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Set up CI with Azure Pipelines #1

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@raymondxyang raymondxyang deleted the azure-pipelines branch November 19, 2018 20:22
@snnn snnn mentioned this pull request Mar 7, 2019
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@luvclear luvclear mentioned this pull request Jun 12, 2019
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@jywu-msft jywu-msft mentioned this pull request Aug 14, 2019
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tmccrmck added a commit to tmccrmck/onnxruntime that referenced this pull request Aug 28, 2019
@tmccrmck
Merge pull request microsoft#1 from tmccrmck/trmccorm/build_integration
54072d2 
Simple CMake setup for ONNX hosting
@walrusmcd walrusmcd mentioned this pull request Oct 18, 2019
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groszewn pushed a commit to groszewn/onnxruntime that referenced this pull request Nov 13, 2019
Merge pull request microsoft#1 from NickGroszewski/tree_ensemble_regr…
a35dc76 
…essor_double

Update TreeEnsembleRegressor type support
@keepWalking keepWalking mentioned this pull request Dec 17, 2019
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@yueyihua yueyihua mentioned this pull request Jan 9, 2020
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scxiao referenced this pull request in ROCm/onnxruntime Feb 4, 2020
@scxiao
Merge pull request #1 from scxiao/migraphx-docker
ea9db42 
Migraphx docker
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chenfeiyue-cfy pushed a commit to chenfeiyue-cfy/onnxruntime that referenced this pull request Feb 1, 2024
@sunshinemyson
Merge pull request microsoft#1 from chenfeiyue-cfy/vsinpu
3cbed88 
Added VSINPU ExecutionProvider for onnxruntime
@Marcus112-CS Marcus112-CS mentioned this pull request Feb 2, 2024
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@snnn @kleiti
nodejs: Release Ort Env before main function returns (microsoft#17288)
d0160a5 
### 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 microsoft#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 microsoft#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 microsoft#3: 0x00007fffee39e2ab libonnxruntime.so.1.16.0`OrtEnv::~OrtEnv(this=0x00007ffff4c30e50) at ort_env.cc:43:1
    frame microsoft#4: 0x00007fffee39fa96 libonnxruntime.so.1.16.0`std::default_delete<OrtEnv>::operator()(this=0x00007fffefff8f78, __ptr=0x00007ffff4c30e50) const at unique_ptr.h:85:2
    frame microsoft#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 microsoft#6: 0x00007ffff78574b5 libc.so.6`__run_exit_handlers + 261
    frame microsoft#7: 0x00007ffff7857630 libc.so.6`exit + 32
    frame microsoft#8: 0x00007ffff783feb7 libc.so.6`__libc_start_call_main + 135
    frame microsoft#9: 0x00007ffff783ff60 libc.so.6`__libc_start_main@@GLIBC_2.34 + 128
    frame microsoft#10: 0x0000000000abbdee node`_start + 46
```
After this change, OrtEnv will be deleted before the main function
returns and nodejs is still alive.
@jywu-msft jywu-msft mentioned this pull request Apr 16, 2024
Merged
siweic0 pushed a commit to siweic0/onnxruntime-web that referenced this pull request May 9, 2024
@snnn
nodejs: Release Ort Env before main function returns (microsoft#17288)
29a70cd 
### 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 microsoft#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 microsoft#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 microsoft#3: 0x00007fffee39e2ab libonnxruntime.so.1.16.0`OrtEnv::~OrtEnv(this=0x00007ffff4c30e50) at ort_env.cc:43:1
    frame microsoft#4: 0x00007fffee39fa96 libonnxruntime.so.1.16.0`std::default_delete<OrtEnv>::operator()(this=0x00007fffefff8f78, __ptr=0x00007ffff4c30e50) const at unique_ptr.h:85:2
    frame microsoft#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 microsoft#6: 0x00007ffff78574b5 libc.so.6`__run_exit_handlers + 261
    frame microsoft#7: 0x00007ffff7857630 libc.so.6`exit + 32
    frame microsoft#8: 0x00007ffff783feb7 libc.so.6`__libc_start_call_main + 135
    frame microsoft#9: 0x00007ffff783ff60 libc.so.6`__libc_start_main@@GLIBC_2.34 + 128
    frame microsoft#10: 0x0000000000abbdee node`_start + 46
```
After this change, OrtEnv will be deleted before the main function
returns and nodejs is still alive.
@EricGeng EricGeng mentioned this pull request May 24, 2024
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@luciferldy luciferldy mentioned this pull request May 27, 2024
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@glen-amd glen-amd mentioned this pull request Jun 19, 2024
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PhaneeshB pushed a commit to PhaneeshB/onnxruntime that referenced this pull request Jul 13, 2024
@stellaraccident
[iree ep] Switch to using IREE CMake dev packages. (microsoft#1)
afcbfe1 
This is needed after iree-org/iree#16008 goes in.
@TinyDustCCC TinyDustCCC mentioned this pull request Jul 22, 2024
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@yufenglee yufenglee mentioned this pull request Aug 26, 2024
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carsonswope added a commit to boris-fx/onnxruntime that referenced this pull request Aug 28, 2024
@carsonswope
add linux build to jenkins, microsoft#1
b21803e
@sai-fpp sai-fpp mentioned this pull request Sep 6, 2024
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@Manamama Manamama mentioned this pull request Sep 22, 2024
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@tianleiwu tianleiwu mentioned this pull request Oct 14, 2024
Merged
4 tasks
tianleiwu added a commit that referenced this pull request Oct 14, 2024
@tianleiwu
[CUDA] Lean Attention (#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. -->
@frenetj frenetj mentioned this pull request Oct 15, 2024
Closed
guschmue pushed a commit that referenced this pull request Oct 18, 2024
@tianleiwu @guschmue
[CUDA] Lean Attention (#22352)
012112b 
### 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
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@tianleiwu
[CUDA] Lean Attention (microsoft#22352)
c51c530 
### 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 microsoft#1 and microsoft#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
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- If it fixes an open issue, please link to the issue here. -->
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@msftclas @raymondxyang