### Description
Several tests result in segfaults during the minimal cuda build.
Although test failures are expected due to the limitation of the minimal
cuda EP, failing gracefully would be much preferred.
### Motivation and Context
To reproduce:
1. Build ORT with:
```bash
./build.sh --build_shared_lib --use_full_protobuf --cuda_home /usr/local/cuda --cudnn_home /usr/lib/x86_64-linux-gnu/ --tensorrt_home /TensorRT-10.0.1.6 --parallel --skip_tests --skip_submodule_sync --allow_running_as_root --use_tensorrt --cmake_extra_defines onnxruntime_CUDA_MINIMAL=1
```
2. Run `onnxruntime_test_all`
```bash
...
[----------] 1 test from AllocationPlannerTest
[ RUN ] AllocationPlannerTest.ReusedInputCrossDifferentStreams
Segmentation fault (core dumped)
```
### Description
Added CUDNN Frontend and used it for NHWC convolutions, and optionally
fuse activation.
#### Backward compatible
- For model existed with FusedConv, model can still run.
- If ORT is built with cuDNN 8, cuDNN frontend will not be built into
binary. Old kernels (using cudnn backend APIs) are used.
#### Major Changes
- For cuDNN 9, we will enable cudnn frontend to fuse convolution and
bias when a provider option `fuse_conv_bias=1`.
- Remove the fusion of FusedConv from graph transformer for CUDA
provider, so there will not be FusedConv be added to graph for CUDA EP
in the future.
- Update cmake files regarding to cudnn settings. The search order of
CUDNN installation in build are like the following:
* environment variable `CUDNN_PATH`
* `onnxruntime_CUDNN_HOME` cmake extra defines. If a build starts from
build.py/build.sh, user can pass it through `--cudnn_home` parameter, or
by environment variable `CUDNN_HOME` if `--cudnn_home` not used.
* cudnn python package installation directory like
python3.xx/site-packages/nvidia/cudnn
* CUDA installation path
#### Potential Issues
- If ORT is built with cuDNN 8, FusedConv fusion is no longer done
automatically, so some model might have performance regression. If user
still wants FusedConv operator for performance reason, they can still
have multiple ways to walkaround: like use older version of onnxruntime;
or use older version of ORT to save optimized onnx, then run with latest
version of ORT. We believe that majority users have moved to cudnn 9
when 1.20 release (since the default in ORT and PyTorch is cudnn 9 for 3
months when 1.20 release), so the impact is small.
- cuDNN graph uses TF32 by default, and user cannot disable TF32 through
the use_tf32 cuda provider option. If user encounters accuracy issue
(like in testing), user has to set environment variable
`NVIDIA_TF32_OVERRIDE=0` to disable TF32. Need update the document of
use_tf32 later.
#### Follow ups
This is one of PRs that target to enable NHWC convolution in CUDA EP by
default if device supports it. There are other changes will follow up to
make it possible.
(1) Enable `prefer_nhwc` by default for device with sm >= 70.
(2) Change `fuse_conv_bias=1` by default after more testing.
(3) Add other NHWC operators (like Resize or UpSample).
### Motivation and Context
The new CUDNN Frontend library provides the functionality to fuse
operations and provides new heuristics for kernel selection. Here it
fuses the convolution with the pointwise bias operation. On the [NVIDIA
ResNet50](https://pytorch.org/hub/nvidia_deeplearningexamples_resnet50/)
we get a performance boost from 49.1144 ms to 42.4643 ms per inference
on a 2560x1440 input (`onnxruntime_perf_test -e cuda -I -q -r 100-d 1 -i
'prefer_nhwc|1' resnet50.onnx`).
---------
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Maximilian Mueller <maximilianm@nvidia.com>
### Description
model: phi-3-mini-4k-instruct
avx2 symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |49.5|70.0|-29.2%|9.6|10.8|-34.2%
32 |76.8|52.4|9.7%|15.2|14.6|4.1%
64 |78.2|71.4|9.5%|16.6|16.3|1.8%
128 |72.9|70.6|3.2%|17.1|16.8|1.7%
256 |83.7|63.6|31.6%|18.1|17.4|4%
avx2 asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |50.7|61.5|-17.5%|9.6|9.2|4.3%
32 |77.4|52.4|47.7%|14.6|13.9|5.0%
64 |78.7|63.0|24.9%|16.2|15.9|1.8%
128 |80.0|61.9|29.2%|17.2|16.9|1.7%
256 |81.5|63.3|28.7%|17.9|17.3|3.4%
avx2vnni symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |82.9|117.0|-29.0%|15.9|19.3|-17.6%
32 |133.0|100.4|32.4%|26.1|24.5|6.5%
64 |166.9|118.8|40.4%|28.3|27.1|4.4%
128 |165.9|119.6|38.7%|29.3|28.5|2.8%
256 |165.2|119.6|38.1%|30.2|29.0|4.1%
avx2vnni asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |80.2|118.9|-32.5%|15.1|16.7|-9.5%
32 |130.7|99.7|31.0%|25.0|23.8|5.0%
64 |168.7|124.9|35.0%|27.3|26.8|1.8%
128 |169.6|123.8|36.9%|29.2|27.9|4.6%
256 |175.0|125.7|39.0%|30.0|29.7|1.0%
avx512 symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |135.2|156.5|-13.6|25.5|23.8|7.1
32 |150.0|159.5|-5.9|34.9|29.6|17.9
64 |167.5|157.5|6.3|39.7|34.4|15.4
128 |177.8|158.0|12.5|40.3|35.4|13.8
256 |182.6|157.3|16.0|41.7|37.7|10.6
avx512 asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |136.1|151.4|-10.1%|26.1|19.9|31.1%
32 |150.0|157.8|-4.9%|34.3|29.3|17.0%
64 |165.7|156.6|5.8%|38.7|30.7|26.0%
128 |180.4|156.6|15.1%|40.2|34.7|15.8%
256 |181.3|158.0|14.7%|41.6|36.6|13.6%
avx512vnni symmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |143.4|155.4|-7.7%|25.6|23.3|9.8%
32 |159.2|157.0|1.4%|34.1|29.8|14.4%
64 |182.0|159.5|14.1%|38.4|34.8|10.3%
128 |221.2|160.8|37.5%|41.0|36.4|12.6%
256 |250.5|162.4|54.2%|41.6|37.7|10.3%
avx512vnni asymmetric
blklen|updated prompt tps | baseline prompt tps | prompt tps
change%|updated token gen tps | baseline token gen tps | token gen
change%
-|-|-|-|-|-|-
16 |142.5|152.3|-6.4%|26.3|19.7|33.5%
32 |158.2|155.0|2.0%|34.3|29.2|17.4%
64 |184.1|156.6|17.5%|38.3|30.9|23.9%
128 |215.8|156.1|17.5%|41.3|35.0|17.9%
256 |249.2|155.9|59.8%|41.1|36.3|13.2%
4bit gemm implementation with avx using tile.
1.
tile size is 2blk by 4. in case of size less then tile, it reduce to
1blk by 4, 2blk by 1 and lastly 1blk by 1.
with internal kernel, weight and activation are loaded based on SIMD
register width and blk length:
avx2 256bit register, 64 weights and activation are loaded.
blklen16: 4 blks are computed by the internal kernel
blklen32: 2 blks are computed by the internal kernel
blklen64: 1 blk are computed by the internal kernel
blklen128: 1 blks are computed 2 times by the internal kernel
blklen16: 1 blks are computed 4 times by the internal kernel
avx512 512bit register, 128 weights and activation are loaded.
blklen16: 8 blks are computed by the internal kernel
blklen32: 4 blks are computed by the internal kernel
blklen64: 2 blk are computed by the internal kernel
blklen128: 1 blks are computed by the internal kernel
blklen16: 1 blks are computed 2 times by the internal kernel
2.
blksum is precomputed during prepacking.
computation is reformed:
Sum1(scale_a * scale_b * Sum_blk(a_i * b_i)) + Sum2(blksum_a * blksum_b)
Sum_blk is over one blk
Sum1 is over all blks for one output
Sum2 is over all blks for one output
Sum is computed with sgemm with the current implementation. Further
improvement is possible.
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
Signed-off-by: liqunfu <liqun.fu@microsoft.com>
Signed-off-by: Liqun Fu <liqun_fu@hotmail.com>
### Description
The header files were added in PR #16454.
Then, recently I made a PR #21464 that changed how we packed Linux
tarballs.
The new tarball misses the custom op header files.
Therefore I need to make this change.
### 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. -->
### Description
<!-- Describe your changes. -->
Update TRT OSS Parser to [latest 10.2-GA
branch](f161f95883)
### 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. -->
### Description
This PR adds a new option `ort.env.wasm.wasmBinary`, which allows user
to set to a buffer containing preload .wasm file content.
This PR should resolve the problem from latest discussion in #20876.
### Description
Local CI setup for AIX reported tests failure after the gtest 1.15.0
upgrade.
### Motivation and Context
Below tests failure is observed after gtest upgrade.
The following tests FAILED:
1 - onnxruntime_test_all (ILLEGAL)
7 - onnxruntime_logging_apis_test (Subprocess aborted)
To fix this, I am enabling pthread support under gtest. This was
disabled with previous version of gtest for some reason.
Now by enabling this, above tests are getting passed with gtest 1.15.0.
### Description
Add OVEP features for 1.19
The PR has,
- Added support for EpCtx with ORT Session options for optimized
performance.
- Added bug fixes
- Support for OV 2024.3
---------
Co-authored-by: ubuntu <ubuntu@ubuntu-mtlp-118727.iind.intel.com>
Co-authored-by: vthaniel <vishnudas.thaniel.s@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: saurabhkale17 <saurabh1.kale@intel.com>
Co-authored-by: Maheshkar <ankit.maheshkar@intel.com>
### Description
Before this change, copy_strip_binary.sh manually copies each file from
onnx runtime's build folder to an artifact folder. It can be hard when
dealing with symbolic link for shared libraries.
This PR will change the packaging pipelines to run "make install" first,
before packaging shared libs .
### Motivation and Context
Recently because of feature request #21281 , we changed
libonnxruntime.so's SONAME. Now every package that contains this shared
library must also contains libonnxruntime.so.1. Therefore we need to
change the packaging scripts to include this file. Instead of manually
construct the symlink layout, using `make install` is much easier and
will make things more consistent because it is a standard way of making
packages.
**Breaking change:**
After this change, our **inference** tarballs that are published to our
Github release pages will be not contain ORT **training** headers.
### Description
<!-- Describe your changes. -->
Add ML Program ConvTranspose
- some limitations to simplify the implementation for now
- some limitations due to flaky CoreML output
Added support for non-contiguous MLMultiArray output as we see that with
some unit tests when the CPU-only flag is not set (e.g. innermost dim
has min size of 16 but test output only has 8 values).
- support only one non-contiguous dim to keep it simple
- manually tested as we don't have a setup that can test objective-c
code
- test code is in model.mm and can be enabled via ifdef if we need to
validate any future changes
### 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. -->
Address operator gaps in high priority model.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
* Fix fallback setting (cuda still falls back to cuda).
* Fix cuda provider fallback inconsistent with/without CUDA_PATH
environment variable.
* Add cuda and cudnn major version requirement in error message.
Example result in Windows:
```
>>> import onnxruntime
>>> ort_session = onnxruntime.InferenceSession("model.onnx", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
2024-07-19 17:43:44.2260019 [E:onnxruntime:Default, provider_bridge_ort.cc:1972 onnxruntime::TryGetProviderInfo_CUDA] D:\onnxruntime\onnxruntime\core\session\provider_bridge_ort.cc:1636 onnxruntime::ProviderLibrary::Get [ONNXRuntimeError] : 1 : FAIL : LoadLibrary failed with error 126 "" when trying to load "C:\Users\.conda\envs\py310\lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
2024-07-19 17:43:44.2312351 [W:onnxruntime:Default, onnxruntime_pybind_state.cc:970 onnxruntime::python::CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*, and the latest MSVC runtime. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.
>>> ort_session
<onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x0000016BB2DF7D60>
>>> ort_session.get_providers()
['CPUExecutionProvider']
```
Example result in Linux:
```
>>> import onnxruntime
>>> ort_session = onnxruntime.InferenceSession("resnet50-v2-7.onnx", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
2024-07-20 20:33:26.486974543 [E:onnxruntime:Default, provider_bridge_ort.cc:1972 TryGetProviderInfo_CUDA] /work/onnxruntime/onnxruntime/core/session/provider_bridge_ort.cc:1636 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcublasLt.so.12: cannot open shared object file: No such file or directory
2024-07-20 20:33:26.487034646 [W:onnxruntime:Default, onnxruntime_pybind_state.cc:961 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.
>>> ort_session.get_providers()
['CPUExecutionProvider']
```
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/21424
1. Update google benchmark from 1.8.3 to 1.8.5
2. Update google test from commit in main branch to tag 1.15.0
3. Update pybind11 from 2.12.0 to 2.13.1
4. Update pytorch cpuinfo to include the support for Arm Neoverse V2,
Cortex X4, A720 and A520.
5. Update re2 from 2024-05-01 to 2024-07-02
6. Update cmake to 3.30.1
7. Update Linux docker images
8. Fix a warning in test/perftest/ort_test_session.cc:826:37: error:
implicit conversion loses integer precision: 'streamoff' (aka 'long
long') to 'const std::streamsize' (aka 'const long')
[-Werror,-Wshorten-64-to-32]
### Description
* Add a cuda provider option `sdpa_kernel` to choose which attention kernel to run for testing purpose.
* Allow dump which attention kernel is used per node.
* Reserve a flag for cudnn flash attention which will be added soon.
#### CUDA provider option sdpa_kernel
Instead of setting environment variable, we also support setting it in
provider option. Note that the setting is global per session. That could
help performance testing of each kernel.
#### Attention Kernel Debug Info
Set an environment variable `ORT_ENABLE_ATTENTION_KERNEL_DEBUG_INFO=1`,
and ORT will print sdpa kernel used in each node:
For example
```
ORT_ENABLE_ATTENTION_KERNEL_DEBUG_INFO=1 ./onnxruntime_test_all --gtest_filter=MultiHeadAttentionTest*
```
It will show debug information of kernel used in testing:
```
[ RUN ] MultiHeadAttentionTest.SelfAttention_Batch2_HeadSize32_NoBias_NoMask_PackedQKV
AttentionKernelOptions: FLASH_ATTENTION=0 EFFICIENT_ATTENTION=0 TRT_FUSED_ATTENTION=1 CUDNN_FLASH_ATTENTION=0 TRT_FLASH_ATTENTION=1 TRT_CROSS_ATTENTION=0 TRT_CAUSAL_ATTENTION=0 MATH=1
Operator=MultiHeadAttention Node=node1 DataType=fp16 TRT_FUSED_ATTENTION=1
AttentionKernelOptions: FLASH_ATTENTION=0 EFFICIENT_ATTENTION=1 TRT_FUSED_ATTENTION=0 CUDNN_FLASH_ATTENTION=0 TRT_FLASH_ATTENTION=0 TRT_CROSS_ATTENTION=0 TRT_CAUSAL_ATTENTION=0 MATH=1
Operator=MultiHeadAttention Node=node1 DataType=fp16 EFFICIENT_ATTENTION=1
```
In this test case, the debug info shows that one session uses trt fused
attention and another session use efficient attention.
### Description
Enablement of onnxruntime for AIX and fixing issues related to
big-endian platform.
### Motivation and Context
changes in this PR contains:
1. Enablement code for building onnxruntime on AIX operating system.
2. while testing the build on AIX, we found issues related to big endian
platform . More details about few of those issues can be found in [Big
endian issue: Graph Transformation Attention Fusion tests are failing
#12921](https://github.com/microsoft/onnxruntime/issues/12921)
Below are list of files and the description about the change.
1. cmake/CMakeLists.txt
[BUILDING on AIX issue] check for "IBMClang" is added for handling
-Wno-unused-parameter
2. cmake/external/onnxruntime_external_deps.cmake
[BUILDING on AIX issue]Enabling gtest_disable_pthreads for AIX
3. cmake/onnxruntime.cmake
[BUILDING on AIX issue]
o Blocking codes for AIX which generates generated_source.c and further
requires some symbol files.
o Putting NO AIX check for non-supported linker flags like --Xlinker
o iconv linking
4. cmake/onnxruntime_framework.cmake
[BUILDING on AIX issue]Putting NO AIX check for -Wl,-rpath='$ORIGIN'
5. cmake/onnxruntime_mlas.cmake
[BUILDING on AIX issue]POWER10 releated macro/function definition .
6. cmake/onnxruntime_providers_cpu.cmake
[BUILDING on AIX issue]Putting NO AIX check for non-supported linker
flags like --Xlinker
7. cmake/onnxruntime_unittests.cmake
[BUILDING on AIX issue]
o Putting NO AIX check for non-supported linker flags like --Xlinker
o Adding required libraries for AIX linker under applicatiion like
onnxruntime_shared_lib_test ,onnxruntime_logging_apis etc
8. cmake/patches/flatbuffers/flatbuffers.patch
[BUILDING on AIX issue] Handling of TypeCode in
include/flatbuffers/flatbuffers.h under AIX + clang
9. onnxruntime/contrib_ops/cpu/murmur_hash3.cc
[Big endian issue] Byte-Conversion handlling in compute() and getblock()
routines
10. onnxruntime/contrib_ops/cpu/quantization/matmul_nbits_impl.cc
[Big endian issue] Handling of test failures . Byte swapping for
quant_value.
11. onnxruntime/core/framework/tensorprotoutils.cc
[Big endian issue]
Implementation of SetRawDataInTensorProto , ConvertRawDataInTensorProto
.
o SetRawDataInTensorProto : Wrapper for set_raw_data(). Calling
ConvertRawDataInTensorProto() in big-endian system
o ConvertRawDataInTensorProto : function used mainly on big-endian
system for byte-swapping of tensor raw_data
12. onnxruntime/core/framework/tensorprotoutils.h
[Big endian issue]
Declaration of SetRawDataInTensorProto, ConvertRawDataInTensorProto
13. onnxruntime/core/graph/graph.cc
[Big endian issue]
o Call ConvertRawDataInTensorProto for SPARSE_TENSOR type
o Call ConvertRawDataInTensorProto for SaveToOrtFormat
14. onnxruntime/core/mlas/lib/platform.cpp
[BUILDING on AIX issue] POWER10 released enablement for AIX
15. onnxruntime/core/mlas/lib/power/qgemm_kernel_power10.cpp
[BUILDING on AIX issue]Handling of __vector under AIX+clang
16. onnxruntime/core/mlas/lib/qgemm.h
[BUILDING on AIX issue] Adding _AIX flag
17. onnxruntime/core/mlas/lib/qlmul.cpp
[BUILDING on AIX issue] Handling of __vector under AIX+clang
18. onnxruntime/core/optimizer/attention_fusion.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
19. onnxruntime/core/optimizer/compute_optimizer/shared_utils.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
20. onnxruntime/core/optimizer/constant_folding.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
21. onnxruntime/core/optimizer/embed_layer_norm_fusion.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
22. onnxruntime/core/optimizer/nchwc_transformer.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
23. onnxruntime/core/optimizer/qdq_transformer/avx2_weight_s8_to_u8.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
24. onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
25. onnxruntime/core/optimizer/qdq_transformer/s8_to_u8.h
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
26.
onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_actions.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
27. onnxruntime/core/optimizer/reshape_fusion.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
28. onnxruntime/core/optimizer/stft_decomposition.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
29.
onnxruntime/core/optimizer/transpose_optimization/ort_optimizer_api_impl.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
30. onnxruntime/core/platform/path_lib.h
[BUILDING on AIX issue] Moving to normal function call, instead of
template
31. onnxruntime/core/platform/posix/env.cc
[BUILDING on AIX issue]Blocking syscall.h in AIX
32. onnxruntime/core/session/inference_session.cc
[Big endian issue] Removing ORT_RETURN_IF_NOT, FLATBUFFERS_LITTLEENDIAN
33. onnxruntime/test/flatbuffers/flatbuffer_utils_test.cc
[Big endian issue] Call ConvertRawDataInTensorProto in CreateInitializer
and ExternalWriteReadWithLoadInitializers
34. onnxruntime/test/framework/sparse_kernels_test.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
35. onnxruntime/test/framework/tensorutils_test.cc
[Big endian issue] Helper method ConvertEndianessForVector and call this
from required place.
36. onnxruntime/test/framework/test_tensor_loader.cc
o. [BUILDING on AIX issue] Handling of getcwd for AIX
o. [Big endian issue] Bytes Swapping in run_external_data_test
37. onnxruntime/test/onnx/main.cc
[Big endian issue] including <thread> for AIX
38. onnxruntime/test/onnx/tensorprotoutils.cc
[Big endian issue] Bytes swapping in UnpackTensorWithRawData
39. onnxruntime/test/optimizer/graph_transform_test.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
40. onnxruntime/test/optimizer/graph_transform_test_builder.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
41. onnxruntime/test/optimizer/graph_transform_test_builder.h
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
42. onnxruntime/test/optimizer/initializer_test.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
43. onnxruntime/test/optimizer/nchwc_optimizer_test.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
44. onnxruntime/test/providers/base_tester.cc
[Big endian issue] Use util function SetRawDataInTensorProto, instead of
set_raw_data
45. onnxruntime/test/providers/cpu/generator/random_test.cc
[BUILDING on AIX issue] Adding AIX check in MultinomialGoodCase
---------
Co-authored-by: Vamshikrishna Thatikonda <vamshikrishna@in.ibm.com>
### Description
Resolve#21281 and #10589 .
1. Change libonnxruntime.so's SONAME: remove the minor and patch
version.
By default when creating an ELF shared object, linker will set the
file's internal DT_SONAME field to the specified name which is the file
name plus SOVERSION . For example, the file name for our library is
libonnxruntime.so. And by default SOVERSION is the lib's VERSION number,
which is something like 1.19.0. So the DT_SONAME field in
libonnxruntime.so is something like libonnxruntime.so.1.18.0. You can
use readelf tool to examine it.
```
readelf -d libonnxruntime.so | grep SONAME
0x000000000000000e (SONAME) Library soname: [libonnxruntime.so.1.18.0]
```
When an executable is linked with a shared object which has a DT_SONAME
field, then when the executable is run the dynamic linker will attempt
to load the shared object specified by the DT_SONAME field rather than
using the file name(which is libonnxruntime.so) given to the linker.
After this change, the SONAME will be shorten to "libonnxruntime.so.1"
instead.
2. Set default version strings for Windows DLLs, to resolve#10589
- Pass a list of files instead of path separator-delimited string to project.files(). See this issue: https://github.com/gradle/gradle/issues/19817
- Check for host (instead of target) being Windows when using fallback patch program.
### Description
Repeat of #21084 with removal of policy CMP0144 to suppress warnings
which uses CMake 3.27.0.
### Motivation and Context
Already approved PR:
https://github.com/microsoft/onnxruntime/pull/21084
Removed the added policy from CMake 3.27.0.
### Description
Implement [FlashAttention](https://arxiv.org/pdf/2205.14135) and
[FlashAttention-2](https://arxiv.org/pdf/2307.08691) for
MultiHeadAttention on CPU.
### Motivation and Context
Accelerate the execution of MultiHeadAttention.
Current performance: 10ms vs 16ms (com.microsoft.MultiHeadAttention) on
my Linux machine and 10ms vs 38ms (com.microsoft.MultiHeadAttention) on
my Windows machine. May need further optimizations.
---------
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Qingnan Duan <qiduan@microsoft.com>
Update AArch64 SQNBitGemm CompInt8 kernels to process matrix in tiles. E.g., computing the output in 2x2 tiles allows us to compute four elements of the output with one read of two rows of A and two columns of B.
Also moved some code around as it was getting big for a single file.
### Description
Our macOS pipeline are failing because of a build error in absl.
However, the bug fix we need is not available in the latest ABSL
release.
Here is the issue: https://github.com/abseil/abseil-cpp/pull/1536
And here is the fix:
779a3565ac
GTests uses ABSL. But this ABSL target also depends on GTest. So, it is
a circular dependency. We should be able to avoid that by avoid building
tests for ABSL. However, the version we are using has a problem with
that: it has cmake target that still depends on GTest even when testing
is disabled.
It's strange that we suddenly hit this problem and it only happens on macOS.
### Description
CMake logic fixed to allow enabling MPI while NCCL is disabled.
### Motivation and Context
MPI is also used on the CPU backend, not only with CUDA, so it makes
sense to decouple it properly from NCCL (which is for dealing with
multiple Nvidia GPUs).
### Description
Previously ROCMExecutionProvider uses `hipMemGetInfo` to obtain the
sizes of total memory and available memory. However, this API has been
broken since ROCm 5.7. In this PR, we use `rocm_smi` library instead of
`hipMemGetInfo`.
### Motivation and Context
`hipMemGetInfo` API has been broken since ROCm 5.7 and inference with
ROCMExecutionProvider will lead to following errors:
```
HIP failure 1: invalid argument ; GPU=0 ; hostname=4cc4900475fe ; file=/onnxruntime/onnxruntime/core/providers/rocm/rocm_execution_provider.cc ; line=229 ; expr=hipMemGetInfo(&free, &total);
```
MIOpen has a brute-force fix for this
(911e671895/src/hip/handlehip.cpp (L72)).
Instead of hard-coding available memory to 16GB, I suppose we could
obtain memory info through `rocm_smi` library as in this PR.
### Description
- Refactor codes to meet line length limit and guard missing warning
- Add slice/dropout op support
- Move vsinpu ep's cmake settings from onnxruntime_providers.cmake to a
separate file
- Modify apis with param onnxruntime::Path because this kind is replaced
by std:filesystem::path by #20920
### Description
Extend cuda minimal option to TRT provider, as with TRT 10 no linking to
cuDNN is required anymore
.
Besides that with the new engine dump feature it is also possible to
embed an engine in to an ONNX and not ship a builder lib.
In addition to that this has roughly the same deserialization
time/session setup time that using TRT standalone has.
### Motivation and Context
```
exe_builder_lib\onnxruntime_perf_test.exe -I -e tensorrt -r 5 -i 'trt_engine_cache_enable|1 trt_timing_cache_enable|1 trt_dump_ep_context_model|1 trt_weightless_engine_enable|1' model.onnx
exe_no_builder_lib\onnxruntime_perf_test.exe -I -e tensorrt -r 5 -i 'trt_engine_cache_enable|1 trt_timing_cache_enable|1 trt_dump_ep_context_model|1 trt_weightless_engine_enable|1' model_ctx.onnx
```
### Description
<!-- Describe your changes. -->
-It is an initial PR for VSINPU execution provider
### 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. -->
- For support VeriSilicon hardware
- TIM-VX(Tensor Interface Module)
(https://github.com/VeriSilicon/TIM-VX) is an integrated software
solution by Verisilicon for our hardware(A311D/i.MX 8M Plus etc.)
design, it is easy to use Verisilicon’s hardware by simply connecting
onnxruntime with the TIM-VX API by this VSINPU execution provider.
### Description
1. Update the functions in tensorprotoutils.h to use
std::filesystem::path instead of onnxruntime::Path. Eventually we can
remove the whole onnxruntime::Path class, but to this PR small I am not
doing that.
2. Remove the _SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING macro
def when TensorRT EP is enabled.
### Description
Provide user level options to control the fallback on CPU for models not
supported on Intel's NPU hardware.
### Motivation and Context
- Current workflow of OVEP allows safe fallback from OV NPU to OV CPU on
compilation failures. Also supports MLAS CPU fallback in presence of
unsupported custom ops.
- The PR provides a build-time option to disable fallback from OV NPU to
OV CPU.
- The session Option "kOrtSessionOptionsDisableCPUEPFallback" disables
OV CPU and MLAS CPU fallback.
- Also has bug fix for proto creation.
---------
Co-authored-by: jatinwadhwa921 <jatin.wadhwa@intel.com>
Co-authored-by: ankitm3k <ankit.maheshkar@intel.com>
### Description
As suggested by SciPy's doc, we will
`Build against NumPy 2.0.0, then it will work for all NumPy versions
with the same major version number (NumPy does maintain backwards ABI
compatibility), and as far back as NumPy 1.19 series at the time of
writing`
I think it works because in
[numpyconfig.h#L64](https://github.com/numpy/numpy/blob/main/numpy/_core/include/numpy/numpyconfig.h#L64)
there is a macro NPY_FEATURE_VERSION. By default it is set to
NPY_1_19_API_VERSION. And the NPY_FEATURE_VERSION macro controls ABI.
This PR only upgrade the build time dependency; When a user installs
ONNX Runtime, they still can use numpy 1.x.
### Motivation and Context
Recently numpy published a new version, 2.0.0, which is incompatible with the latest ONNX Runtime release.
### Description
This reverts commit 1d7bf56947 because it
broken the AMD GPU CI pipeline. Sorry when I reviewed the PR I forgot to
run the AMD GPU CI pipeline.
Will revert the PR first then ask the author to fix the issue.
### Description
Update protobuf_cmake.patch to allow extra disablements. ORT repo
already patches protobuf to not disable the warning 4996.
### Motivation and Context
To meet SDL requirements, Microsoft repos have to fail build if there is
warning 4996
Binskim also gives errors if warning 4996 is disabled.
We can suppress the Binskim issues, but we need a way to disable the
warnings for the minimal set of code that has them.
Right now, WindowsAI disables 4996 for entirety of ORT, but it should
only be disabled for protobuf.
### Description
Remove the "--enable_language_interop_ops" build flag, because the code
is incompatible with the latest numpy, and the build flag is not used
anywhere except a macOS CI pipeline. It does not seem to have a ship
plan.
### Motivation and Context
The build error was:
```
onnxruntime/core/language_interop_ops/pyop/pyop.cc:122:85: error: no member named 'elsize' in '_PyArray_Descr'
static_cast<int64_t>(PyArray_DescrFromType(type)->elsize),
~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^
```
### Description
Upgrade pybind11 to the latest as suggested by @gnought in #21063
### Motivation and Context
Recently numpy released a new version, which caused compatibility issue
between the latest numpy version and the latest ONNX Runtime version.
### Description
<!-- Describe your changes. -->
- Add check for CoreML MLProgram supported ops
- Only check usability with ORT Mobile package if requested
- this package will be deprecated so info is a) of minimal value and b)
can be confusing.
- Output more things at INFO level
- a lot of meaningful info was only output at DEBUG level. The default
INFO level is more useful
- dump full partition info at DEBUG level
- Check subgraphs fully
- CoreML can handle a subgraph
- TBD if we want to add support for adding a subgraph to the parent
graph for Loop and If nodes
- most likely will be required for simple If nodes to be performant
- Check 5D CoreML limitation
### 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. -->
Improve helper tools
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
- Fixes compilation error for "reduced operator" builds with no FP16
kernels and `MLAS_F16VEC_INTRINSICS_SUPPORTED` enabled.
- Fixes linker error for "reduced operator" builds with QNN EP by
excluding QNN EP unit tests. QNN EP unit tests require CPU EP operator
implementations to evaluate accuracy.
### Motivation and Context
Need to be able to build a reduced operator build with QNN EP. See
https://github.com/microsoft/onnxruntime/blob/main/docs/Reduced_Operator_Kernel_build.md
The following example operator config file causes a compilation error
when either `MLAS_F16VEC_INTRINSICS_SUPPORTED` is defined or QNN EP is
enabled.
```
# reduced_op_config.txt
ai.onnx;12;Add
```
```shell
python tools\ci_build\build.py --include_ops_by_config reduced_op_config.txt --config Debug --build_wheel --build_shared_lib --skip_tests --build_dir build --parallel --use_qnn --qnn_home '<QNN_ROOT_DIR>'
```
### Description
<!-- Describe your changes. -->
Conditionally route to custom AllReduce kernel when buffer size and gpu
numbers meet certain requirements. Otherwise, keep using NCCL's
AllReduce.
### 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. -->
---------
Co-authored-by: Ye Wang <wangye@microsoft.com@h100vm-ort.kxelwkzfzxguje5bxvwxxs135a.gvxx.internal.cloudapp.net>
Co-authored-by: Your Name <you@example.com>
### Description
Upgrade cutlass to 3.5 to fix build errors using CUDA 12.4 or 12.5 in
Windows
- [x] Upgrade cutlass to 3.5.0.
- [x] Fix flash attention build error with latest cutlass header files
and APIs. This fix is provided by @wangyems.
- [x] Update efficient attention to use new cutlass fmha interface.
- [x] Patch cutlass to fix `hrsqrt` not found error for sm < 53.
- [x] Disable TF32 Staged Accumulation to fix blkq4_fp16_gemm_sm80_test
build error for cuda 11.8 to 12.3.
- [x] Disable TRT 10 deprecate warnings.
The following are not included in this PR:
* TRT provider replaces the deprecated APIs.
* Fix blkq4_fp16_gemm_sm80_test build error for cuda 12.4 or 12.5. This
test is not built by default unless you add `--cmake_extra_defines
onnxruntime_ENABLE_CUDA_EP_INTERNAL_TESTS=ON` in build command.
To integrate to rel-1.18.1: Either bring in other changes (like onnx
1.16.1), or generate manifest and upload a new ONNX Runtime Build Time
Deps artifact based on rel-1.18.1.
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/19891https://github.com/microsoft/onnxruntime/issues/20924https://github.com/microsoft/onnxruntime/issues/20953
# Description
This PR removes the building of the ORT "mobile" packages and much of the associated infrastructure which is no longer needed.
Not removed yet - tools/ci_build/github/android/mobile_package.required_operators.config and the helper scripts that depend on it.
# Motivation and Context
The mobile packages were deprecated in 1.18. Users should use the full packages (Android - onnxruntime-android, iOS - onnxruntime-c/onnxruntime-objc) instead or do a custom build.
Some dev environments come with a preinstalled abseil. For example,
conda users often do that. If the preinstalled abseil version is
incompatible with what we have in cmake/deps.txt, it could result in a
hard-to-understand build error. This PR adds a version check to improve
that.
