### Description
<!-- Describe your changes. -->
Add clog back to onnxruntime_EXTERNAL_LIBRARIES.
### 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 iOS packaging pipeline build failure.
### Description
Register Resize op into nhwc schema for Qnn EP.
### Motivation and Context
Resize op is identified as layout sensitive op for Qnn EP, need to
register it into nhwc schema
Change the default behavior to link against the nvonnxparser library
(onnx-tensorrt parser) that is included with the TensorRT package.
Previously, the default behavior was to build and statically link
against the OSS onnx-tensorrt parser.
Historically, we wanted to incorporate the latest commits/fixes from OSS
parser.
These days the OSS parser is not significantly different from the
included parser library so there is less reason to build against it by
default.
By linking with parser shared library from TensorRT library, the major
benefit is it's much easier to
build/link against a minor version update of TensorRT. And OnnxRuntime
can be updated with a new TensorRT minor version by simply replacing
TensorRT libraries with the newer version. (because the parser is no
longer statically linked into onnxruntime)
Added --use_tensorrt_oss_parser to build.py to support the previous
default behavior. (build + static link OSS parser)
Add support for ViT optimization in optimizer.py
As ViT architecture follows BERT rather closely, we can easily reuse
BERT fusions for ViT. The only difference is that ViT does not have
attention mask, which means there is no Add node in qk paths.
Make the necessary changes in onnx_exporter.py to be able to cover
optimizations with test.
### Description
Update python package pipeline to support 3.11
### 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
Adjust various code paths to allow Whisper model to function with
BeamSearch op.
Approach: Add a new kModelType enum value in IGenerationParameters as
so:
#### Old: 0 = GPT2, 1 = T5
#### New: 0 = GPT2, 1 = T5, 2 = Whisper
When the user assigns this attribute value to 2, various shape and type
checks are changed to accommodate Whisper inputs.
### Motivation and Context
BeamSearch is currently designed to function with BERT-based models with
inputs as vocab tokens, and needs changes to function with Whisper
inputs (3-D float values processed from audio data).
---------
Co-authored-by: Peter McAughan <petermca@microsoft.com>
### Description
In merge branch, the run only reads the cache generated in main build.
As a result, each run in merge branch will not upload new cache except
at the first time.
### Motivation and Context
1.Reduce the cache storage.
If there's some big changes, devs should trigger the specific builds
manually in https://dev.azure.com/onnxruntime/onnxruntime/_build. It
still reads own branch cache.
### Description
Add scripts to export Whisper model to ONNX and integrate the ORT
BeamSearch op with the resulting graphs.
Example command to execute this script:
python convert_to_onnx.py -m openai/whisper-large --output whisper -e
---------
Co-authored-by: Peter McAughan <petermca@microsoft.com>
Fix prefast warnings:
(1) Arithmetic overflow: Using operator '*' on a 4 byte value and then
casting the result to a 8 byte value. Cast the value to the wider type
before calling operator '*' to avoid overflow (io.2).
(2) Dereferencing NULL pointer 'key'.
### Description
Rework some external targets to ease building with
`-DFETCHCONTENT_FULLY_DISCONNECTED=ON`
This will allow package managers to more easily provide an onnxruntime
package by reducing the amount of patching needed downstream at each
version.
### Motivation and Context
Availability of onnxruntime in some C++ package managers
https://github.com/microsoft/onnxruntime/issues/7150https://github.com/conan-io/conan-center-index/issues/16699https://github.com/microsoft/vcpkg/issues/20548
My initial intent is to get this in conan but the PR would most likely
be useful (though not tested) to vcpkg as well (and maybe others).
I tried to get only a first batch of not too specific patches (i.e. not
specific to conan).
The first commit reworks `flatbuffers` and just extends what @snnn did
in https://github.com/microsoft/onnxruntime/pull/13991
The second commit reworks `pytorch_cpuinfo`
The third commit reworks `google_nsync`
Temporarily remove Azure build check to unblock PR(s).
We need to investigate the sudden build failure and reenable.
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Ensure that Loop operators run on CPU.
Fix memcpy for Sequence Tensors, so that empty sequences (like when
SequenceEmpty runs on DirectML) can be copied back to CPU.
MSVC and gcc are both not good at optimizing select(), even in trivial
usage outside of ORT.
gcc seems to do better with -ffast-math (not used by ORT) but /fp:fast
does nothing for MSVC
This PR delivers a 33% speedup on the same model (360us -> 270us on
Windows; 205 us -> 153 us on Linux; measured on different systems).
TODO: Examine and fix Elu and other similar activation functions for the
use of `Eigen::select`
Co-authored-by: @fpribeiro
### 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 a tool to convert fused BERT like model to packing mode
### 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. -->
Followed by https://github.com/microsoft/onnxruntime/pull/14881
### 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: Edward Chen <18449977+edgchen1@users.noreply.github.com>
rocm python packaging pipeline failed because manylinux version and
manylinux.patch update.
1. fix duplicate `epel-release` installation issue, ROCm pipeline
install it at the begin of the dockerfile to install rocm libs. remove
duplicate installation on install-runtime-packages.sh.
```
/var/tmp/yum-root-sMRl36/epel-release-latest-7.noarch.rpm: does not update installed package.
Error: Nothing to do
```
2. add python10 to fix error below.
```
+ /opt/python/cp310-cp310/bin/python -m venv /opt/_internal/tools
build_scripts/finalize.sh: line 40: /opt/python/cp310-cp310/bin/python: No such file or directory
```
3. add python10 to rocm pipeline.
pipeline link:
https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=294776&view=results
### Description
Removing fp16 support from apple build
### Motivation and Context
FP16 support on ARM64 only available after armv8.2a, thus the clang
compiler needs a compilation flag `-march=armv8.2-a+fp16`.
Unfortunately, our current universal build does not support hardware
specific compilation flags on cpp source files, as it would cause
trouble when compiling against more than one hardware target. Until we
figure out how to remove this limitation, had to disable fp16 support
for Apple systems.
### Description
<!-- Describe your changes. -->
Add required graph transformer to duplicate DQ nodes to ensure that QDQ
node units have unique DQ nodes. This condition is necessary for QDQ
node unit processing.
### 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 is an existing Python utility that does this:
c7ced7a5e9/tools/python/util/qdq_helpers/qdq_model_utils.py (L77)
This PR implements it as a graph transformer so it is integrated into
ORT and does not require a separate step to update the model. There are
also tests to ensure that its effects are not undone by basic level
graph optimizations.
### Description
When calculating symbolic shape like `mul(get_int_val(values=[1024,
0.5]))`,
the current script calls `get_int_val()` to get values, which values
becomes `[1024, 0]`.
Thus, the result of `mul(values)`->`mul([1024,0])`=0, but the expected
shape size is 512
Fix: for math binary operations like `mul()` and `div()`,
don't convert input shapes into integers if any possible precision loss
happen;
keep the input shape as float, finish the operation, and cast final
result into integer and output the shape.
Test cases are added:
1. mul(1024, 0.5)=>512 (before this fix, the output would be 0, as float
0.5 would be converted to int 0)
2. div(768, 1.5)=>512 (before this fix, the output would be 768, as
float 1.5 would be converted to int 0)
### 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 uses cudaStreamNonBlocking flag when creating cuda stream,
meaning the created stream will run concurrently with default stream, no
implicit synchronization with default stream.
### Motivation and Context
This PR is required for the perf concern
### Description
DNNL EP doesn't support opset18 yet. So, let it skip such tests so that
we could still test the other EPs.
The models mentioned above are ONNX node tests that live in
github.com/onnx/onnx
### Description
<!-- Describe your changes. -->
Add all the ONNX layout sensitive ops from opset 11 on.
Make list in transpose optimizer consistent.
### 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. -->
When we run L1 optimizers after layout transform in a full build it
needs a schema for any layout sensitive ops that get converted to the
internal domain. Previously we did not run L1, so we got away without
having schemas unless the EP used a static kernel for the nhwc version
of the op.
### Description
1. Make 2 cache tasks in one pipeline really works
2. Each building step has its own environment variable CCACHE_DIR
instead of job variables.
3. Extenal Protobuf compilation cache only updates with deps.txt. It
doesn't generate new cache in every commit.
### Motivation and Context
The simple workflow is as below
```
--------build with ccache-------
|
cache
|
{CCACHE_DIR}-----cache stat.
```
```
-------Cache@2------
|
download cache
|
{path}--------upload cache
```
1. {XXX} means environment variable or task input.
2. {CCACHE_DIR} must be consistent with {path}. Ccache produces caches
in {CCACHE_DIR} and Cache@2 download cache into {path} and tar {path}
and upload it.
3. Protobuf changes with deps.txt so that it would reduce the storage
size.
4. Next step, we may split the compilation into 2 steps, one for
external dependencies and another for ORT.
### Description
1. move the cache task definition into template
2. In debug mode, the compiler mtime is different in different machine.
So, change the CCACHE_COMPILERCHECK to content.
### Motivation and Context
1. Accelerate the CoreML pipeline.
Test run:
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=938040&view=logs&j=1ac7588f-a5bd-5ff7-4a8a-a34869d50220
With Cache, the run can be finished in 12 minutes. Without cache, it
takes about 1 hour.
3. Make the cache function easy to use and maintain.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
This PR disables browser test temporarily. The test randomly fails and
we are investigating the issue. Disable the test to unblock others.
### Description
1. Remove Linux jobs for ORT-Extension combined build
2. Add a macOS build job for ORT-Extension combined build
3. Adjust the yaml file so that it can support two different ADO
instances.
### Motivation and Context
To test our code better. And it will enable us to run such tests for
every commit in the main branch. It would be easier for us to figure out
which change caused a build break.
See
[AB#13435](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/13435)
### Description
**Multi-stream** execution support for **CANN EP**.
### Motivation and Context
**CANN EP** is currently **unavailable** due to the introduction of a
new mechanism for multi-stream execution
[#13495](https://github.com/microsoft/onnxruntime/pull/13495), the
deletion of the Fence-based synchronization mechanism, and the failure
to update the relevant logic of **CANN EP** synchronously.
This PR is to fix it.
