### Description
Fix onnxruntime_mlas build failure with cmake 3.26. Updated CMAKE
generator expression to make sure certain complier flags only apply for
C/CXX compiler.
### Motivation and Context
CMake changed the behavior of ASM_MASM in version 3.26. See
https://gitlab.kitware.com/cmake/cmake/-/issues/24639.
This also fixed the issue of #15101
### Description
Update mimalloc dependency.
### Motivation and Context
The latest release contains important fixes including memory leaks and
used by customers.
### 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.
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)
### 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`
### 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
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
<!-- 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
- Adds support for newer opset of Reduction operators (ReduceSum,
ReduceMax, ReduceMin, ReduceMean, ReduceProd) with axes as an
initializer input.
- Adds tests for HTP and CPU backends.
### Motivation and Context
Newer opset versions changed the `axes` attribute into an optional
input. This PR adds support for these newer reduction operators as long
as the axes input is defined as an initializer. The goal is to enable more models on QNN.
### Description
<!-- Describe your changes. -->
1. upgrade cutlass to 3.0 that containing attn_bias support.
2. extend Attention/MHA to use memory efficient attention when
rel_pos_bias with [1, num_head, s, s*] and 1d mask with [2 * batch_size
+ 1] are present.
new mask format introduction:
MASK_1D_KEY_SEQ_LEN_START,
[3 * batch_size + 2] with [key_len[0], ..., key_len[batch_size - 1],
query_start[0], ..., query_start[batch_size - 1], query_end[batch_size -
1], key_start[0], ..., key_start[batch_size - 1], key_end[batch_size -
1]]
e.g
2D mask with [[1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0]] converts to this
1D mask is [3, 5, 0, 6, 12, 0, 6, 12]
### 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. -->
It potentially benefits tnlrv6 and t5(encoder)
---------
Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Statistics tool for ORTModule convergence parity
As ORTModule get more and more validated, it is pretty fast to
intergrade PyTorch based model with ORT.
The same time, we need make sure once there is convergence issue, we
don't spend months of time to investigate. As part of this efforts, this
PR is introducing a tool to dump activation statistics without much
involvement from users. The dumping results contains only some statistic
numbers plus sampled data, which is not big, compared with dumping all
the tensors, it is much faster and space efficient.
For us to use it, two single lines are needed before wrapping ORTModule.
For baseline run, need also apply the same trick.
```
+ from onnxruntime.training.utils.hooks import SubscriberManager, StatisticsSubscriber
+ SubscriberManager.subscribe(model, [StatisticsSubscriber("pt_out", override_output_dir=True)])
```
Once you run the steps, following command can be used to merge result
into per-step-summary respectively for ORT and baseline runs.
```bash
python -m onnxruntime.training.utils.hooks.merge_activation_summary --pt_dir pt_out --ort_dir ort_out --output_dir /tmp/output
```
Docs is added here as part of this PR [convergence investigation
notes](https://github.com/microsoft/onnxruntime/blob/pengwa/conv_tool/docs/ORTModule_Convergence_Notes.md)
Based on the generated merged files, we can compare them with tools.
### Design and Implementation
This PR introduced a common mechanism registering custom logic for
nn.Module's post forward hooks. And statistics for activation
(StatisticsSubscriber) is one of the implementations. If there is other
needs, we can define another XXSubscriber to do the customized things.
### Description
This PR includes the following changes:
- upgrade js dependencies
- enable STRICT mode for web assembly build.
- corresponding fix for cmake-js upgrade
- corresponsing fix for linter upgrade
- upgrade default typescript compile option of:
- `moduleResolution`: from `node` to `node16`
- `target`: from `es2017` to `es2020`
- fix ESM module import in commonJS source file
## change explanation
### changes to onnxruntime_webassembly.cmake
`-s WASM=1` and `-s LLD_REPORT_UNDEFINED` in latest version is
by-default and deprecated.
### changes to onnxruntime_node.cmake
The npm package `cmake-js` updated its way to find file `node.lib`.
previously it downloads this file from Node.js public release channel,
and now it generates it from a definition file.
The node.js release channel does not contain a windows/arm64 version, so
previously cmake-js will fail to download `node.lib` for that platform.
this is why we made special handling to download the unofficial binary
to build. now this is no longer needed so we removed that from the cmake
file.
### changes to tsconfig.json
`node16` module resolution supports async import and `es2020` as target
supports top level await.
### Description
<!-- Describe your changes. -->
AMX isn't supportted until assembler 2.40 even though the GCC frontend
supports it.
### 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
Convolution for fp16 datatype. Use NHWC for computation. For NCHW input,
it rearranges the input tensor to NHWC format before computing the
result.
Support two optional fusion:
1. Activation
2. Add (not yet implemented)
### Motivation and Context
Accelerating fp16 inference
### Description
<!-- Describe your changes. -->
We are introducing the FasterTransfomer model-level integration using
ORT [custom op runtime
wrapper](https://github.com/microsoft/onnxruntime/pull/13427).
In order to make the FT wrapper/integration work, two things need to be
done:
- New API `KernelInfoGetConstantInput_tensor`. (Done in this PR)
During custom op kernel initialization, it needs to get the model
weights (saved as node's constant inputs) ready for FT's weights
instantiation. What's why we need to add this new API to make kernel
info capable of getting constant inputs.
- Custom op and custom op kernel to wrap FT model. (Will provide in
onnxruntime extensions or inference examples)
During custom op kernel initialization, it can fetch attributes from
kernel info to determine which kind of FT model instance create. During
custom op kernel compute/inference, it can get input/output from kernel
context and then assign input/output buffers for model instance to run.
### Description
Add logging APIs for custom ops.
This PR introduces a `OrtLogger` type, which can be retrieved from a
`OrtKernelInfo` or `OrtKernelContext`. The kernel info's logger is the session logger stored
in the execution provider. The kernel context's logger is a run logger.
### Motivation and Context
Allows custom ops to log information in a manner consistent with
built-in ops.
Example usage in custom op:
```C++
struct MyCustomKernel {
MyCustomKernel(const OrtApi& api, const OrtKernelInfo* info) {
Ort::ConstKernelInfo kinfo(info);
this->logger_ = kinfo.GetLogger();
// ...
ORT_CXX_LOGF_NOEXCEPT(this->logger_, OrtLoggingLevel::ORT_LOGGING_LEVEL_ERROR, "Error: %s", err_msg);
}
void Compute(OrtKernelContext* context) {
ORT_CXX_LOG(this->logger_, OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE, "Calling compute...");
// ...
}
// ...
private:
Ort::Logger logger_;
};
```
### Description
<!-- Describe your changes. -->
This fix macos packaging build on universal2 arch.
### 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. -->
Add BiasSplitGelu/BiasAdd/GroupNorm/NhwcConv operator for ROCm EP.
1. BiasSplitGelu and BiasAdd operators can be automatically hipified
from CUDA EP.
2. GroupNorm was hipified from CUDA EP and modified to build.
3. NhwcConv is similar to NhwcConv in CUDA EP, But the MIOpen API and
cuDnn API are different. `miopenConvolutionForwardbias` and
`miopenOpTensor` of MIOpen doesn't support NHWC layout now, use
BinaryElementwise to replace miopenConvolutionForwardbias(NHWC layout).
### Description
QNN EP:
- Adds the
[InstanceNormalization](https://onnx.ai/onnx/operators/onnx__InstanceNormalization.html)
operator to QNN EP.
- Fixes graph composition bug when Transpose node is the last node in a
graph.
- Adds check for input shape when GetCapability is called (before and
after layout transformation)
- Should add similar checks for other layout sensitive ops (conv, pool,
...) in a separate PR
- Adds initial QNN op tests for QDQ conv and QDQ InstanceNormalization
- Should add tests for other ops in a separate PR
Optimizer:
- Makes InstanceNormalization a layout sensitive operator.
- Adds a custom QDQ group selector for InstanceNormalization.
Quantization tool:
- Adds QDQ support for InstanceNormalization operator.
- Adds python unit test for InstanceNormalization quantization.
### Motivation and Context
Needed to support stable diffusion models with QNN.
---------
Co-authored-by: Hector Li <hecli@microsoft.com>
### Description
This will enable a user to use a TensorRT timing cache based on #10297
to accelerate build times on a device with the same compute capability.
This will work across models as it simply store kernel runtimes for
specific configurations. Those files are usually very small (only a few
MB) which makes them very easy to ship with an application to accelerate
the build time on the user end.
### Motivation and Context
Especially for workstation use cases TRT build times can be a roadblock.
With a few model from ONNX model zoo i evaluated speedups when a timing
cache is present.
`./build/onnxruntime_perf_test -e tensorrt -I -t 5 -i
"trt_timing_cache_enable|true" <onnx_path>`
|Model | no Cache | with Cache|
| ------------- | ------------- | ------------- |
|efficientnet-lite4-11 | 34.6 s | 7.7 s|
|yolov4 | 108.62 s | 9.4 s|
To capture this is had to modify the onnxruntime_perf_test. The time is
sometimes not captured within "Session creation time cost:" which is why
i introduced "First inference time cost:".
---------
Co-authored-by: Chi Lo <Chi.Lo@microsoft.com>
- Update Gradle version used in most places from 6.8.3 to 8.0.1. Update Android Gradle Plugin version where applicable.
Not updated in this change: React Native Android projects (under `js/react_native/`). That can be done later along with updating the React Native projects.
- Add Gradle wrapper in `java/` to make it easier to consistently use a specific Gradle version.
### Description
<!-- Describe your changes. -->
Consume ONNX 1.13.1 in ONNX Runtime. (ONNX 1.13.0 to ONNX 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. -->
ONNX 1.13.1 patch was just released yesterday. This PR is making ORT's
ONNX submodule consistent with the latest released ONNX. Not sure
whether this PR is really needed, but let me make it ready. Previous PR
for testing ONNX 1.13.1rc2 :
https://github.com/microsoft/onnxruntime/pull/14634.
Fixed
[AB#13174](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/13174)
.
### Description
<!-- Describe your changes. -->
1. add a build flag for rocblas tuning feature.
2. fix a build bug when enable rocblas tuning.
### 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. -->
The rocblas tunning feature has no build flag to control, only using a
MACRO flag.
So I add an build flag, and fix a code bug when enable rocblas tunning.
### Description
allow onnxruntime_test_all to run in browser for WebAssembly build (use
flag `--wasm_run_tests_in_browser`).
To output the logs from stdout correctly, this test needs to be build
with `--enable_wasm_threads`.
### Description
* Support flag 'optimizedModelFilePath' in session options.
In Node.js, the model will be saved into filesystem just like its
behaviour on native platforms.
In browser, the new model is not saved to filesystem. the file path is
ignored. Instead, a new pop-up window will be launched in browser and
user can 'save' the file as onnx model.
* Add corresponding commandline args for the following session option
flags:
- optimizedModelFilePath
- graphOptimizationLevel
### Description
<!-- Describe your changes. -->
Merging extensions from Git submodule to cmake FetchContent
### 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: Changming Sun <chasun@microsoft.com>
Co-authored-by: Jian Chen <jchen351@MacBook-Pro.local>
### Description
Update oneDNN version from 2.7 to 3.0
### 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. -->
1. Add Softmax warpwise_forward into SoftmaxTunableOp.
2. Set Softmax op use tunableOp as optional and use original
implementation by default.
3. There are some other operators use `dispatch_warpwise_softmax_forward
/dispatch_warpwise_softmax_forward/ SoftMaxComputeHelper ` directly. But
they only have files under cuda directory, adding `RocmTuningContext `
for these files requires copying and modifying hipified files. Now only
set RocmTuningContext as nullptr by default and not hipified other
operators.
Related PR: https://github.com/microsoft/onnxruntime/pull/14541
---------
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description
FP16 GEMM, including hardware agnostic driver code, a slow C++ kernel,
and ARM64 NEON kernel.
### Motivation and Context
First step in creating native support of fp16 model inferencing on ARM64
and AMD64 platforms.
---------
Co-authored-by: Chen Fu <fuchen@microsoft.com>
