beta1=1, beta2=1,
epsilon=0.9, grad=grad,
use_locking=False)
# crash
# tf.raw_ops.ResourceApplyAdam(var=var.handle, m=accum.handle, v=accum2.handle, beta1_power=10,beta2_power=10,
# lr=lr, beta1=1, beta2=1,
# epsilon=0.9, grad=grad,
# use_locking=False, use_nesterov=False)
# crash
# tf.raw_ops.ResourceApplyAdamWithAmsgrad(var=var.handle, m=accum.handle, v=accum2.handle,vhat=var.handle,
# beta1_power=10,
# beta2_power=10, lr=lr,
# beta1=1, beta2=2,
# epsilon=0.9, grad=grad,
# use_locking=False)
```
### Relevant log output
```shell
2024-06-06 01:30:44.494465: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:30:44.527542: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:30:45.578367: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/a279ab73a9514adbe2a349c98bff883b/69288_2-15-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69288\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69288\">No</a>\n"
] | "2024-06-06T01:28:34" | "2024-06-07T00:28:15" | "2024-06-07T00:28:11" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyCenteredRMSProp/tf.raw_ops.ResourceSparseApplyCenteredRMSProp triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
tf.raw_ops.ResourceApplyProximalAdagrad also has the same crash.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceSparseApplyProximalAdagrad(var=var.handle, accum=accum.handle, lr=lr,
l1=0.1, l2=0.2, grad=grad,
indices=tf.constant([1,2,2]),
use_locking=False)
# crash
# tf.raw_ops.ResourceApplyProximalAdagrad(var=var.handle, accum=accum.handle, lr=lr, l1=1,
# l2=1, grad=grad,
# use_locking=False)
```
### Relevant log output
```shell
2024-06-06 01:28:02.747576: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:28:02.780112: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:28:03.819782: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/99e902a62e82195ece67fa0ebcfa3e77/69286_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | "2024-06-06T01:24:46" | "2024-06-11T09:48:39" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyCenteredRMSProp/tf.raw_ops.ResourceSparseApplyCenteredRMSProp triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceSparseApplyCenteredRMSProp(var=var.handle, mg=accum.handle, ms=accum2.handle, mom=var.handle,
lr=lr, rho=lr,
momentum=momentum,
epsilon=0.9, grad=tf.constant([0.1, 0.2, 0.3]),
indices=tf.constant([1,2,2]),
use_locking=False)
# crash
# tf.raw_ops.ResourceApplyCenteredRMSProp(var=var.handle, mg=accum.handle, ms=accum2.handle, mom=var.handle,
# lr=lr, rho=lr,
# momentum=momentum,
# epsilon=0.9, grad=tf.constant([0.1, 0.2, 0.3]),
# use_locking=False)
```
### Relevant log output
```shell
2024-06-06 01:23:02.820585: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:23:02.853830: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:23:03.911976: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n\r\nI was able to reproduce the issue on Colab using TF [v2.16](https://colab.research.google.com/gist/sushreebarsa/5cec0f7080f1672e116f91f6f9cc72a2/69285.ipynb\r\n) and TF-[nightly](https://colab.research.google.com/gist/sushreebarsa/1aa2c02a796f7ca238a92fa601e8d0e7/69285.ipynb#scrollTo=M3Qm-JLCymRs) ,Please find the gist there for reference.\r\nThank you!"
] | "2024-06-06T01:19:37" | "2024-06-11T10:19:31" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyAdagrad/tf.raw_ops.ResourceApplyAdagradDA/tf.raw_ops.ResourceApplyAdagradV2 triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyAdagrad(
var=var.handle,
accum=accum.handle,
lr=lr,
grad=grad,
use_locking=False,
)
# crash
# tf.raw_ops.ResourceApplyAdagradDA(var=var.handle,
# gradient_accumulator=accum.handle,
# gradient_squared_accumulator=accum2.handle,
# grad=grad, lr=lr,
# l1=lr, l2=lr, global_step=1000,
# use_locking=False)
# crash
# tf.raw_ops.ResourceApplyAdagradV2(var=var.handle,
# accum=accum.handle,
# epsilon = 0.9,
# grad=grad, lr=lr,
# use_locking=False,update_slots=False)
```
### Relevant log output
```shell
2024-06-06 01:18:44.797518: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:18:44.829812: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:18:45.875807: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/bf69d15be75adf7ec38d0dce4b4c0747/69284_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | "2024-06-06T01:15:35" | "2024-06-11T04:53:48" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceSparseApplyAdagrad/tf.raw_ops.ResourceSparseApplyAdagradDA/tf.raw_ops.ResourceSparseApplyAdagradV2 triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceSparseApplyAdagrad(
var=var.handle,
accum=accum.handle,
lr=lr,
grad=grad,
indices=tf.constant([1,2,3]),
use_locking=False,
)
# crash only when gpu is available
# tf.raw_ops.ResourceSparseApplyAdagradDA(var=var.handle,
# gradient_accumulator=accum.handle,
# gradient_squared_accumulator=accum2.handle,
# grad=grad, indices=tf.constant([1,2,3]), lr=lr,
# l1=lr, l2=lr, global_step=1000,
# use_locking=False)
# crash
# tf.raw_ops.ResourceSparseApplyAdagradV2(var=var.handle,
# accum=accum.handle,
# epsilon = 0.9,
# grad=grad, indices=tf.constant([1,2,3]), lr=lr,
# use_locking=False,update_slots=False)
```
### Relevant log output
```shell
2024-06-06 01:14:57.455749: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:14:57.489757: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:14:58.552846: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/6576162b24c1f255fe89e3c5d5448b96/69283_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | "2024-06-06T01:10:53" | "2024-06-11T09:39:37" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceSparseApplyAdadelta/tf.raw_ops.ResourceApplyAdadelta triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceSparseApplyAdadelta(
var=var.handle,
accum=accum.handle,
accum_update=accum2.handle,
lr=lr,
rho=0.9,
epsilon=0.9,
grad=grad,
indices=tf.constant([1,2,2]),
use_locking=False,
)
# crash
# tf.raw_ops.ResourceApplyAdadelta(
# var=var.handle,
# accum=accum.handle,
# accum_update=accum2.handle,
# lr=lr,
# rho=0.9,
# epsilon=0.9,
# grad=grad,
# use_locking=False,
# )
```
### Relevant log output
```shell
2024-06-06 01:10:09.399119: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-06 01:10:09.432483: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:10:10.501032: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"@x0w3n I was able to replicate the issue reported here using TF [v2.16](https://colab.research.google.com/gist/sushreebarsa/e83f09c20b67c474866a7a6d165ac835/69281.ipynb#scrollTo=7OK58fNuo1mD) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/24a762d4f3f5feda5c4a69563f4e0347/69281.ipynb).\r\nThank you!"
] | "2024-06-06T01:07:46" | "2024-06-11T09:55:20" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyRMSProp/tf.raw_ops.ResourceSparseApplyRMSProp triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyRMSProp(
var=var.handle,
ms=accum.handle,
mom=accum2.handle,
lr=lr,
rho=lr,
momentum=momentum,
epsilon=0.9,
grad=grad,
use_locking=False,
)
# tf.raw_ops.ResourceSparseApplyRMSProp(
# var=var.handle,
# ms=accum.handle,
# mom=accum2.handle,
# lr=lr,
# rho=lr,
# momentum=momentum,
# indices=tf.constant([1,2,2]),
# epsilon=0.9,
# grad=grad,
# use_locking=False,
# )
```
### Relevant log output
```shell
2024-06-06 01:07:00.757682: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:07:01.835541: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/ed408a56233362e9701c61bd56e5d7ea/69280_2-15-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69280\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69280\">No</a>\n"
] | "2024-06-06T01:05:06" | "2024-06-06T08:36:54" | "2024-06-06T08:36:52" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyPowerSign triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. The cause of the crash may be the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyPowerSign(var=var.handle, m=accum.handle, lr=lr, logbase=lr,
sign_decay=lr, beta=lr, grad=grad,
use_locking=False)
```
### Relevant log output
```shell
2024-06-06 01:04:36.157948: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 01:04:37.219223: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/007a03f4964b1cbf66b50026411a9f96/69279_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | "2024-06-06T01:01:58" | "2024-06-11T09:33:32" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyKerasMomentum/tf.raw_ops.ResourceSparseApplyKerasMomentum triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. We analyzed it, and the cause of this crash is probably the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyKerasMomentum(
var=var.handle,
accum=accum.handle,
grad=grad,
lr=lr,
momentum=0.1,
use_locking=False,
use_nesterov=False,
name=None
)
# crash
# tf.raw_ops.ResourceSparseApplyKerasMomentum(
# var=var.handle,
# accum=accum.handle,
# grad=grad,
# lr=lr,
# indices=tf.constant([1,2,2]),
# momentum=0.1,
# use_locking=False,
# use_nesterov=False,
# name=None
# )
```
### Relevant log output
```shell
2024-06-06 00:59:43.994008: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 00:59:44.992853: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"@x0w3n I was able to replicate the issue reported here using TF [v2.16](https://colab.research.google.com/gist/sushreebarsa/a34c03262d64b9793776ed5c63ce1274/69278.ipynb) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/3a018ca3afef26d92f48b75f04f57188/69278.ipynb#scrollTo=e88vHySgckhO).\r\nThank you!"
] | "2024-06-06T00:53:19" | "2024-06-11T09:26:28" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceApplyFtrl/tf.raw_ops.ResourceApplyFtrlV2/tf.raw_ops.ResourceSparseApplyFtrl/tf.raw_ops.ResourceSparseApplyFtrlV2 triggers "Aborted (core dumped)". We ran the code on colab's latest TensorFlow Nightly, which also triggers the crash. We analyzed it, and the cause of this crash is probably the grad parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceApplyFtrl(
var=var.handle,
accum=accum.handle,
linear=var.handle,
grad=grad,
lr=lr,
l1=0.1,
l2=0.1,
lr_power=1,
use_locking=False,
multiply_linear_by_lr=False,
name=None
)
# crash
# tf.raw_ops.ResourceApplyFtrlV2(
# var=var.handle,
# accum=accum.handle,
# linear=var.handle,
# grad=grad,
# lr=lr,
# l1=0.1,
# l2=0.1,
# lr_power=1,
# l2_shrinkage=1,
# use_locking=False,
# multiply_linear_by_lr=False,
# name=None
# )
# crash
# tf.raw_ops.ResourceSparseApplyFtrl(
# var=var.handle,
# accum=accum.handle,
# linear=var.handle,
# grad=grad,
# lr=lr,
# l1=0.1,
# l2=0.1,
# lr_power=-1,
# use_locking=False,
# indices=tf.constant([1,2,2]),
# multiply_linear_by_lr=False,
# name=None
# )
# crash
# tf.raw_ops.ResourceSparseApplyFtrlV2(
# var=var.handle,
# accum=accum.handle,
# linear=var.handle,
# grad=grad,
# lr=lr,
# l1=0.1,
# l2=0.1,
# lr_power=-1,
# use_locking=False,
# indices=tf.constant([1,2,2]),
# multiply_linear_by_lr=False,
# l2_shrinkage=1,
# name=None
# )
```
### Relevant log output
```shell
2024-06-06 00:53:09.467458: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 00:53:10.440397: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/3803d5ae106ddd0467235c62249970d2/69277_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69277\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69277\">No</a>\n"
] | "2024-06-06T00:45:15" | "2024-06-06T08:36:38" | "2024-06-06T08:36:36" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.ResourceSparseApplyMomentum triggers "Aborted (core dumped)".
tf.raw_ops.ResourceApplyMomentum also has the same crash.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
accum2 = tf.Variable([0, 1.2, -3.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
# crash
tf.raw_ops.ResourceSparseApplyMomentum(var=var.handle,
accum=accum.handle,
grad=grad,
lr=lr,
indices=tf.constant([1,2,2]),
momentum=0.1,
use_locking=False,
use_nesterov=False,
name=None)
# crash
# tf.raw_ops.ResourceApplyMomentum(var=var.handle, accum=accum.handle, lr=lr, grad=grad, momentum=momentum)
```
### Relevant log output
```shell
2024-06-06 00:45:01.131952: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-06 00:45:02.203082: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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} | [XLA:LAYOUT_ASSIGNMENT] Do not crash on repeated all reduce channel ids with
different shapes.
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} | [XLA:MSA] Add a runtime simulator to predict the HLO module execution time for a given memory space assignment.
The runtime simulator takes into account the number of trip counts of outer loops when calculating the execution time of an instruction. This is important for nested loops, where the execution time of an instruction can be significantly affected by the number of times it is executed. Before this update, the existing simulator assumes all nested layers have the same trip count, which is a user-provided configuration (default: 5).
This patch implements a new simulator which uses static analysis to get the trip count for each nested layer, and product them to get the total trip counts.
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} | Use `stablehlo::getMinimumVersion()` for `HloProgram` serialization
Using `stable::getCurrentVersion()` provides ~no compatibility guarantees. We should use the version that guarantees version compatibility due to the IFRT Proxy requirements.
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/69269/checks?check_run_id=25864858103) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @Realtech1 This looks like spam. Please don't spam. Thank you!\r\nCc @mihaimaruseac"
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} | Integrate LLVM at llvm/llvm-project@e282118f4715
Updates LLVM usage to match
[e282118f4715](https://github.com/llvm/llvm-project/commit/e282118f4715)
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} | Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 17 -> 17
Patch: 0 -> 0
WARNING: Below are potentially instances of lingering old version string
"2.17.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/tensorflow.bzl:93:2.17.0
tensorflow/tools/pip_package/setup.py:51:2.17.0
tensorflow/tools/pip_package/setup.py:117:2.17.0
tensorflow/lite/tools/versioning/runtime_version.cc:135:2.17.0
Binary file tensorflow/lite/testdata/string_input_model_with_signature.bin
matches
WARNING: Below are potentially instances of lingering old version string
"2.17.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/tensorflow.bzl:93:2.17.0
tensorflow/tools/pip_package/setup.py:51:2.17.0
tensorflow/tools/pip_package/setup.py:117:2.17.0
tensorflow/lite/tools/versioning/runtime_version.cc:135:2.17.0
Binary file tensorflow/lite/testdata/string_input_model_with_signature.bin
matches
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"@erik-fauna Bazel allows for conditional build rules based on configurations like target architecture. It's possible the archive creation step is defined only for specific architectures (e.g., amd64). Kindly search for conditional statements (\"if\", \"select\") within the Bazel build files in the lib_package folder.\r\n\r\n```\r\nbazel build --verbose_failures //lib_package:your_target\r\n\r\n```\r\nAlso try to replace `your_target` with the specific build target defined in the Bazel files.\r\nThank you!",
"I see that there are some differences in the build commands defined in the lib_package, so I've made some adjustments to my build line. It should finish building by the morning, so I can report back then. I added the verbose_failures flag as well.",
"@erik-fauna Yes, please! Let us know kindly.\r\nThank you!",
"This worked to get the tar.gz archive. `../bazel-6.5.0-linux-arm64 build --verbose_failures --jobs=12 --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=1\" -c opt //tensorflow/tools/lib_package:libtensorflow` For anyone who comes across this looking for number of jobs, bazel maxes out at 8. \r\n\r\nI guess rather than just building //tensorflow:libtensorflow.so, the entire library package was needed. I will eventually go and checkout 2.15 and see if the package for that builds successfully (with bazel 6.1.0), because that failed when trying to build the .so file directly."
] | "2024-06-05T20:37:39" | "2024-06-11T14:02:37" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04 (arm64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.5.0
### GCC/compiler version
gcc
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The readme within the the [lib_package](https://github.com/tensorflow/tensorflow/tree/r2.16/tensorflow/tools/lib_package) folder indicates that a tar.gz archive should be created upon building with bazel to finish installation. Currently trying to build on arm64 architecture (jetson orin) as there are no docker images on dockerhub for Tensorflow C with that architecture. I would have preferred to use 2.15 to stay the same as the version used by the [amd64 desktops](https://www.tensorflow.org/install/lang_c), but that failed to build because of stubs being too large (using bazel 6.1.0, gcc, cxxopt flag used).
There are no logs because the build claims it completed successfully, there are the usual shared library files within `bazel-bin/tensorflow`, but porting them and all the dependencies to the right locations sounds like a nightmare.
Any thoughts why the archive isn't being built?
### Standalone code to reproduce the issue
```shell
cd ~/workspace
wget https://github.com/bazelbuild/bazel/releases/download/6.5.0/bazel-6.5.0-linux-arm64
chmod +x bazel-6.5.0-linux-arm64
git clone https://github.com/tensorflow/tensorflow
cd ./tensorflow
git checkout r2.16 #may need to change the number of jobs that can run at once
../bazel-6.5.0-linux-arm64 build --jobs=12 --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" -c opt //tensorflow:libtensorflow.so //tensorflow:libtensorflow_cc.so //tensorflow:libtensorflow_framework.so //tensorflow:install_headers
```
### Relevant log output
_No response_ | {
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} | PR #13392: Remove Redundant Shape Comparison in Norm Rewriter
Imported from GitHub PR https://github.com/openxla/xla/pull/13392
Removes a superfluous comparison of the shapes of the input and scale when matching and rewriting layer norm patterns.
Copybara import of the project:
--
f126d2a142015f2b36d8845bdfc991d1302aa57c by Philipp Hack <phack@nvidia.com>:
Removes a redundant shape comparison in the norm rewriter.
Merging this change closes #13392
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13392 from philipphack:u_layer_shape_xla f126d2a142015f2b36d8845bdfc991d1302aa57c
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} | Fix profiler utils blocked queue iterator arrow operator
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} | [XLA] [NFC] Do not duplicate flags in tooling for attributes we already have attached to HloModuleConfig
For --xla_dump_to and others, we already have flags auto-generated from
xla.proto, let's just use those instead of creating a new set of flags for each
binary, and then passing attributes across structs.
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This is needed to be able to build SymbolicTileAnalysis in Cost Model.
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"@cpappasILMX Please make sure that Android NDK has a version compatible with your target Android API level.\r\nBazel build system. Could you check TensorFlow [documentation](https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/gpu/package-summary) for specific version requirements for these tools for building the GPU Delegate?\r\nThank you!"
] | "2024-06-05T17:31:46" | "2024-06-10T09:41:14" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### Custom code
No
### OS platform and distribution
Android
### Mobile device
Android
### Python version
3.9
### Bazel version
7.1.2
### GCC/compiler version
Apple Clang 15.0.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On a Mac machine, I am trying to build the tensorflow-lite gpu delegate for android. I have followed the build instructions without docker found at https://www.tensorflow.org/lite/android/lite_build to build tflite for android and was successful. I attempted to build the native gpu delegate as a shared library as well following the instructions found at https://www.tensorflow.org/lite/android/delegates/gpu_native. The goal is to build the delegate as a shared library to simply copy over into my own project.
The problem appears to be when building a dependency of the delegate, dependency declarations are missing.
### Standalone code to reproduce the issue
```shell
TF_LITE_TARGET_NDK_VERSION_DIRECTORY=$(ls -1 /Users/$USER/Library/Android/sdk/ndk/ | tail -n 1)
export ANDROID_NDK_HOME="/Users/$USER/Library/Android/sdk/ndk/26.3.11579264"
export ANDROID_SDK_API_LEVEL="34"
export ANDROID_NDK_API_LEVEL="26"
# AndroidConfiguration is a simple text file to auto fill the queries from the provided configure script
./configure < AndroidConfiguration
bazel build -c opt --config android_arm64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so
```
### Relevant log output
```shell
/private/var/tmp/_bazel_user/897db67e3896f5db6d832aa86833dcea/external/XNNPACK/BUILD.bazel:2884:19: Compiling src/operators/convolution-nchw.c failed: undeclared inclusion(s) in rule '@XNNPACK//:operators':
this rule is missing dependency declarations for the following files included by 'src/operators/convolution-nchw.c':
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/limits.h'
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/float.h'
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/stdbool.h'
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/stddef.h'
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/stdint.h'
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/stdarg.h'
'external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/lib/clang/17/include/inttypes.h'
Target //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so failed to build
```
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`mlir::stablehlo::deserializePortableArtifact` returns nullptr if parsing fails, which we need to handle explicitly. Also, `ScopedDiagnosticHandler` is used to propagate error messages from MLIR to the caller.
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needs to fill in the internal release notes for this version before the PR gets
submitted. Click on the :pencil2: icon in the header for `RELEASE.md` under
"Files Changed" above. | {
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Also update the verifier to be aware of these changes. Add nested fusion cost handling for dynamic update slice.
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} | PR #13254: [NVIDIA] Support pipelining collectives in backward direction with loop invariant instructions in chain
Imported from GitHub PR https://github.com/openxla/xla/pull/13254
In some quantization patterns like:
```
constant
|
broadcast
/ \
/ \
clamp clamp
/ \
dot all-gather
```
The all-gathers cannot be pipelined due to the shared operand broadcast having multiple users and not all users are in all-gather's operand chain. The backward pipelining restricts this situation to avoiding exploding data dependencies across loop iterations.
An exception can be made if such operand chain only contains loop invariant instructions in this case: broadcast and constant.
This pr introduces a config to collective pipeliner backward direction to allow loop invariant instructions to be in the operand chain.
Copybara import of the project:
--
eeea881d084934214b6e2e34319cb3ff4fed9c1e by TJ <tjx@nvidia.com>:
Support pipelining collectives in backward direction with loop invariant instructions in chain
--
977e713643ae08337ba61ab15fe7812f95078900 by TJ <tjx@nvidia.com>:
Reduced unit test complexity
Merging this change closes #13254
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13254 from Tixxx:tixxx/pipeliner_improve 977e713643ae08337ba61ab15fe7812f95078900
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} | [XLA:GPU] Make `cuda_hlo_runner_main` also work for ROCM.
Rename it to `hlo_runner_main_gpu`.
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"[original issue](https://github.com/tensorflow/tensorflow/issues/62610)..\r\nIt works fine in 2.15.0"
] | "2024-06-05T15:07:55" | "2024-06-06T04:49:19" | null | NONE | null | null | null | ### 1. System information
- OS Platform and Distribution : Linux Ubuntu 22.04
- TensorFlow installation (pip package or built from source): pip package
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.16.1
### 2. Code
```
import tensorflow as tf
import pdb
print("TensorFlow version:", tf.__version__)
class network(tf.keras.Model):
def __init__(self):
super().__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=2, kernel_size=3, strides=2, activation='relu')
def call(self, x):
x = self.conv1(x)
return x
model = network()
inp = tf.keras.Input(shape=(28, 28, 1))
out = model(inp)
model.build(input_shape=(None, 28, 28, 1))
model.summary()
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tfliteModel = converter.convert()
```
### Output
```
LLVM ERROR: Failed to infer result type(s).
Aborted (core dumped
```
[ouptut.txt](https://github.com/user-attachments/files/15592694/ouptut.txt)
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Tensorflow lite v2.16.1
Hexagon library 1.20.0.1
**Standalone code to reproduce the issue**
I generated efficientdet normal and lite int8 models from automl github.
https://github.com/google/automl/blob/master/efficientdet/tf2/tutorial.ipynb
The models work well on CPU, but do not work on hexagon delegate.
When I put the same input value, the output of the hexagon delegate is invalid value.
**Any other info / logs**
I attached the efficientdet lite0 int8 model as a sample.
https://1drv.ms/u/s!AnqHHtrBqwyUg8N31bJmQjMmrF9QnA?e=lmvIdI
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} | PR #13108: [GPU] Shard GEMM fusion autotuning across multiple compilation processes.
Imported from GitHub PR https://github.com/openxla/xla/pull/13108
This solves the problem of compiler processes compiling different programs due to fluctuations of measurements during autotuning and in addition makes some cases faster (depending on the number of fusions and the availability of CPU cores).
Copybara import of the project:
--
73a35a3dc21435061f86e53c02418cc69097e57e by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Shard GEMM fusion autotuning across compiler processes.
--
22201078fadf807dbe00b0f4c3db83f9f015999f by Ilia Sergachev <isergachev@nvidia.com>:
[NFC] Refactor CreateGpuClient().
Merging this change closes #13108
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13108 from openxla:parallel_autotuning 22201078fadf807dbe00b0f4c3db83f9f015999f
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"@x0w3n I tried to replicate the issue reported using tf-nightly and faced the following error rather than core dumped issue. \r\n```\r\nInvalidArgumentError: {{function_node __wrapped__DrawBoundingBoxesV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} The rank of the images should be 4 [Op:DrawBoundingBoxesV2] name: \r\n```\r\nPlease find the gist [here](https://colab.research.google.com/gist/sushreebarsa/9dc92a428a14abd9e3a433dd95ab5d39/69232.ipynb#scrollTo=YUSZ3wQ5a9HV).\r\nThank you! "
] | "2024-06-05T12:32:45" | "2024-06-11T08:36:25" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A crash is triggered when tf.compat.v1.image.draw_bounding_boxes is given a specific input, the APIs affected by this are as follows：
1. tf.compat.v1.image.draw_bounding_boxes(images, boxes, colors=colors)
2. tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors)
3. tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes)
4. tf.image.draw_bounding_boxes(images, boxes, colors=colors)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
images = tf.random.normal(shape=(2, 3, 4))
boxes = tf.random.normal(shape=(2, 2, 4))
colors = tf.random.normal(shape=(2, 4))
result = tf.compat.v1.image.draw_bounding_boxes(images, boxes, colors=colors)
# crash
# output = tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors)
# crash
# output = tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes)
# crash
# result = tf.image.draw_bounding_boxes(images, boxes, colors=colors)
```
### Relevant log output
```shell
2024-06-05 12:32:06.850044: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-05 12:32:07.838677: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
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} | Improve reduction vectorization.
- v4 loads should not be used with f32 or larger inputs. I measured
significant performance degradation from this.
- stop relying on unrolling for vectorization, so we can
remove the MayPreventVectorization heuristic. This enables
more vectorization, e.g. of variadic reductions.
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.16.1 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/339ff14afb0d1eab6d1a3c0dbdc4697d/69229_2-16-1-nightly-v.ipynb) here for reference.\r\n\r\nThank you!",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69229\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69229\">No</a>\n"
] | "2024-06-05T11:32:26" | "2024-06-06T08:34:17" | "2024-06-06T08:34:15" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Assertion failure when entering boundary values for tf.signal.irfft/irfft2d/irfft3d parameters and trigger crash
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
x = np.array([[[0]]])
y = tf.signal.irfft(x) # crash
# y = tf.signal.irfft2d(x) # crash
# y = tf.signal.irfft3d(x) # crash
```
### Relevant log output
```shell
2024-06-05 11:32:13.365512: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
DUCC FFT c2r failed:
bazel-out/k8-opt/bin/external/ducc/_virtual_includes/fft/ducc/src/ducc0/fft/fft1d_impl.h: 2948 (static Trpass<Tfs> ducc0::detail_fft::rfftpass<float>::make_pass(size_t, size_t, size_t, const Troots<Tfs> &, bool) [Tfs = float]):
Assertion failure
no zero-sized FFTs
Aborted (core dumped)
```
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} | Fix float to int conversion in mlir emitters.
The float to signed int conversion was there previously, but removed because I
thought it wasn't needed. Turns out that the float_conversions_test covers this
logic, and it is indeed needed. Also port the logic for float to unsigned int
conversion from elemental_ir_emitter and add a test.
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This is a preparation for future changes to SymbolicTileAnalysis.
I plan to make it possible to build SymbolicTileAnalysis from HloFusionAdaptor, so it can be used in Priority Fusion. By design, HloFusionAdaptor should only consist of fusion computations. This change makes sure that we don't pass HloComputation that is not a fusion.
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Follow up changes will wire it into JAX.
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I will remove PyRegisterCustomCallTarget from xla_extension in a follow up,
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/140c866714fb864890903cb3a5380a36/69221_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69221\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69221\">No</a>\n"
] | "2024-06-05T10:49:00" | "2024-06-10T06:03:18" | "2024-06-07T00:26:24" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Triggers crash when setting boundary values for the indices parameter of tf.scatter_nd.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
indices = tf.constant([[4], [3], [1], [7]], dtype=tf.int32)
updates = tf.constant([9, 10, 11, 12], dtype=tf.int32)
shape = tf.cast([8], dtype=tf.int32)
scatter_nd = tf.scatter_nd(tf.expand_dims(indices, 1), updates, shape)
```
### Relevant log output
```shell
2024-06-05 10:47:19.247456: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-05 10:47:20.277026: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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"@x0w3n I was able to replicate the issue reported here using TF [v2.16](https://colab.research.google.com/gist/sushreebarsa/fb81f8b4fc00adf53d71eb4c09b33208/69220-nightly.ipynb) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/28d977cff6a884ff9c4a13ed84202b4c/69220-nightly.ipynb).\r\nThank you!"
] | "2024-06-05T10:42:09" | "2024-06-11T07:29:43" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A crash is triggered when an unintended value is passed to the input parameter of the tf.raw_ops.QuantizeAndDequantizeV3 function.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = tf.random.uniform(shape=[2, 1], maxval=9.0)
input_min = tf.constant([0, 1.0], dtype=tf.dtypes.float32)
input_max = tf.constant([1.0], dtype=tf.dtypes.float32)
# crash
tf.raw_ops.QuantizeAndDequantizeV3(input=input_data, input_min=input_min, input_max=input_max, num_bits=8, signed_input=True, range_given=True, narrow_range=False, axis=(- 1))
```
### Relevant log output
```shell
2024-06-05 10:39:35.285635: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-05 10:39:36.361982: F tensorflow/core/framework/tensor.cc:852] Check failed: 1 == NumElements() (1 vs. 2)Must have a one element tensor
Aborted (core dumped)
```
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"Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.15 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/bb0860f8ca2f02755ff47c36b6857502/69219_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!\r\n",
"Thank you for your response.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69219\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69219\">No</a>\n"
] | "2024-06-05T10:38:33" | "2024-06-06T08:35:51" | "2024-06-06T08:35:48" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A crash is triggered when input some boundary values to the range_max parameter of the tf.raw_ops.ThreadUnsafeUnigramCandidateSampler/LearnedUnigramCandidateSampler function.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
true_classes = np.random.randint(0, 1000, size=(10, 1))
num_true = 1
num_sampled = 10
unique = True
range_max = (2 ** 32)
seed = 0
seed2 = 0
# crash
tf.raw_ops.ThreadUnsafeUnigramCandidateSampler(true_classes=true_classes, num_true=num_true, num_sampled=num_sampled, unique=unique, range_max=range_max, seed=seed, seed2=seed2)
# crash
# tf.raw_ops.LearnedUnigramCandidateSampler(true_classes=true_classes, num_true=num_true, num_sampled=num_sampled, unique=unique, range_max=range_max, seed=seed, seed2=seed2)
```
### Relevant log output
```shell
2024-06-05 10:33:44.105929: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-05 10:33:45.163653: F tensorflow/core/kernels/range_sampler.cc:183] Check failed: range < kint32max (4294967296 vs. 2147483647)
Aborted (core dumped)
```
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- Basic unroller.
- Basic pipeliner.
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"@x0w3n,\r\nLooks like there is another issue raised for the same API mentioning that it is also aborted. This is a duplicate of issue [#66759](https://github.com/tensorflow/tensorflow/issues/66759). Can you please check and close this issue, since it is already being tracked there? \r\n\r\nThank you!\r\n",
"I've noticed this. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69215\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69215\">No</a>\n"
] | "2024-06-05T09:40:24" | "2024-06-05T12:28:28" | "2024-06-05T12:28:24" | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A crash is triggered when input some boundary values to the gradients or inputs parameter of the tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient function. the affected APIs are listed below:
1. tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel
2. tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient
3. tf.quantization.fake_quant_with_min_max_vars_per_channel
4. tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
min_data = tf.constant([0.0, 1.0, 2.0])
max_data = tf.constant([1.0, 2.0, 3.0])
input_data = tf.random.normal(shape=(),dtype=tf.float32)
# crash
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=input_data, min=0, max=3)
# crash
# tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient(gradients=input_data, inputs=input_data, min=2, max=2, num_bits=8, narrow_range=False, name=None)
# crash
# tf.quantization.fake_quant_with_min_max_vars_per_channel(inputs=input_data, min=2, max=2, num_bits=8, narrow_range=False, name=None)
# crash
# tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=input_data,inputs=input_data, min=2, max=2, num_bits=8, narrow_range=False, name=None)
```
### Relevant log output
```shell
2024-06-05 09:38:08.909464: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-05 09:38:09.950624: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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} | [XLA:GPU] [NFC] Merge flag parsing logic from hlo_runner_flags into hlo_runner_main
Having it in a separate file just makes it non-obvious which flags are used (as
there's only one user), and complicates error handling. Furthermore, it hides
the fact that some flags are duplicated.
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"@x0w3n I was able to replicate the issue reported using TF [2.16](https://colab.research.google.com/gist/sushreebarsa/448cdd28d822c0c7ff9f1bd4cadab141/69213-nightly.ipynb) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/20944b00e753931560aa668a254b4f30/69213-nightly.ipynb).\r\nThank you! \r\n"
] | "2024-06-05T09:19:56" | "2024-06-11T07:01:53" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A crash is triggered when some boundary values are passed to the perm parameter of these transpose functions. the affected APIs are as follows:
1. tf.transpose
2. tf.experimental.numpy.transpose
3. tf.compat.v1.transpose
4. tf.keras.ops.transpose
5. tf.raw_ops.transpose
6. tf.raw_ops.ConjugateTranspose
tf.raw_ops.ConjugateTranspose is a Segmentation fault crash, all other API crashes are Aborted.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
x = tf.random.normal(shape=(3, 3))
# crash
x = tf.transpose(x, perm=((- 1), (- 2)))
# crash
# tf.experimental.numpy.transpose(
# x, axes=((- 1), (- 2))
# )
# crash
# tf.compat.v1.transpose(
# x, perm=((- 1), (- 2)), name='transpose', conjugate=False
# )
# crash
# tf.keras.ops.transpose(
# x, axes=((- 1), (- 2))
# )
# crash
# tf.raw_ops.ConjugateTranspose(
# x=x, perm=((- 1), (- 2))
# )
# crash
# tf.raw_ops.Transpose(
# x=x, perm=((- 1), (- 2))
# )
```
### Relevant log output
```shell
2024-06-05 09:16:55.482645: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-05 09:16:56.526139: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
2024-06-05 09:17:47.967585: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Segmentation fault (core dumped)
```
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Updates LLVM usage to match
[e282118f4715](https://github.com/llvm/llvm-project/commit/e282118f4715)
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"Hi **@PhyllisJi** ,\r\n- Sorry for the delay, Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).\r\nThank you!"
] | "2024-06-05T03:28:56" | "2024-06-10T05:55:33" | null | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I request the addition of the groups parameter to the tf.keras.layers.Conv2DTranspose layer. This feature is crucial for the following reasons:
**Enhanced Model Flexibility and Efficiency:**
The groups parameter allows for the implementation of grouped transposed convolutions, which can significantly enhance model flexibility and computational efficiency. By splitting the input and output channels into separate groups, it enables the creation of more complex and diverse neural network architectures, such as those found in state-of-the-art models like ResNeXt and MobileNetV2.
**Consistency with Other Frameworks:**
PyTorch, a widely used deep learning framework, supports the groups parameter in its ConvTranspose2d layer. Including this feature in TensorFlow would align its functionality with PyTorch, facilitating easier migration of models between these frameworks and enhancing interoperability for developers and researchers.
**Optimization of Computational Resources:**
Grouped convolutions are known to reduce the number of parameters and computational load, which is particularly beneficial for deploying models on resource-constrained devices such as mobile phones and embedded systems. By supporting the groups parameter, TensorFlow can better cater to the needs of edge computing and real-time inference applications.
Broadening Application Scope:
The ability to use grouped transposed convolutions would broaden the scope of applications that TensorFlow can effectively handle, including tasks that require fine-grained control over the convolution operations, such as generative adversarial networks (GANs) and super-resolution models.
Adding groups support to the Conv2DTranspose layer would greatly enhance the usability and versatility of TensorFlow. It would provide developers with a powerful tool for building efficient and advanced neural network architectures, aligning TensorFlow with industry standards and best practices.
### Standalone code to reproduce the issue
```shell
Current：
tf.keras.layers.Conv1DTranspose(
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs
)
```
```
### Relevant log output
_No response_ | {
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"@ngostin,\r\nI tried to import the tensorflow_decision_forests in the latest version Tensorflow 2.16.1 and could not face any error/issue while importing. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/f42d057225455cc7203570d65bdeb58c/untitled1934.ipynb).\r\n```python\r\n!pip install tensorflow-decision-forests\r\n```\r\n\r\nAlso there is another repo for the TensorFlow decision forests for the quick resolution. Please try to post in that repo.\r\nhttps://github.com/tensorflow/decision-forests/issues\r\n\r\nThank you!"
] | "2024-06-05T02:31:54" | "2024-06-05T06:33:46" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Mac OS Big Sur 11.3.1
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I've been wrestling trying to install/implement some form of Decision Forests for weeks now, and keep running into issues with Both ydf and tf-df. Both issues seem to happen on import. For tf-df, I get a module not found error, relating to yggdrasil_decision_forests
(less related, the issue I'm having with ydf gives an error "no suitable image found" for ydf)
### Standalone code to reproduce the issue
```shell
### import required packages, define constants
import tensorflow_decision_forests as tfdf
```
### Relevant log output
```shell
2024-06-04 19:22:02.418757: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 10
7 import pandas as pd
9 #import ydf # Yggdrasil Decision Forests
---> 10 import tensorflow_decision_forests as tfdf
12 pi = np.pi
File ~/opt/anaconda3/lib/python3.9/site-packages/tensorflow_decision_forests/__init__.py:64
60 from tensorflow_decision_forests.tensorflow import check_version
62 check_version.check_version(__version__, compatible_tf_versions)
---> 64 from tensorflow_decision_forests import keras
65 from tensorflow_decision_forests.component import py_tree
66 from tensorflow_decision_forests.component.builder import builder
File ~/opt/anaconda3/lib/python3.9/site-packages/tensorflow_decision_forests/keras/__init__.py:53
15 """Decision Forest in a Keras Model.
16
17 Usage example:
(...)
48
49 """
51 from typing import Callable, List
---> 53 from tensorflow_decision_forests.keras import core
54 from tensorflow_decision_forests.keras import wrappers
56 # Utility classes
File ~/opt/anaconda3/lib/python3.9/site-packages/tensorflow_decision_forests/keras/core.py:62
60 from tensorflow.python.data.ops import dataset_ops
61 from tensorflow.python.data.ops import load_op
---> 62 from tensorflow_decision_forests.component.inspector import inspector as inspector_lib
63 from tensorflow_decision_forests.component.tuner import tuner as tuner_lib
64 from tensorflow_decision_forests.keras import core_inference
File ~/opt/anaconda3/lib/python3.9/site-packages/tensorflow_decision_forests/component/inspector/inspector.py:71
69 from yggdrasil_decision_forests.model import hyperparameter_pb2
70 from yggdrasil_decision_forests.model.decision_tree import decision_tree_pb2
---> 71 from yggdrasil_decision_forests.model.gradient_boosted_trees import gradient_boosted_trees_pb2
72 from yggdrasil_decision_forests.model.multitasker import multitasker_pb2
73 from yggdrasil_decision_forests.model.random_forest import random_forest_pb2
ModuleNotFoundError: No module named 'yggdrasil_decision_forests.model.gradient_boosted_trees'
```
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} | Add support for async operations in `CommandBuffers`.
This is now straightforward -- all we need to do is ensure that the `WaitForStreamsThunks` get lowered into appropriate `CommandBufferCmds`. There's already a `BarrierCmd` that *almost* works. I only had to add support for waiting on multiple streams instead of waiting on a single stream.
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