[DONOTMERGE-for internal code review only] Add logging messages - #1
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[DONOTMERGE-for internal code review only] Add logging messages#1smkarlap wants to merge 2 commits into
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Implements logging messages at INFO, WARNING and ERROR levels. Utilizes ONNX-RT's logging infrastrcture. Reversed IsGraphSupported check logic to facilitate logging.
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This patch defines log messages. "WARNING" and "ERROR" messages are enabled at all times, while "INFO" messages need be explicitly enabled by setting verbose option in the application code. |
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Build_CI pipeline triggered at: (https://gitlab.devtools.intel.com/iot-edge/onnxrt/onnxruntime_internal_ci/pipelines/286128) |
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Closing this PR and opening another one for upstreaming |
MaajidKhan
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Oct 26, 2021
* Edgchen1/objc api reference 1.9 (#1) * Update Objective-C API reference for 1.9. * Update spelling. * Test link changes. * Fix links. * Fix links and updates in mobile-performance-tuning.md. * Fix links in tutorials/mobile/custom-build.md. * Fix * Fix * Fix links in docs/tutorials/mobile/initial-setup.md. * Fix links. * Update note. * Add 1.9 op type support page. * Add nav info to page. * Rename files to not have spaces.
MaajidKhan
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Feb 15, 2022
…icrosoft#10260) * update java API for STVM EP. Issue is from PR#10019 * use_stvm -> use_tvm * rename stvm worktree * STVMAllocator -> TVMAllocator * StvmExecutionProviderInfo -> TvmExecutionProviderInfo * stvm -> tvm for cpu_targets. resolve onnxruntime::tvm and origin tvm namespaces conflict * STVMRunner -> TVMRunner * StvmExecutionProvider -> TvmExecutionProvider * tvm::env_vars * StvmProviderFactory -> TvmProviderFactory * rename factory funcs * StvmCPUDataTransfer -> TvmCPUDataTransfer * small clean * STVMFuncState -> TVMFuncState * USE_TVM -> NUPHAR_USE_TVM * USE_STVM -> USE_TVM * python API: providers.stvm -> providers.tvm. clean TVM_EP.md * clean build scripts #1 * clean build scripts, java frontend and others #2 * once more clean #3 * fix build of nuphar tvm test * final transfer stvm namespace to onnxruntime::tvm * rename stvm->tvm * NUPHAR_USE_TVM -> USE_NUPHAR_TVM * small fixes for correct CI tests * clean after rebase. Last renaming stvm to tvm, separate TVM and Nuphar in cmake and build files * update CUDA support for TVM EP * roll back CudaNN home check * ERROR for not positive input shape dimension instead of WARNING * update documentation for CUDA * small corrections after review * update GPU description * update GPU description * misprints were fixed * cleaned up error msgs Co-authored-by: Valery Chernov <valery.chernov@deelvin.com> Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru> Co-authored-by: Thierry Moreau <tmoreau@octoml.ai>
saipj
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Jun 8, 2022
…icrosoft#11732) This reverts commit 1f2c926. Because it makes our packaging pipeline crash Error message: [ RUN ] QLinearConvTest.Conv3D_S8S8_Depthwise Test #1: onnxruntime_test_all ...................Subprocess killed***Exception: 838.24 sec We haven't successfully reproduced the bug on a real ARM64 hardware. Currently we only saw it showed up with qemu. More investigations are on-going.
sfatimar
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Aug 28, 2023
### Description
Release OrtEnv before main function returns. Before this change, OrtEnv
is deleted when C/C++ runtime destructs all global variables in ONNX
Runtime's core framework.
The callstack is like this:
```
* frame #0: 0x00007fffee39f5a6 libonnxruntime.so.1.16.0`onnxruntime::Environment::~Environment(this=0x00007fffee39fbf2) at environment.h:20:7
frame #1: 0x00007fffee39f614 libonnxruntime.so.1.16.0`std::default_delete<onnxruntime::Environment>::operator()(this=0x00007ffff4c30e50, __ptr=0x0000000005404b00) const at unique_ptr.h:85:2
frame #2: 0x00007fffee39edca libonnxruntime.so.1.16.0`std::unique_ptr<onnxruntime::Environment, std::default_delete<onnxruntime::Environment>>::~unique_ptr(this=0x5404b00) at unique_ptr.h:361:17
frame #3: 0x00007fffee39e2ab libonnxruntime.so.1.16.0`OrtEnv::~OrtEnv(this=0x00007ffff4c30e50) at ort_env.cc:43:1
frame #4: 0x00007fffee39fa96 libonnxruntime.so.1.16.0`std::default_delete<OrtEnv>::operator()(this=0x00007fffefff8f78, __ptr=0x00007ffff4c30e50) const at unique_ptr.h:85:2
frame #5: 0x00007fffee39f394 libonnxruntime.so.1.16.0`std::unique_ptr<OrtEnv, std::default_delete<OrtEnv>>::~unique_ptr(this=0x7ffff4c30e50) at unique_ptr.h:361:17
frame #6: 0x00007ffff78574b5 libc.so.6`__run_exit_handlers + 261
frame #7: 0x00007ffff7857630 libc.so.6`exit + 32
frame #8: 0x00007ffff783feb7 libc.so.6`__libc_start_call_main + 135
frame #9: 0x00007ffff783ff60 libc.so.6`__libc_start_main@@GLIBC_2.34 + 128
frame #10: 0x0000000000abbdee node`_start + 46
```
After this change, OrtEnv will be deleted before the main function
returns and nodejs is still alive.
sspintel
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Dec 20, 2023
### Description Hello we(@lixing-star) are the developers of loongson team. We add 128 (lsx), 256 (lasx) vector optimization code for the loongarch architecture [100% tests passed, 0 tests failed out of 7](https://cloud.a-boat.cn:2021/api/public/dl/6831z1Bi?inline=true) ### Development Environments1 ``` CPU: Loongson-3C5000L uname -a: Linux localhost.localdomain 4.19.190-6.4.lns8.loongarch64 #1 SMP Thu Jul 14 12:08:04 CST 2022 loongarch64 loongarch64 loongarch64 GNU/Linux ``` ### LonngArch Documents - [LoongArch Reference Manual - Volume 1: Basic Architecture: This manual describes the basic part of the LoongArch architecture.](https://loongson.github.io/LoongArch-Documentation/LoongArch-Vol1-EN.html) - [LoongArch ELF psABI: This manual describes the LoongArch ELF psABI.](https://loongson.github.io/LoongArch-Documentation/LoongArch-ELF-ABI-EN.html) - [more](https://loongson.github.io/LoongArch-Documentation/README-EN.html)
ankitm3k
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Oct 15, 2024
### Description Add [Lean Attention](https://arxiv.org/abs/2405.10480) and the integration with MultiHeadAttention operator for LLM in GPU. LeanAttention speeds up self-attention for the token-generation phase (decode-phase) of decoder-only transformer models, especially on long context lengths. - [x] Initial implementation of Lean Attention (by Srikant Bharadwaj) - [x] Integration with MultiHeadAttention operator - [x] Add parity tests - [x] Add benchmark #### Implementation Details (1) Lean Attention is enabled in build for Linux, and disabled for Windows (2) Lean Attention is disabled by default. Need enable it through cuda provider option sdpa_kernel, or use environment variable `ORT_ENABLE_LEAN_ATTENTION=1` (3) It only works for token-generation (sequence_length==1, past_sequence_length > 0). (4) Like flash attention, it only works in Ampere or newer GPU. We can revisit #1 and #2 after comparing with DecoderMaskedMultiHeadAttention and XQA kernels. #### Benchmark ``` cd onnxruntime/test/python/transformers /bin/bash benchmark_mha.sh lean ``` Example outputs in H100: Note that past and present does not share buffer for MHA for now, so we can see low tflops. The relative ratio will change after buffer sharing is enabled. But we expect that the order (kernel A is faster than B) will remain the same after buffer sharing is enabled. Note that common settings `sequence_length=1; causal=True;attn_bias=None;cuda_graph=False` are not shown in the below table. batch_size | past_sequence_length | num_heads | head_size | average_latency | tflops | kernel -- | -- | -- | -- | -- | -- | -- 1 | 512 | 16 | 64 | 0.000059 | 0.0178 | ort:flash 1 | 512 | 16 | 64 | 0.000068 | 0.0155 | ort:efficient 1 | 512 | 16 | 64 | 0.000065 | 0.0161 | ort:math 1 | 512 | 16 | 64 | 0.000060 | 0.0176 | ort:lean 1 | 512 | 32 | 128 | 0.000062 | 0.0674 | ort:flash 1 | 512 | 32 | 128 | 0.000064 | 0.0661 | ort:efficient 1 | 512 | 32 | 128 | 0.000067 | 0.0625 | ort:math 1 | 512 | 32 | 128 | 0.000062 | 0.0678 | ort:lean 1 | 1024 | 16 | 64 | 0.000061 | 0.0345 | ort:flash 1 | 1024 | 16 | 64 | 0.000086 | 0.0244 | ort:efficient 1 | 1024 | 16 | 64 | 0.000065 | 0.0322 | ort:math 1 | 1024 | 16 | 64 | 0.000063 | 0.0332 | ort:lean 1 | 1024 | 32 | 128 | 0.000075 | 0.1125 | ort:flash 1 | 1024 | 32 | 128 | 0.000088 | 0.0951 | ort:efficient 1 | 1024 | 32 | 128 | 0.000079 | 0.1068 | ort:math 1 | 1024 | 32 | 128 | 0.000072 | 0.1171 | ort:lean 1 | 2048 | 16 | 64 | 0.000069 | 0.0606 | ort:flash 1 | 2048 | 16 | 64 | 0.000125 | 0.0336 | ort:efficient 1 | 2048 | 16 | 64 | 0.000064 | 0.0655 | ort:lean 1 | 2048 | 32 | 128 | 0.000098 | 0.1720 | ort:flash 1 | 2048 | 32 | 128 | 0.000132 | 0.1270 | ort:efficient 1 | 2048 | 32 | 128 | 0.000092 | 0.1828 | ort:lean 1 | 4096 | 16 | 64 | 0.000076 | 0.1097 | ort:flash 1 | 4096 | 16 | 64 | 0.000207 | 0.0406 | ort:efficient 1 | 4096 | 16 | 64 | 0.000069 | 0.1209 | ort:lean 1 | 4096 | 32 | 128 | 0.000140 | 0.2394 | ort:flash 1 | 4096 | 32 | 128 | 0.000213 | 0.1575 | ort:efficient 1 | 4096 | 32 | 128 | 0.000139 | 0.2419 | ort:lean 1 | 8192 | 16 | 64 | 0.000104 | 0.1609 | ort:flash 1 | 8192 | 16 | 64 | 0.000392 | 0.0428 | ort:efficient 1 | 8192 | 16 | 64 | 0.000093 | 0.1809 | ort:lean 1 | 8192 | 32 | 128 | 0.000212 | 0.3160 | ort:flash 1 | 8192 | 32 | 128 | 0.000360 | 0.1866 | ort:efficient 1 | 8192 | 32 | 128 | 0.000212 | 0.3162 | ort:lean 1 | 16384 | 16 | 64 | 0.000139 | 0.2410 | ort:flash 1 | 16384 | 16 | 64 | 0.000731 | 0.0459 | ort:efficient 1 | 16384 | 16 | 64 | 0.000136 | 0.2465 | ort:lean 1 | 16384 | 32 | 128 | 0.000361 | 0.3722 | ort:flash 1 | 16384 | 32 | 128 | 0.000667 | 0.2014 | ort:efficient 1 | 16384 | 32 | 128 | 0.000357 | 0.3765 | ort:lean 1 | 32768 | 16 | 64 | 0.000210 | 0.3194 | ort:flash 1 | 32768 | 16 | 64 | 0.001428 | 0.0470 | ort:efficient 1 | 32768 | 16 | 64 | 0.000209 | 0.3211 | ort:lean 1 | 32768 | 32 | 128 | 0.000659 | 0.4074 | ort:flash 1 | 32768 | 32 | 128 | 0.001270 | 0.2114 | ort:efficient 1 | 32768 | 32 | 128 | 0.000651 | 0.4123 | ort:lean 1 | 65536 | 16 | 64 | 0.000355 | 0.3785 | ort:flash 1 | 65536 | 16 | 64 | 0.002736 | 0.0491 | ort:efficient 1 | 65536 | 16 | 64 | 0.000349 | 0.3845 | ort:lean 1 | 65536 | 32 | 128 | 0.001251 | 0.4290 | ort:flash 1 | 65536 | 32 | 128 | 0.002480 | 0.2165 | ort:efficient 1 | 65536 | 32 | 128 | 0.001239 | 0.4333 | ort:lean 4 | 512 | 16 | 64 | 0.000063 | 0.0665 | ort:flash 4 | 512 | 16 | 64 | 0.000069 | 0.0607 | ort:efficient 4 | 512 | 16 | 64 | 0.000066 | 0.0634 | ort:math 4 | 512 | 16 | 64 | 0.000062 | 0.0674 | ort:lean 4 | 512 | 32 | 128 | 0.000100 | 0.1677 | ort:flash 4 | 512 | 32 | 128 | 0.000099 | 0.1703 | ort:efficient 4 | 512 | 32 | 128 | 0.000108 | 0.1557 | ort:math 4 | 512 | 32 | 128 | 0.000092 | 0.1818 | ort:lean 4 | 1024 | 16 | 64 | 0.000077 | 0.1094 | ort:flash 4 | 1024 | 16 | 64 | 0.000099 | 0.0850 | ort:efficient 4 | 1024 | 16 | 64 | 0.000081 | 0.1038 | ort:math 4 | 1024 | 16 | 64 | 0.000072 | 0.1161 | ort:lean 4 | 1024 | 32 | 128 | 0.000143 | 0.2343 | ort:flash 4 | 1024 | 32 | 128 | 0.000137 | 0.2447 | ort:efficient 4 | 1024 | 32 | 128 | 0.000150 | 0.2245 | ort:math 4 | 1024 | 32 | 128 | 0.000135 | 0.2496 | ort:lean 4 | 2048 | 16 | 64 | 0.000096 | 0.1757 | ort:flash 4 | 2048 | 16 | 64 | 0.000156 | 0.1078 | ort:efficient 4 | 2048 | 16 | 64 | 0.000089 | 0.1892 | ort:lean 4 | 2048 | 32 | 128 | 0.000223 | 0.3010 | ort:flash 4 | 2048 | 32 | 128 | 0.000217 | 0.3101 | ort:efficient 4 | 2048 | 32 | 128 | 0.000209 | 0.3209 | ort:lean 4 | 4096 | 16 | 64 | 0.000137 | 0.2448 | ort:flash 4 | 4096 | 16 | 64 | 0.000256 | 0.1312 | ort:efficient 4 | 4096 | 16 | 64 | 0.000133 | 0.2530 | ort:lean 4 | 4096 | 32 | 128 | 0.000389 | 0.3450 | ort:flash 4 | 4096 | 32 | 128 | 0.000376 | 0.3574 | ort:efficient 4 | 4096 | 32 | 128 | 0.000354 | 0.3794 | ort:lean 4 | 8192 | 16 | 64 | 0.000210 | 0.3198 | ort:flash 4 | 8192 | 16 | 64 | 0.000453 | 0.1480 | ort:efficient 4 | 8192 | 16 | 64 | 0.000206 | 0.3260 | ort:lean 4 | 8192 | 32 | 128 | 0.000725 | 0.3705 | ort:flash 4 | 8192 | 32 | 128 | 0.000693 | 0.3874 | ort:efficient 4 | 8192 | 32 | 128 | 0.000653 | 0.4114 | ort:lean 4 | 16384 | 16 | 64 | 0.000355 | 0.3782 | ort:flash 4 | 16384 | 16 | 64 | 0.000849 | 0.1581 | ort:efficient 4 | 16384 | 16 | 64 | 0.000346 | 0.3874 | ort:lean 4 | 16384 | 32 | 128 | 0.001395 | 0.3848 | ort:flash 4 | 16384 | 32 | 128 | 0.001337 | 0.4017 | ort:efficient 4 | 16384 | 32 | 128 | 0.001252 | 0.4288 | ort:lean 4 | 32768 | 16 | 64 | 0.000647 | 0.4146 | ort:flash 4 | 32768 | 16 | 64 | 0.001649 | 0.1628 | ort:efficient 4 | 32768 | 16 | 64 | 0.000639 | 0.4204 | ort:lean 4 | 32768 | 32 | 128 | 0.002721 | 0.3947 | ort:flash 4 | 32768 | 32 | 128 | 0.002601 | 0.4128 | ort:efficient 4 | 32768 | 32 | 128 | 0.002434 | 0.4411 | ort:lean 4 | 65536 | 16 | 64 | 0.001231 | 0.4361 | ort:flash 4 | 65536 | 16 | 64 | 0.003238 | 0.1658 | ort:efficient 4 | 65536 | 16 | 64 | 0.001217 | 0.4412 | ort:lean 4 | 65536 | 32 | 128 | 0.005357 | 0.4009 | ort:flash 4 | 65536 | 32 | 128 | 0.005118 | 0.4196 | ort:efficient 4 | 65536 | 32 | 128 | 0.004781 | 0.4492 | ort:lean 16 | 512 | 16 | 64 | 0.000098 | 0.1724 | ort:flash 16 | 512 | 16 | 64 | 0.000104 | 0.1616 | ort:efficient 16 | 512 | 16 | 64 | 0.000118 | 0.1420 | ort:math 16 | 512 | 16 | 64 | 0.000087 | 0.1926 | ort:lean 16 | 512 | 32 | 128 | 0.000220 | 0.3062 | ort:flash 16 | 512 | 32 | 128 | 0.000208 | 0.3237 | ort:efficient 16 | 512 | 32 | 128 | 0.000237 | 0.2838 | ort:math 16 | 512 | 32 | 128 | 0.000209 | 0.3216 | ort:lean 16 | 1024 | 16 | 64 | 0.000136 | 0.2465 | ort:flash 16 | 1024 | 16 | 64 | 0.000150 | 0.2235 | ort:efficient 16 | 1024 | 16 | 64 | 0.000148 | 0.2266 | ort:math 16 | 1024 | 16 | 64 | 0.000129 | 0.2611 | ort:lean 16 | 1024 | 32 | 128 | 0.000367 | 0.3663 | ort:flash 16 | 1024 | 32 | 128 | 0.000351 | 0.3829 | ort:efficient 16 | 1024 | 32 | 128 | 0.000400 | 0.3357 | ort:math 16 | 1024 | 32 | 128 | 0.000349 | 0.3853 | ort:lean 16 | 2048 | 16 | 64 | 0.000209 | 0.3206 | ort:flash 16 | 2048 | 16 | 64 | 0.000243 | 0.2762 | ort:efficient 16 | 2048 | 16 | 64 | 0.000201 | 0.3338 | ort:lean 16 | 2048 | 32 | 128 | 0.000671 | 0.4002 | ort:flash 16 | 2048 | 32 | 128 | 0.000645 | 0.4163 | ort:efficient 16 | 2048 | 32 | 128 | 0.000642 | 0.4185 | ort:lean 16 | 4096 | 16 | 64 | 0.000360 | 0.3732 | ort:flash 16 | 4096 | 16 | 64 | 0.000425 | 0.3162 | ort:efficient 16 | 4096 | 16 | 64 | 0.000341 | 0.3933 | ort:lean 16 | 4096 | 32 | 128 | 0.001292 | 0.4156 | ort:flash 16 | 4096 | 32 | 128 | 0.001251 | 0.4291 | ort:efficient 16 | 4096 | 32 | 128 | 0.001241 | 0.4327 | ort:lean 16 | 8192 | 16 | 64 | 0.000666 | 0.4030 | ort:flash 16 | 8192 | 16 | 64 | 0.000804 | 0.3339 | ort:efficient 16 | 8192 | 16 | 64 | 0.000627 | 0.4283 | ort:lean 16 | 8192 | 32 | 128 | 0.002541 | 0.4226 | ort:flash 16 | 8192 | 32 | 128 | 0.002454 | 0.4376 | ort:efficient 16 | 8192 | 32 | 128 | 0.002438 | 0.4405 | ort:lean 16 | 16384 | 16 | 64 | 0.001292 | 0.4156 | ort:flash 16 | 16384 | 16 | 64 | 0.001571 | 0.3417 | ort:efficient 16 | 16384 | 16 | 64 | 0.001217 | 0.4411 | ort:lean 16 | 16384 | 32 | 128 | 0.005042 | 0.4260 | ort:flash 16 | 16384 | 32 | 128 | 0.004859 | 0.4420 | ort:efficient 16 | 16384 | 32 | 128 | 0.004827 | 0.4449 | ort:lean 16 | 32768 | 16 | 64 | 0.002537 | 0.4233 | ort:flash 16 | 32768 | 16 | 64 | 0.003103 | 0.3461 | ort:efficient 16 | 32768 | 16 | 64 | 0.002385 | 0.4501 | ort:lean 16 | 32768 | 32 | 128 | 0.009961 | 0.4312 | ort:flash 16 | 32768 | 32 | 128 | 0.009605 | 0.4472 | ort:efficient 16 | 32768 | 32 | 128 | 0.009524 | 0.4510 | ort:lean 16 | 65536 | 16 | 64 | 0.005019 | 0.4279 | ort:flash 16 | 65536 | 16 | 64 | 0.006133 | 0.3502 | ort:efficient 16 | 65536 | 16 | 64 | 0.004703 | 0.4566 | ort:lean 16 | 65536 | 32 | 128 | 0.019746 | 0.4350 | ort:flash 16 | 65536 | 32 | 128 | 0.019027 | 0.4515 | ort:efficient 16 | 65536 | 32 | 128 | 0.018864 | 0.4554 | ort:lean ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
ankitm3k
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…ft#28503) ### Description Add an internal session config entry, `"session.compile_only"`, set by `CompileModel()` before session initialization. The NvTensorRTRTX EP reads it in `NvExecutionProviderInfo::FromProviderOptions()` and, when set, skips `deserializeCudaEngine()` / `createExecutionContext()` in `CreateNodeComputeInfoFromGraph()`. The EP context node is still saved — that path uses the serialized engine buffer directly and does not depend on the deserialized engine. A stub compute function is registered to satisfy the framework; it returns `NOT_IMPLEMENTED` if called, which cannot happen in practice because compile-only sessions are destroyed without inference. ### Motivation and Context `OrtCompileAPI::CompileModel()` creates an `InferenceSession` solely to drive `EP::Compile()` and write out the EPContext model, then destroys it without running inference. During that session, the NvTensorRTRTX EP was performing a full `deserializeCudaEngine()` and `createExecutionContext()` — uploading engine weights to the GPU and JIT-ing the engine, only to free everything when the session was destroyed. When the user then loads the EPContext model in a real session, the same JIT and upload happen again. Net effect on the typical "compile, then load and run" flow: ``` ONNX model → CompileModel() [JIT + GPU upload #1 — discarded] → EP context model saved to disk → Session from EP context model [JIT + GPU upload #2 — necessary] → Inference ``` JIT and GPU upload run twice.
hdharpure9922
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Jul 28, 2026
…rosoft#29880) ### Description `import onnxruntime` segfaults during `dlopen` of `onnxruntime_pybind11_state.so` on Linux. This removes the global initializer in `onnxruntime_pybind_state.cc` that eagerly calls `Env::Default()`, and resolves the platform `Env` on first use at its two call sites instead. ### Motivation and Context The module has a namespace-scope dynamic initializer: ```cpp static Env& platform_env = Env::Default(); ``` Since POSIX telemetry landed (microsoft#27379), `Env::Default()` constructs `PosixEnv`, whose `PosixTelemetry` member initializes the 1DS SDK in its constructor. That path reads `defaultRuntimeConfig`, a namespace-scope `static ILogConfiguration` defined in the 1DS SDK's `RuntimeConfig_Default.hpp`, which lives in a **different translation unit of the same shared library**. Dynamic initialization order across translation units is unspecified, and the pybind TU's initializer runs first. `Variant::merge_map` therefore iterates a still zero-initialized `std::map`: `_M_node_count == 0`, but `_M_header._M_left` is `nullptr` rather than self-pointing, so `begin() != end()` and the loop dereferences null. Textbook static initialization order fiasco. Backtrace (Release build relinked without `--strip-all` to recover symbols): ``` #0 std::map<..., Variant>::lower_bound stl_map.h:1307 #1 std::map<..., Variant>::operator[] stl_map.h:509 #2 Variant::merge_map VariantType.hpp:508 #3 RuntimeConfig_Default::RuntimeConfig_Default RuntimeConfig_Default.hpp:97 #4 LogManagerImpl::LogManagerImpl LogManagerImpl.cpp:183 #5 LogManagerFactory::Create LogManagerFactory.cpp:36 #6 LogManagerFactory::lease #7 LogManagerFactory::Get LogManagerFactory.hpp:71 #8 LogManagerProvider::Get LogManagerProvider.cpp:16 #9 onnxruntime::PosixTelemetry::Initialize() #10 onnxruntime::PosixTelemetry::PosixTelemetry() #11 onnxruntime::(anonymous namespace)::PosixEnv::PosixEnv() #12 onnxruntime::Env::Default() #13 _GLOBAL__sub_I_onnxruntime_pybind_state.cc #14 call_init elf/dl-init.c:74 ... #21 _dl_open elf/dl-open.c:905 ``` The crash is independent of `LD_LIBRARY_PATH` and `CUDA_VISIBLE_DEVICES` — it happens before any ORT runtime code runs. `libonnxruntime.so` is unaffected because it contains no global initializer that reaches `Env::Default()`, which is why C/C++ and onnxruntime-genai consumers do not see it. Both remaining uses of `platform_env` are inside pybind lambdas that run long after load, so calling `Env::Default()` there is safe. This also removes the now-stale `TODO: we may delay-init this variable` and a pre-existing `#pragma warning(push)` that should have been `pop`. Note for follow-up: `Env::Default()` is now unsafe to call from any dynamic initializer. This was the only such call site in the tree, but hardening `PosixTelemetry` to defer SDK initialization out of its constructor would remove the hazard entirely. ### Tests Verified on a Linux CUDA 13 Release build (`onnxruntime_USE_TELEMETRY` enabled): - `dlopen` of `onnxruntime_pybind11_state.so` succeeds (previously SIGSEGV). - `import onnxruntime` reports the version and `['CUDAExecutionProvider', 'CPUExecutionProvider']`. - `onnxruntime.enable_telemetry_events()` / `disable_telemetry_events()` — the two call sites changed here — work. - CPU and CUDA inference sessions produce correct results. - Reproduced and verified with `LD_LIBRARY_PATH` unset and `CUDA_VISIBLE_DEVICES` empty.
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Description:
Motivation and Context