### Description
Use a common set of prebuilt manylinux base images to build the
packages, to avoid building the manylinux part again and again. The base
images can be used in GenAI and other projects too.
This PR also updates the GCC version for inference python CUDA11/CUDA12
builds from 8 to 11. Later on I will update all other CUDA pipelines to
use GCC 11, to avoid the issue described in
https://github.com/onnx/onnx/issues/6047 and
https://github.com/microsoft/onnxruntime-genai/issues/257 .
### Motivation and Context
To extract the common part as a reusable build infra among different
ONNX Runtime projects.
### Description
<!-- Describe your changes. -->
This branch is based on rel-1.18.0 and supports TensorRT 10-GA.
### 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 reverts commit f396748ed6.
### 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
<!-- Describe your changes. -->
Add Nuget package changes for adding new 'net6.0-maccatalyst' platform.
The output ORT Nuget package was manually tested and verified in a .NET
MAUI app setup.
### 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: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Yi Zhang <zhanyi@microsoft.com>
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
### Description
update with ONNX 1.16.0 branch according to
https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md
ONNX 1.16.0 release notes:
https://github.com/onnx/onnx/releases/tag/v1.16.0
#### Updated ops for CPU EP:
- DequantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block dequantization support
- QuantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block quantization support
- Cast(21)
- Missing int4 and uint4 support
- CastLike(21)
- Missing int4 and uint4 support
- ConstantOfShape(21)
- Missing int4 and uint4 support
- Identity(21)
- Missing int4 and uint4 support
- If(21)
- Missing int4 and uint4 support
- Loop(21)
- Missing int4 and uint4 support
- Reshape(21)
- Missing int4 and uint4 support
- Scan(21)
- Missing int4 and uint4 support
- Shape(21)
- Missing int4 and uint4 support
- Size(21)
- Missing int4 and uint4 support
- Flatten(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Pad(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Squeeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Transpose(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Unsqueeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
#### Unimplemented opset 21 features/ops
- int4 and uint4 data type
- QLinearMatMul(21)
- GroupNormalization(21)
- ai.onnx.ml.TreeEnsemble(5)
### 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. -->
### Disabled tests
#### ORT Training
orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py
- test_ort_custom_ops: Potential shape inference bug for custom ops
#### Python quantization unit tests
test/onnx/python/quantization (shape inference bug)
- test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16
- test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16
- test_op_gemm.py: test_quantize_qop_gemm_s8s8
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3
- test_op_matmul.py: test_quantize_matmul_u8u8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution
- test_op_relu.py: test_quantize_qop_relu_s8s8
#### ONNX tests
- test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a
maxpool output size bug and added this test. Enable this test when [ORT
PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged.
Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741).
- test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op
ai.onnx.ml.TreeEnsemble
- test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same
- test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same
- test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same
- test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4
yet
- test_cast_INT4_to_INT8_cpu: same
- test_cast_UINT4_to_FLOAT_cpu: same
- test_cast_UINT4_to_UINT8_cpu: same
- test_cast_INT4_to_FLOAT_cuda
- test_cast_INT4_to_INT8_cuda
- test_cast_UINT4_to_FLOAT_cuda
- test_cast_UINT4_to_UINT8_cuda
- test_constantofshape_float_ones_cuda: ConstantOfShape(21) not
implemented for cuda
- test_constantofshape_int_shape_zero_cuda: same
- test_constantofshape_int_zeros_cuda: same
- test_flatten_axis0_cuda: Flatten(21) not implemented for cuda
- test_flatten_axis1_cuda: same
- test_flatten_axis2_cuda: same
- test_flatten_axis3_cuda: same
- test_flatten_default_axis_cuda: same
- test_flatten_negative_axis1_cuda: same
- test_flatten_negative_axis2_cuda: same
- test_flatten_negative_axis3_cuda: same
- test_flatten_negative_axis4_cuda: same
- test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not
implemented in ORT yet
- test_qlinearmatmul_2D_int8_float32_cpu: same
- test_qlinearmatmul_2D_uint8_float16_cpu: same
- test_qlinearmatmul_2D_uint8_float32_cpu: same
- test_qlinearmatmul_3D_int8_float16_cpu: same
- test_qlinearmatmul_3D_int8_float32_cpu: same
- test_qlinearmatmul_3D_uint8_float16_cpu: same
- test_qlinearmatmul_3D_uint8_float32_cpu: same
- test_qlinearmatmul_2D_int8_float16_cuda: same
- test_qlinearmatmul_2D_int8_float32_cuda: same
- test_qlinearmatmul_2D_uint8_float16_cuda: same
- test_qlinearmatmul_2D_uint8_float32_cuda: same
- test_qlinearmatmul_3D_int8_float16_cuda: same
- test_qlinearmatmul_3D_int8_float32_cuda: same
- test_qlinearmatmul_3D_uint8_float16_cuda: same
- test_qlinearmatmul_3D_uint8_float32_cuda: same
- test_size_cuda: Size(21) not implemented for cuda
- test_size_example_cuda: same
- test_dequantizelinear_blocked: Missing implementation for block
dequant for DequantizeLinear(21)
- test_quantizelinear_blocked_asymmetric: Missing implementation for
block quant for QuantizeLinear(21)
- test_quantizelinear_blocked_symmetric: Missing implementation for
block quant for QuantizeLinear(21)
---------
Signed-off-by: liqunfu <liqun.fu@microsoft.com>
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
### Description
Add NPU to list of device supported.
Added changes for Support to OV 2024.0
Nuget packages removes packaging of OpenVINO DLL
Bug Fixes with Python API
Reverted Dockerfiles not being maintained.
### Motivation and Context
NPU Device has been introduced by Intel in latest client systems
OpenVINO 2024.0 release is out.
---------
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: Ubuntu <ubuntu@ubuntu-118727.iind.intel.com>
Co-authored-by: hmamidix <hemax.sowjanya.mamidi@intel.com>
Co-authored-by: vthaniel <vishnudas.thaniel.s@intel.com>
Co-authored-by: saurabhkale17 <saurabh1.kale@intel.com>
### Description
* Update name of existing dockerfiles and add support to test latest
TensorRT EA binary located in the image
* Add cuda 12.3/cuDNN 9/TensorRT 8.6 dockerfile
* Add detail to CI prompts and configs
Instruction to test latest TRT via BIN:
1. Select `BIN` in TensorRT Version
2. In Variables, update related tarCudaVersion, **clear**
tarCudnnVersion (not required in latest TRT tar binary) , and path to
binary.
### Description
Add Whisper Conversion and E2E into Big Models pipeline
### 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: Your Name <your@email.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
This PR upgrades ORTModule's default opset from 15 to 17. Opset 17 is
the final opset supported by torchscript exporter
(https://github.com/pytorch/pytorch/pull/107829)
### 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. -->
Engineering excellence contribution for ORT Training DRI.
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
This pull request includes a small change to the
`Dockerfile.manylinux2_28_cuda` file in the
`tools/ci_build/github/linux/docker` directory. The change corrects the
`PREPEND_PATH` argument from `/usr/local/cuda/binet` to
`/usr/local/cuda/bin`, ensuring the correct path to CUDA binaries is
set.
### Description
1. Add two build jobs for enabling Address Sanitizer in CI. One for
Windows CPU, One for Linux CPU.
2. Set default compiler flags/linker flags in build.py for normal
Windows/Linux/MacOS build. This can help control compiler flags in a
more centralized way.
3. All Windows binaries in our official packages will be built with
"/PROFILE" flag. Symbols of onnxruntime.dll can be found at [Microsoft
public symbol
server](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/microsoft-public-symbols).
Limitations:
1. On Linux Address Sanitizer ignores RPATH settings in ELF binaries.
Therefore once Address Sanitizer is enabled, before running tests we
need to manually set LD_LIBRARY_PATH properly otherwise
libonnxruntime.so may not be able to find custom ops and shared EPs.
4. On Linux we also need to set LD_PRELOAD before running some tests(if
the main executable, like python, is not built with address sanitizer.
On Windows we do not need to.
5. On Windows before running python tests we should manually copy
address sanitizer DLL to the onnxruntime/capi directory, because python
3.8 and above has enabled "Safe DLL Search Mode" that wouldn't use the
information provided by PATH env.
6. On Linux Address Sanitizer found a lot of memory leaks from our
python binding code. Therefore right now we cannot enable Address
Sanitizer when building ONNX Runtime with python binding.
7. Address Sanitizer itself uses a lot of memory address space and
delays memory deallocations, which is easy to cause OOM issues in 32-bit
applications. We cannot run all the tests in onnxruntime_test_all in
32-bit mode with Address Sanitizer due to this reason. However, we still
can run individual tests in such a way. We just cannot run all of them
in one process.
### Motivation and Context
To catch memory issues.
### Description
Adding python3.12 support to ORT
### 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
Change all macOS python packages to use universal2, to reduce the number
of packages we have.
### Motivation and Context
According to [wikipedia](https://en.wikipedia.org/wiki/MacOS_Big_Sur),
macOS 11 is the first macOS version that supports universal 2. And it is
the min macOS version we support. So we no longer need to maintain
separate binaries for different CPU archs.
### Description
Fixes a failure in the ortmodule nightly pipeline.
### 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
Updating transformers package in test pipeline to fix a security
vulnerability.
### 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. -->
Build ORT-training packaging pipeline for CUDA 12.2
### 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 will help any customer using CUDA 12 and would not need to build
ORT-training from source
Test run:
https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=382993&view=logs&s=130be951-c2f3-5601-5709-434b5e50ddb0
Recent PyTorch breaks DORT CI and [a
patch](https://github.com/pytorch/pytorch/pull/113697) has been merged
into PyTorch main. In order to update DORT's CI, we made dummy change in
this PR.
1. Now we use a released version of ONNX, so we can directly download a
prebuilt package from pypi.org. We do not need to build one from source.
2. Update protobuf python package's version to match the C/C++ version
we are using.
3. Update tensorboard python python because the current one is
incompatible with the newer protobuf version.
### Description
Version 41.0.0 currently used has vulnerabilities.
### Motivation and Context
See [Vulnerable OpenSSL included in cryptography
wheels](https://github.com/advisories/GHSA-v8gr-m533-ghj9)
### 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. -->
- Update ROCm and MIGraphX CI to ROCm5.7
- Simplify test exculde file. Some tests will output `registered
execution providers ROCMExecutionProvider were unable to run the model.`
if they cannot run.
- Add `enable_training` build argument for MIGraphX pipeline.
Python package pipeline fails due to "tokenizers" compilation. Since
"tokenizers" is a dep of "transformers", we update its version and hope
a new solution had been there.
```
error: casting `&T` to `&mut T` is undefined behavior, even if the reference is unused, consider instead using an `UnsafeCell`
--> tokenizers-lib/src/models/bpe/trainer.rs:517:47
```
### Description
this is for ORT 1.17.0 - make ORT to use ONNX release 1.15.0 branch. Eventually will update to the release tag once ONNX 1.15.0 is released
### Motivation and Context
Prepare for ORT 1.17.0 release. People can start work on new and updated ONNX ops in ORT.
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
### Description
1. Remove 'dnf update' from docker build scripts, because it upgrades TRT
packages from CUDA 11.x to CUDA 12.x.
To reproduce it, you can run the following commands in a CentOS CUDA
11.x docker image such as nvidia/cuda:11.8.0-cudnn8-devel-ubi8.
```
export v=8.6.1.6-1.cuda11.8
dnf install -y libnvinfer8-${v} libnvparsers8-${v} libnvonnxparsers8-${v} libnvinfer-plugin8-${v} libnvinfer-vc-plugin8-${v} libnvinfer-devel-${v} libnvparsers-devel-${v} libnvonnxparsers-devel-${v} libnvinfer-plugin-devel-${v} libnvinfer-vc-plugin-devel-${v} libnvinfer-headers-devel-${v} libnvinfer-headers-plugin-devel-${v}
dnf update -y
```
The last command will generate the following outputs:
```
========================================================================================================================
Package Architecture Version Repository Size
========================================================================================================================
Upgrading:
libnvinfer-devel x86_64 8.6.1.6-1.cuda12.0 cuda 542 M
libnvinfer-headers-devel x86_64 8.6.1.6-1.cuda12.0 cuda 118 k
libnvinfer-headers-plugin-devel x86_64 8.6.1.6-1.cuda12.0 cuda 14 k
libnvinfer-plugin-devel x86_64 8.6.1.6-1.cuda12.0 cuda 13 M
libnvinfer-plugin8 x86_64 8.6.1.6-1.cuda12.0 cuda 13 M
libnvinfer-vc-plugin-devel x86_64 8.6.1.6-1.cuda12.0 cuda 107 k
libnvinfer-vc-plugin8 x86_64 8.6.1.6-1.cuda12.0 cuda 251 k
libnvinfer8 x86_64 8.6.1.6-1.cuda12.0 cuda 543 M
libnvonnxparsers-devel x86_64 8.6.1.6-1.cuda12.0 cuda 467 k
libnvonnxparsers8 x86_64 8.6.1.6-1.cuda12.0 cuda 757 k
libnvparsers-devel x86_64 8.6.1.6-1.cuda12.0 cuda 2.0 M
libnvparsers8 x86_64 8.6.1.6-1.cuda12.0 cuda 854 k
Installing dependencies:
cuda-toolkit-12-0-config-common noarch 12.0.146-1 cuda 7.7 k
cuda-toolkit-12-config-common noarch 12.2.140-1 cuda 7.9 k
libcublas-12-0 x86_64 12.0.2.224-1 cuda 361 M
libcublas-devel-12-0 x86_64 12.0.2.224-1 cuda 397 M
Transaction Summary
========================================================================================================================
```
As you can see from the output, they are CUDA 12 packages.
The problem can also be solved by lock the packages' versions by using
"dnf versionlock" command right after installing the CUDA/TRT packages.
However, going forward, to get the better reproducibility, I suggest
manually fix dnf package versions in the installation scripts like we do
for TRT now.
```bash
v="8.6.1.6-1.cuda11.8" &&\
yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo &&\
yum -y install libnvinfer8-${v} libnvparsers8-${v} libnvonnxparsers8-${v} libnvinfer-plugin8-${v} libnvinfer-vc-plugin8-${v}\
libnvinfer-devel-${v} libnvparsers-devel-${v} libnvonnxparsers-devel-${v} libnvinfer-plugin-devel-${v} libnvinfer-vc-plugin-devel-${v} libnvinfer-headers-devel-${v} libnvinfer-headers-plugin-devel-${v}
```
When we have a need to upgrade a package due to security alert or some
other reasons, we manually change the version string instead of relying
on "dnf update". Though this approach increases efforts, it can make our
pipeines more stable.
2. Move python test to docker
### Motivation and Context
Right now the nightly gpu package mixes using CUDA 11.x and CUDA 12.x
and the result package is totally not usable(crashes every time)
manylinux build is used for nightly packaging generation and it's hard
to capture issue in time when related files change. This PR add
manylinux build in CI.
### Description
install dotnet 6.0 in the docker image.
move C# build and test into docker.
### Motivation and Context
### Note
The Unit tests and Symbolic shape infer's migration will be in another
PR.
### Description
1. Update docker files and their build instructions.
ARM64 and x86_64 can use the same docker file.
2. Upgrade Linux CUDA pipeline's base docker image from CentOS7 to UBI8
AB#18990
