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[DONOTMERGE-for internal code review only] Add logging messages#1

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[DONOTMERGE-for internal code review only] Add logging messages#1
smkarlap wants to merge 2 commits intomasterfrom
openvino-ep/logging

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@smkarlap smkarlap commented Jun 21, 2019

Description:

  • Implements logging messages for OpenVINO Execution Provider at INFO, WARNING and ERROR levels.
  • Utilizes ONNX-RT's logging infrastrcture.
  • Reversed IsGraphSupported check logic to facilitate logging.

Motivation and Context

  • Logging is required to validate that a provided graph has been successfully inferred using the OpenVINO Execution provider.
  • These logging messages are used by automated validation infrastructure.

@smkarlap
Add logging messages
6f2799e 
Implements logging messages at INFO, WARNING and ERROR levels.
Utilizes ONNX-RT's logging infrastrcture.
Reversed IsGraphSupported check logic to facilitate logging.
@smkarlap smkarlap requested a review from suryasidd June 21, 2019 19:30
@smkarlap smkarlap self-assigned this Jun 21, 2019
@smkarlap smkarlap requested a review from ldcastell June 21, 2019 19:59
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smkarlap commented Jun 21, 2019

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.
@ldcastell please review these messages for usability in the CI pipeline.

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smkarlap commented Jun 21, 2019

Build_CI pipeline triggered at: (https://gitlab.devtools.intel.com/iot-edge/onnxrt/onnxruntime_internal_ci/pipelines/286128)

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smkarlap commented Jun 24, 2019

Closing this PR and opening another one for upstreaming

@smkarlap smkarlap closed this Jun 24, 2019
MaajidKhan pushed a commit that referenced this pull request Oct 26, 2021
@edgchen1
[Docs] Update mobile docs for 1.9 (microsoft#9237)
3c9ca35 
* 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 pushed a commit that referenced this pull request Feb 15, 2022
@vvchernov @KJlaccHoeUM9l
[TVM EP] Rename Standalone TVM (STVM) Execution Provider to TVM EP (m…
1cdc23a 
…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 <[email protected]>
Co-authored-by: KJlaccHoeUM9l <[email protected]>
Co-authored-by: Thierry Moreau <[email protected]>
saipj pushed a commit that referenced this pull request Jun 8, 2022
Revert "fixed point based requantization on arm64 (microsoft#11540)" (m…
3c1dd95 
…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 pushed a commit that referenced this pull request Aug 28, 2023
nodejs: Release Ort Env before main function returns (microsoft#17288)
3e93403 
### 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 pushed a commit that referenced this pull request Dec 20, 2023
@junchao-loongson
[mlas] add loongarch lsx and lasx optimize code (microsoft#17937)
4abec97 
### 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 pushed a commit that referenced this pull request Oct 15, 2024
@tianleiwu
[CUDA] Lean Attention (microsoft#22352)
de93f40 
### 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. -->
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