### Description
<!-- Describe your changes. -->
It seems after CI updated to py310, numpy got updated to 2.0 and sympy
1.2 failed to cast float numpy array.
Pointing sympy to 1.13 when py>=3.9 and re-enable unit test
### 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. -->
Error: Linux CPU
CI
### Description
A break-down PR of https://github.com/microsoft/onnxruntime/pull/22651
Op API change only.
- add template to functions and classes that support fp32 and fp16
- rename functions, classes and files that support fp32 and fp16 from
SQNBxxx to QNBxxx
### 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. -->
### 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 registers GroupNormalization for opset 21
### 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
Current linux-ci-pipeline was broken due to missing parameters from
`py-packaging-linux-test-cpu.yml` template
### Motivation and Context
Fix Linux CI pipeline
### Description
Update stable diffusion benchmark:
(1) allow IO binding for optimum.
(2) do not use num_images_per_prompt across all engines for fair
comparison.
Example to run benchmark of optimum on stable diffusion 1.5:
```
git clone https://github.com/tianleiwu/optimum
cd optimum
git checkout tlwu/diffusers-io-binding
pip install -e .
pip install -U onnxruntime-gpu
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion
git checkout tlwu/benchmark_sd_optimum_io_binding
pip install -r requirements/cuda12/requirements.txt
optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 --task text-to-image ./sd_onnx_fp32
python optimize_pipeline.py -i ./sd_onnx_fp32 -o ./sd_onnx_fp16 --float16
python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16
python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 --use_io_binding
```
Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without
IO Binding; IO binding gains 16ms, or 2.7%,
### Motivation and Context
Optimum is working on enabling I/O binding:
https://github.com/huggingface/optimum/pull/2056. This could help
testing the impact of I/O binding on the performance of the stable
diffusion.
### Description
Enable the ConvReplaceWithQLinear graph optimization when using the ACL
execution provider.
### Motivation and Context
Fixes an issue where quantized Conv nodes followed by ReLU don't get
converted to QLinearConv, so ACL sees the weights as mutable and
therefore cannot run the Conv node.
Signed-off-by: Michael Tyler <michael.tyler@arm.com>
### Description
Making ::p optional in the Linux python CUDA package pipeline
### Motivation and Context
Linux stage from Python-CUDA-Packaging-Pipeline has failed since merge
of #22773
### Description
This PR fixes the spelling of the key value of the GRU operator in the
map in the `GetSupportedNodes` function (Gru -> GRU) and removes the
data type check for the fifth input (sequence_lens) of the GRU operator.
PTAL, thanks!
Add new provider option `trt_op_types_to_exclude`:
- User can provide op type list to be excluded from running on TRT
- e.g. `trt_op_types_to_exclude="MaxPool"`
There is a known performance issue with the DDS ops (NonMaxSuppression,
NonZero and RoiAlign) from TRT versions 10.0 to 10.7. TRT EP excludes
DDS ops from running on TRT by default, user can override default value
with empty string to include all ops.
The performance cost of falling back to the CPU EP is high for several
resampling nodes and causes multiple partitions in SD Turbo and VAE
decoder. Since the asymmetric mode with nearest to floor and integer
scales is identical to half_pixel anyway, stick with the WebNN EP.
### Description
<!-- Describe your 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. -->
### Description
A break down PR of https://github.com/microsoft/onnxruntime/pull/22651
Add fp16 kernels.
### 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. -->
WebNN doesn't provide dedicate op for LRN, use a couple of WebNN ops to
emulate it in WebNN EP:
pow -> transpose -> pad -> averagePool -> transpose -> mul -> add -> pow
-> div
@Honry @fdwr PTAL, thanks!
### Description
This PR registers the following opset 21 operators:
Idenity-21
OlieanrMatmul-21
### 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. -->
`Module.jsepRegisterMLConstant` will be shorten by Closure Compiler in
offical release, this would cause undefined error.
Fix it by using `Module['jsepRegisterMLConstant']`.
### Description
Fixes a unit test that would fail intermittently due to an existing bug
with Pad (reflect mode). When the number of padded values is >= the
inner dimension size, the ORT Pad implementation accesses invalid
memory. This PR makes the number of padding values less than the inner
dimension size to avoid triggering the bug.
### Motivation and Context
See related issues:
https://github.com/microsoft/onnxruntime/issues/8265https://github.com/microsoft/onnxruntime/issues/11828https://github.com/microsoft/onnxruntime/issues/20801
Here's a valgrind trace obtained on a Linux machine (with
`sess_options.enable_cpu_mem_arena = False`)
```
==864228== Invalid read of size 4
==864228== at 0x2716272A: void onnxruntime::PadInnermostAxis<unsigned int>(unsigned int*, unsigned int*, long, unsigned long) (pad.cc:370)
==864228== by 0x2715D213: onnxruntime::common::Status onnxruntime::PadImpl<unsigned int>(onnxruntime::OpKernelContext*, absl::lts_20240722::InlinedVector<long, 10ul, std::allocator<long> > const&, absl::lts_20240722::InlinedVector<long, 10ul, std::allocator<long> > const&, onnxruntime::Mode const&, unsigned int) (pad.cc:551)
==864228== by 0x2715B2BB: onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const (pad.cc:725)
==864228== by 0x276FF6A7: onnxruntime::ExecuteKernel(onnxruntime::StreamExecutionContext&, unsigned long, unsigned long, bool const&, onnxruntime::SessionScope&) (sequential_executor.cc:484)
==864228== by 0x276F4A04: onnxruntime::LaunchKernelStep::Execute(onnxruntime::StreamExecutionContext&, unsigned long, onnxruntime::SessionScope&, bool const&, bool&) (execution_steps.cc:73)
...
```
The above is obtained with the basic Pad(reflect) example on the [ONNX
Pad operator spec
page](https://onnx.ai/onnx/operators/onnx__Pad.html#summary):
```python
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
pads = [0, 2, 0, 0]
mode = 'reflect'
# Expected output by ONNX spec
expected_output = [
[1.0, 1.2, 1.0, 1.2],
[2.3, 3.4, 2.3, 3.4],
[4.5, 5.7, 4.5, 5.7],
]
# Bugged output from onnxruntime has invalid/uninitialized data for the first element in the inner dimension
# invalid data may be 0.0, inf, nan, etc.
ort_output = [
[inf, 1.2, 1.0, 1.2],
[inf, 3.4, 2.3, 3.4],
[inf, 5.7, 4.5, 5.7],
]
```
The previous PR was reverted because it causes the whole model to
fallback when there is output shape info missing. This PR fixes the
issue by removing redundant fallbacks.
### Description
Fixes command for building Linux python packages by preventing an empty
`-p` command-line option from being passed to a subsequent build script:
1f3b675453/tools/ci_build/github/linux/run_python_dockerbuild.sh (L37)
### Motivation and Context
A recent [PR
](https://github.com/microsoft/onnxruntime/pull/22773)introduced a new
optional command-line option (`-p`) to pass custom python exe paths. We
need to check if the option is empty before forwarding the option to a
separate build script.
### Description
This PR Fix warning - `LegacyKeyValueFormat: "ENV key=value" should be
used instead of legacy "ENV key value" format` from all Dockerfile
### 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
Fixes#22512, MatMul, Add can be fused into a single Gemm even if
tensors dimensions are > 2. The PR excludes that cases.
### Motivation and Context
ORT crashes on valid models due to that unexpected fusion.
### Description
Support to set EPdynamic options in OVEP
### Motivation and Context
relate to https://github.com/microsoft/onnxruntime/pull/22282
---------
Co-authored-by: Javier E. Martinez <javier.e.martinez@intel.com>
For per-axis quantization/dequantization, WebNN requires the scale and
zero_point inputs to be broadcastable. Axis should be used for reshape
these two inputs.
### Description
<!-- Describe your changes. -->
[VitisAI] Cache node subgraph when necessary
### 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: Zhenze Wang <zhenzew@xilinx.com>
Co-authored-by: zhenzew <zhenzew@amd.com>
### Description
1. Add XNNPack build on Linux ARM64
2. Build only one python wheel for PR request.
[AB#49763](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/49763)
### Motivation and Context
Why I add xnnpack build on Linux ARM64 rather than Windows ARM64.
Becuase KleidiAI doesn't support Windows
```
IF(XNNPACK_TARGET_PROCESSOR STREQUAL "arm64" AND XNNPACK_ENABLE_ARM_I8MM AND NOT CMAKE_C_COMPILER_ID STREQUAL "MSVC")
IF (XNNPACK_ENABLE_KLEIDIAI)
MESSAGE(STATUS "Enabling KleidiAI for Arm64")
ENDIF()
ELSE()
SET(XNNPACK_ENABLE_KLEIDIAI OFF)
ENDIF()
```
---------
### Description
Ignore all whitespace lint messages for cpplint. Remove redundant
configs in dml/.
### Motivation and Context
They are handled automatically by clang-format and creates too much
noise in the PR files tab.
### Description
Adds `reduce_range` option to `get_qdq_config()`
### Motivation and Context
Make it easier to set this option when calling get_qdq_config().
Otherwise, user has to set the option manually.
### Description
<!-- Describe your 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. -->
This PR make MatMul shaders not depend on inputs broadcasting pattern,
but only depend on input ranks and their shape provided in uniform. This
change fix the issue that currently shaders code are different for
different broadcasting, but have identical cache key and results in
wrong cache hit.
### Description
Fix a build error seen with GCC 11 when building at Homebrew on our
Linux x86_64 Ubuntu 22.04 CI (GitHub action runner).
### Motivation and Context
When building latest v1.20.0 at Homebrew
(https://github.com/Homebrew/homebrew-core/pull/196547), we hit a build
failure with GCC 11:
```
[ 65%] Building CXX object CMakeFiles/onnxruntime_optimizer.dir/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc.o
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/shims/linux/super/g++-11 -DCPUINFO_SUPPORTED_PLATFORM=1 -DEIGEN_MPL2_ONLY -DEIGEN_USE_THREADS -DENABLE_CPU_FP16_TRAINING_OPS -DHAS_STRING_VIEW=1 -DNSYNC_ATOMIC_CPP11 -DONLY_C_LOCALE=0 -DONNX_ML=1 -DONNX_NAMESPACE=onnx -DORT_ENABLE_STREAM -DORT_NO_RTTI -DPLATFORM_POSIX -DPROTOBUF_USE_DLLS -D_GNU_SOURCE -I/tmp/onnxruntime-20241103-6403-lh3bwj/build/_deps/utf8_range-src -I/tmp/onnxruntime-20241103-6403-lh3bwj/include/onnxruntime -I/tmp/onnxruntime-20241103-6403-lh3bwj/include/onnxruntime/core/session -I/tmp/onnxruntime-20241103-6403-lh3bwj/build/_deps/pytorch_cpuinfo-src/include -I/tmp/onnxruntime-20241103-6403-lh3bwj/build -I/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime -I/tmp/onnxruntime-20241103-6403-lh3bwj/build/_deps/onnx-src -I/tmp/onnxruntime-20241103-6403-lh3bwj/build/_deps/onnx-build -ffunction-sections -fdata-sections -Wno-restrict -DCPUINFO_SUPPORTED -O3 -DNDEBUG -fPIC -fno-rtti -Wall -Wextra -Wno-deprecated-copy -Wno-tautological-pointer-compare -Wno-nonnull-compare -Wno-ambiguous-reversed-operator -Wno-deprecated-anon-enum-enum-conversion -Wno-undefined-var-template -Wno-deprecated-builtins -Wshorten-64-to-32 -Werror -MD -MT CMakeFiles/onnxruntime_optimizer.dir/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc.o -MF CMakeFiles/onnxruntime_optimizer.dir/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc.o.d -o CMakeFiles/onnxruntime_optimizer.dir/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc.o -c /tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc
/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc: In function ‘void onnx_transpose_optimization::Permute1DConstant(onnx_transpose_optimization::api::GraphRef&, onnx_transpose_optimization::api::NodeRef&, onnx_transpose_optimization::api::TensorRef&, size_t, std::string_view, const std::vector<long int>&)’:
/tmp/onnxruntime-20241103-6403-lh3bwj/onnxruntime/core/optimizer/transpose_optimization/onnx_transpose_optimization.cc:1114:10: error: ‘memcpy’ is not a member of ‘std’; did you mean ‘wmemcpy’?
1114 | std::memcpy(dst, src, bytes_per_val);
| ^~~~~~
| wmemcpy
```
It is possible this error may not occur on different GCC versions if
`cstring` has been indirectly included by another header.
### Description
This PR will set default python to 3.10 except
tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml. This is
needed because we are no longer using python 3.8
This PR excludes changes for Big Models CI, because it will require
additional changes. Which will be track in
USER STORY 52729
### 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
With recent changes, below build error is found under AIX.
```
ld: 0706-012 The -p flag is not recognized.
ld: 0706-012 The -a flag is not recognized.
ld: 0706-012 The -t flag is not recognized.
ld: 0706-012 The -h flag is not recognized.
ld: 0706-012 The -= flag is not recognized.
ld: 0706-012 The -$ flag is not recognized.
ld: 0706-012 The -$ flag is not recognized.
ld: 0706-012 The -O flag is not recognized.
ld: 0706-027 The -R IGIN flag is ignored.
collect2: error: ld returned 255 exit status
```
### Motivation and Context
AIX linker doesn't support -rpath option , so blocking this option under
AIX.
### Description
Skip `MatMulIntegerToFloat` fusion in case of DML EP for cases where
model uses Quantization before `MatMulInteger`. This is mainly done to
be resource efficient, and we have better `MatMulInteger` Metacommand
coverage which computes in int data type
### 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 CL make WebGPU backend support subgroup features and thus allow
using subgroup optimizations in the future.
### Description
With this CL WebGPU backends will create devices with subgroups and
subgroups-f16 features (both are under origin trial in Chrome) or
chromium-experimental-subgroups feature enabled whenever available.
### Motivation and Context
This CL would allow WebGPU operator shaders to use subgroup
optimizations in the future, and might get some significant speedup with
these optimization.
### Description
<!-- Describe your changes. -->
* Update CI with TRT 10.6
* Update oss parser to [10.6-GA-ORT-DDS
](https://github.com/onnx/onnx-tensorrt/tree/10.6-GA-ORT-DDS) and update
dependency version
* Update Py-cuda11 CI to use trt10.6
### 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. -->
(There will be 3rd PR to further reduce trt_version hardcoding)
