### Description
<!-- Describe your changes. -->
If the EP handles QDQ node units, we need to make sure we do not split
those into different partitions.
Update the partitioning utils to be QDQ aware. If there are node units
we process the logical nodes they represent instead of individual nodes.
This ensure we process all nodes in a QDQ node unit at the same time so
that they are always in the same partition.
### 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. -->
Fix one of the issues in #19590
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
It was disabled in PR #9065. And the reason was:
" api-ms-win-core-kernel32-legacy-*.dll wasn't available in Windows 8
and was added in Windows 10, so cpuinfo breaks our Windows 8 support.
I'm disabling it again."
We no longer support Windows 8. Therefore we can add CPUINFO back.
### Motivation and Context
To make the code simpler. If in any case the library doesn't work as
expected, we can submit a PR to their code base and fix it.
### Description
Answers issue #19640
More details are in the issue, basically I am changing all the include
directory and link directory usage to CMake's `CUDA::*` targets
### Description
<!-- Describe your changes. -->
Setup usage of coremltools via dependencies instead of copying files.
Pull in some changes from
https://github.com/microsoft/onnxruntime/pull/19347 in preparation for
supporting ML Program and enabling building the ML Model on all
platforms to make development and testing of CoreML EP code easier.
- Update to coremltools 7.1
- Add patch for changes required for cross platform build of ML Program
related code
- Generate coreml proto files on all platforms
- mainly to test these changes work everywhere, as the proto files will
be used on all platforms when #19347 is checked in
- rename onnxruntime_coreml_proto target to coreml_proto as it contains
purely coreml protobuf code with no ORT related chagnes
### 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 setup.
### Description
Since Cutlass can be built with CUDA 11.4 (The minimum CUDA version for
onnxruntime CUDA build), there is no need to have a flag to disable
cutlass.
Changes:
(1) Reverted https://github.com/microsoft/onnxruntime/pull/18761
(2) remove the condition to build cutlass.
(3) Fix a few build errors or warnings during testing CUDA 11.4 build.
Note that SM 89 and 90 (including fp8) requires CUDA 11.8 or later.
Flash attention and cutlass fused multihead attention will not be built
for CUDA < 11.6. It is recommended to use CUDA 11.8 or above to build if
you want to support latest GPUs.
It is better to include it in 1.17.0 (otherwise, the release branch
might encounter build failure with CUDA 11.4).
Tests:
(1) Build with flash attention and efficient attention off: **passed**
(2) Build with CUDA 11.4: **passed**
Example build command used in Ubuntu 20.04:
```
export CUDA_HOME=/usr/local/cuda-11.4
export CUDNN_HOME=/usr/lib/x86_64-linux-gnu/
export CUDACXX=/usr/local/cuda-11.4/bin/nvcc
sh build.sh --config Release --build_shared_lib --parallel --use_cuda --cuda_version 11.4 \
--cuda_home $CUDA_HOME --cudnn_home $CUDNN_HOME --build_wheel --skip_tests \
--cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=80 \
--disable_types float8
```
### 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 pull request introduces the necessary changes to enable RISC-V
64-bit cross-compiling support for the ONNX Runtime on Linux. The RISC-V
architecture has gained popularity as an open standard instruction set
architecture, and this contribution aims to extend ONNX Runtime's
compatibility to include RISC-V, thereby broadening the reach of ONNX
models to a wider range of devices.
### Motivation and Context
RISC-V is a free and open-source instruction set architecture (ISA)
based on established RISC principles. It is provided under open licenses
without fees. Due to its extensibility and freedom in both software and
hardware, RISC-V is poised for widespread adoption in the future,
especially in applications related to AI, parallel computing, and data
centers.
### Example Build Command
```
./build.sh --parallel --config Debug --rv64 --riscv_toolchain_root=/path/to/toolchain/root --skip_tests
```
### Documentation Updates
Relevant sections of the documentation will be updated to reflect the
newly supported RISC-V 64-bit cross-compilation feature.
https://github.com/microsoft/onnxruntime/pull/19239
---------
Signed-off-by: Phoebe Chen <phoebe.chen@sifive.com>
### Description
Update abseil to a release tag and register neural_speed to CG.
### Motivation and Context
Now we are using a non-relesed version of abseil. Using a tag is better.
### Description
<!-- Describe your changes. -->
1. Make JBLAS codes an external module of ORT.
2. Move q4 gemm code to contrib_ops.
3. Update template kernel library to v0.1 release.
### 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. -->
We found that the current LLM model performance is far below our
expectations. Here is some performance data collected on Mistral-7B
model with Xeon-8480:
8 threads | prompt length=32 past_len=32 | prompt length=1 past_len=32
-- | -- | --
ORT-main | 1220ms | 263ms
Neural-speed | 564ms | 87ms
ORT-this PR|597ms|120ms
Although `Neural-speed` and `ORT-this PR` use the same int4 kernel code,
there is a 33ms(87ms vs. 120ms) latency gap between the two frameworks.
Through some statistics analysis, the summary latency of `MatMulNBits`
is 86.7ms
The summary latency of all int4 GEMMs in `Neural-speed` is 84.8ms. So
other OPs introduce an extra 30ms latency.
The performance of MatMulNBits in this PR meets our expectations.
### Remain Issues
1. For hybrid CPUs, like core 12900K, the ONNXRuntime thread pool uses
TaskGranularityFactor to scale its number of threads. This is not
expected in our code design. It may slow down the hybrid CPU performance
by 30~40%.
2. Prepack uses a single thread which is very slow to init a session.
3. MatMulNBits with zero points will fall through to COMP_FP32 even
accuracy_level=4. Our COMP_INT8 IGemmCore with zero points process is
not optimized for now. It will be updated in the future. So, for an int4
model with zero points, whether the accuracy_level is 0 or 4 will be no
difference.
### Description
Update DML version to 1.13.1
### 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
[DirectML EP] Add DML EP registration for Col2Im operator
### Motivation and Context
Add Col2Im support for opset 18.
This operator is implemented as the DirectML Fold operator.
---------
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
### Fix build when flash attention and memory efficient attention are
disabled
On a customer env with lower version of CUDA < 11.6. Both flash
attention and memory efficient attention is turned OFF according to
e8f33b54ba/cmake/CMakeLists.txt (L701).
So
e8f33b54ba/cmake/external/cutlass.cmake (L1)
condition check return false. No cutlass lib is built.
```
Turn off flash attention since CUDA compiler version < 11.6
```
While, the kernels in
https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/contrib_ops/cuda/moe/ft_moe
are depending on cutass for its build, so we get error like this:
```
[ 77%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_fp16_fp16.cu.o
In file included from /tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_fp16_fp16.cu:17:
/tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_template.h:23:10: fatal error: cutlass/array.h: No such file or directory
23 | #include "cutlass/array.h"
| ^~~~~~~~~~~~~~~~~
compilation terminated.
In file included from /tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_fp16_fp16.cu:17:
/tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_template.h:23:10: fatal error: cutlass/array.h: No such file or directory
23 | #include "cutlass/array.h"
| ^~~~~~~~~~~~~~~~~
compilation terminated.
In file included from /tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_fp16_fp16.cu:17:
/tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_template.h:23:10: fatal error: cutlass/array.h: No such file or directory
23 | #include "cutlass/array.h"
| ^~~~~~~~~~~~~~~~~
compilation terminated.
In file included from /tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_fp16_fp16.cu:17:
/tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_template.h:23:10: fatal error: cutlass/array.h: No such file or directory
23 | #include "cutlass/array.h"
| ^~~~~~~~~~~~~~~~~
compilation terminated.
fatal : Could not open input file /tmp/tmpxft_00044da3_00000000-11_moe_gemm_kernels_fp16_fp16.compute_60.cpp1.ii
make[2]: *** [CMakeFiles/onnxruntime_providers_cuda.dir/build.make:6290: CMakeFiles/onnxruntime_providers_cuda.dir/tmp/onnxruntime/onnxruntime/contrib_ops/cuda/moe/ft_moe/moe_gemm_kernels_fp16_fp16.cu.o] Error 1
make[2]: *** Waiting for unfinished jobs....
make[1]: *** [CMakeFiles/Makefile2:2210: CMakeFiles/onnxruntime_providers_cuda.dir/all] Error 2
make: *** [Makefile:166: all] Error 2
Traceback (most recent call last):
File "/tmp/onnxruntime/tools/ci_build/build.py", line 2746, in <module>
sys.exit(main())
File "/tmp/onnxruntime/tools/ci_build/build.py", line 2639, in main
build_targets(args, cmake_path, build_dir, configs, num_parallel_jobs, args.target)
File "/tmp/onnxruntime/tools/ci_build/build.py", line 1527, in build_targets
run_subprocess(cmd_args, env=env)
File "/tmp/onnxruntime/tools/ci_build/build.py", line 824, in run_subprocess
return run(*args, cwd=cwd, capture_stdout=capture_stdout, shell=shell, env=my_env)
File "/tmp/onnxruntime/tools/python/util/run.py", line 49, in run
completed_process = subprocess.run(
File "/opt/conda/lib/python3.8/subprocess.py", line 516, in run
raise CalledProcessError(retcode, process.args,
```
### Motivation and Context
To summarize, there are two cases we will have build failure for Linux
CUDA build:
1. User use cuda version < 11.6
2. User disabled Flash attention and memory efficient attention
explictly with onnxruntime_USE_FLASH_ATTENTION and
onnxruntime_USE_MEMORY_EFFICIENT_ATTENTION
Add ACL as the DNNL runtime option for aarch64 platforms. Update
makefile and the python wheel build script.
### Description
<!-- Describe your changes. -->
Add ACL as the DNNL runtime option for aarch64 platforms. Update
makefile and the python wheel build script.
### 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. -->
This is to enable the optimized ACL gemm kernels for dnnl execution
provider on aarch64 platform.
### Description
Truncate traling non-existing arguments.
Make sure we do not skip on the non-existing arguments in the middle,
because shape inferece relies on their proper position.
This also affects the argument position in the Edges that must be
properly rebuilt
each time If node branch is inlined.
Make sure that when we rename Defs in subgraphs, new renamed defs are
created in those subgraphs
instead of pointing to outer scope defs.
Add unit test.
### Motivation and Context
This is a follow up for
https://github.com/microsoft/onnxruntime/pull/18105
Currently, the non-trailing arguments are simply ignored and the edges
are created
with potentially incorrect positions.
### Description
<!-- Describe your changes. -->
1. Introduce MoE CUDA op to ORT based on FT implementation.
2. Upgrade cutlass to 3.1.0 to avoid some build failures on Windows.
Remove patch file for cutlass 3.0.0.
3. Sharded MoE implementation will come with another PR
limitation: __CUDA_ARCH__ >= 700
### 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. -->
Add 32-bit patch binary and infra to fallback to it. The Azure devops
Windows CIs are missing patch.exe from their git install for some reason
so the default `find_package(Patch)` fails as that is where it expects
to find it.
Remove Eigen patch. Underlying issue was fixed in source 3 years ago by
c6c84ed961
and the patch command is invalid (args are for git apply not patch).
### 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. -->
Make usage of patch consistent across all CIs
Fix https://github.com/microsoft/onnxruntime/issues/15248
### Description
Some CMake scripts reference Microsoft.GSL::GSL. Most of the time, the
GSL package that is found on the system is used. However, when cuda is
enabled, it is downloaded and patched. Most CMake scripts rely on the
first case and forget about the second. This patch makes the second case
behave like the first case.
### Motivation and Context
This is an issue that occurs 'in the wild'. For example, I had to patch
this to be able to enable the CUDA provider for the onnxruntime conan
package (see https://github.com/conan-io/conan-center-index/pull/20392).
### Description
<!-- Describe your changes. -->
Fix the broken pieces due to the latest Abseil update.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
Make the debugging bearable.
### Description
<!-- Describe your changes. -->
Update XNNPACK to latest version
- adds fp16 kernels and various other improvements
- requires pthreadpool update as well
Most code updates in the XNNPACK EP are to adjust to the new XNNPACK API
- 'setup' is split into 'reshape' and 'setup'
- some ops use a workspace buffer
- copied workspace allocation from XNNPACK unit test code
- some suffixes changed
Added wrapper for XNNPACK caches to base XNNPACK EP kernel
- simplifies usage
- XNNPACK split out the code and weights caches, but the code cache
isn't currently usable via the public API
- we could use the internal types if we think it's required for
performance reasons. non-trivial though as we'd need to propagate ifdef
values from the XNNPACK build up to the ORT build.
- using XNNPACK internals would also mean we would not be able to
support using a pre-build XNNPACK package
- not an issue currently
Fixed opset registration for internal NHWC domain
- was not being tied to the ONNX version, so nodes inserted by layout
transformation had the incorrect opset
- a number of other places needed updating once this issue was fixed
Remove support for NCHW Resize from XNNPACK EP so it's NHWC only
- we only supported NCHW for fp32,
- doing so adds complexity in multiple places (XNNPACK EP kernel
implementation, layout transformation and transpose optimization)
- unclear if that complexity provides any benefit. can add back if
required by production scenario
### 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. -->
We're looking at enabling fp16 support for CoreML and NNAPI. If we do
that we need a good fallback story if the CPU EP will be used. The
XNNPACK fp16 kernels will hopefully provide that.
NOTE: This PR doesn't add fp16 support to the XNNPACK EP kernels. That
can be done as required in separate EPs and should be relatively simple
to do.
### Description
Google test can be built either with absl/re2 or not. This PR enables
the build option so that google test framework can print out a nice
stacktrace when something went wrong. It helps locate test errors in CI
build pipelines.
Also, Google test will remove the build option and make it always ON. So
sooner or later we must make this change.
This reverts commit bb136f86c8, then
re-implement it in a different way.
I reverted the original change, then added a version constraint to the
find_package args.
If you still found it picks up wrong gtest version after this change,
you may disable `find_package` by setting
'FETCHCONTENT_TRY_FIND_PACKAGE_MODE' to NEVER. For example, the latest
gtest version is 1.14.0. If at a later time Google releases a new
version of gtest and that one is incompatible with the ONNX Runtime
source code you get today and your dev environment already installed the
new version and you do not want to create a new clean build environment
that is without the package, you can add `--cmake_extra_defines
FETCHCONTENT_TRY_FIND_PACKAGE_MODE=NEVER` to your build command to solve
the problem.
### Description
1. `onnxruntime_fetchcontent_makeavailable` works around unconditional
install commands so that can be used instead of `FetchContent_Populate`
2. This dependency is Windows specific, mark it as such.
### Motivation and Context
1. This simplifies `cmake/external/wil.cmake` not to do anything
specific wether WIL was fetched or found
2. Given it's specific to Windows, it might not be available on other OS
in specific air-gapped environment such as
[conan-center-index](https://github.com/conan-io/conan-center-index).
This allows downstream builds not to require specific patches for
something not required by the build in the first place.
### Description
Remove the onnxruntime-extensions submodule since it now was used via
cmake FetchContent
### Motivation and Context
The submodule relies on an outdated version of the extensions, and the
build instructions should be updated to eliminate any confusion.
### Description
This change upgrade emsdk to 3.1.44.
Because backend is upgraded to LLVM 16, so need to fix a lot of build
failures caused by "-Wshorten-64-to-32".
most of the build failures comes from generated `onnx.pb.h`, and this
can be fixed by including "core/graph/onnx_protobuf.h", which detects
and ignore shorten-64-to-32 warnings.
### Description
<!-- Describe your changes. -->
Adjust nativs to display tagged strings.
### 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. -->
Hard to debug without seeing names.
### Description
1. As a follow-up of #16761, this PR allows build ORT on iOS/Android
without the need to explicitly specify a protoc path. #16761 is for
WASM. This one is for iOS/Android
2. Update the MacOS/Linux build scripts that build/install protobuf from
source. Make them be more flexible. Add the support for
RedHatEnterprise(ubi), which will needed for upgrading the base image
from centos:7 to ubi:8.
3. Update tools/ci_build/github/pai/rocm-ci-pipeline-env.Dockerfile :
the docker file's base image has preinstalled protobuf in /usr/local, we
should uninstall them to avoid conflicts.
### Description
Changes allow downloading prebuilt protoc compiler when building
WebAssebly version on mac systems.
Otherwise it tries to build a js/wasm version of protoc and throws an
error while executing it: "protoc.js permission denied"
### Motivation and Context
I need to switch between my main working computer and a PC to make
changes to WebAssebly build. Would like not to do that anymore.
Otherwise, an unsupported version of gtest/gmock will be found at
/opt/conda/include for ROCm builds. Though this issue was initially
found for ROCm builds, the issue is generic. onnxruntime requires a
specific version of googletest and should not rely on locating
googletest using find_package.
The ROCm error was:
```
In file included from /opt/conda/include/gmock/gmock-spec-builders.h:75,
from /opt/conda/include/gmock/gmock-generated-function-mockers.h:47,
from /opt/conda/include/gmock/gmock-function-mocker.h:39,
from /opt/conda/include/gmock/gmock.h:61,
from /stage/onnxruntime/onnxruntime/test/util/test_utils.cc:17:
/opt/conda/include/gmock/gmock-matchers.h: In instantiation of ‘bool testing::internal::PointwiseMatcher<TupleMatcher, RhsContainer>::Impl<LhsContainer>::
MatchAndExplain(LhsContainer, testing::MatchResultListener*) const [with LhsContainer = const gsl::span<const float>&; TupleMatcher = testing::internal::
FloatingEq2Matcher<float>; RhsContainer = gsl::span<const float>]’:
/opt/conda/include/gmock/gmock-matchers.h:2303:10: required from here
/opt/conda/include/gmock/gmock-matchers.h:2312:48: error: no type named ‘const_iterator’ in ‘testing::internal::PointwiseMatcher<testing::internal::
FloatingEq2Matcher<float>, gsl::span<const float> >::Impl<const gsl::span<const float>&>::LhsStlContainer’ {aka ‘class gsl::span<const float>’}
```
- Fix flatbuffers flatc warning, unused-but-set-variable.
- Address `-Wshorten-64-to-32` warnings (fix in our code, allow in dependencies' code).
- Update CI builds to use Xcode 14.3.
- Update minimum iOS version to 12.0.
- Update Mac hosted agents to MacOS 13 where possible.
