### Description
Minor changes to resolve some warnings in ORT
### Motivation and Context
Binskim for WindowsAI (which consumes ORT) treats warnings as errors,
and has hit these warnings.
As a security requirement, warnings like "signed/unsigned mismatch" must
be resolved.
### Description
It might be easier if we just directly include the original gsl headers.
"core/common/gsl.h" is an indirection that doesn't provide extra help.
### Description
<!-- Describe your changes. -->
See #19921 Just to address one comment:
https://github.com/microsoft/onnxruntime/pull/19921#discussion_r1543398640
since this is an external branch. need to open another pull request for
this.
### 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: Sai Kishan Pampana <sai.kishan.pampana@intel.com>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Jian Chen <cjian@microsoft.com>
### Description
1. Replace some old file system calls to use C++17 std::filesystem APIs.
2. Remove tensorflow_C_PACKAGE_PATH cmake option, which was only used in
onnxruntime_perf_test and the code is out of maintain.
3. Excludes onnx_test_runner and onnxruntime_perf_test from iOS build
because C++17 filesystem library is not available there
Disable __cpuid check on arm64 builds as intrinsic is not available
Motivation
Breaking the arm64 build.
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
### Description
Limit SoC core detection via 2 level cache core logic to Intel and
Hybrid processors.
### Motivation and Context
The following code was added to add support for a new class of CPU cores
present in Intel’s next generation Intel Core Ultra mobile processors.
This code is essential to avoid placing threads on low performing SoC
cores that don’t have L3 cache. SoC cores are meant to specialize in
system bringup and help improve responsiveness and power usage, in other
words they are not meant to run compute heavy AI workloads. In order to
avoid broad exposure of this logic, it is currently designed to be
restricted to Intel platforms that have hybrid enabled.
---------
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
### Description
1. If the model should be skipped, don't load it.
2. print loaded tests and skipped tests
3. add more same filters as of the onnxruntime_test_all.
### 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. -->
Enable cpp20 builds for DML EP and WinML API
1) Missing typename for templated types
2) unmove helper for inline references to rvalue temporaries
This is okay since per the standard a temporary bound to a reference
parameter in a function call exists until the end of the full expression
containing that function call: if the function returns a reference,
which outlives the full expression, it becomes a dangling reference.
3) static now not needed for template specializations
---------
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
### Description
Add support for specifying a custom logging function per session.
Bindings for other languages will be added after this PR is merged.
### Motivation and Context
Users want a way to override the logging provided by the environment.
### Description
<!-- Describe your changes. -->
Make status.h independent from gsl.
### 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. -->
In the coming new feature external EP API (see the prototype
https://github.com/microsoft/onnxruntime/pull/16718), we need to expose
stream in the public header, however, stream is dependent on status.h
which is dependent on gsl. We are seeking a way to decouple stream from
gsl.
From Changming's comment offline, prefast is disabled so all
GSL_SUPPRESS are not taking any effect now. He will handle the warnings
when enable prefast in the future
On Windows, clang-format has a bug when AlignTrailingComments.Kind is
set to `Leave`
(https://clang.llvm.org/docs/ClangFormatStyleOptions.html#aligntrailingcomments),
where it will keep adding indentation to comments after each formatting
runs.
This PR changes to always align comments so we do not hit the bug.
As a consequence of the options change we need to reformat some of the
files. Note that this option is aligned with the rest of the repository.
This addresses a DML performance regression from the following PR
resulting in allocations not being rounded and pooled in the DML
execution provider.
https://github.com/microsoft/onnxruntime/pull/15833
This also fixes a pre-existing limitation that allocations during
session initialization (primarily large weights and persistent
resources) only bypassed rounding and pooling while using the Winml API.
The allocator now also respects a caller's rounding mode parameter when
provided.
winml/ was previously excluded from lintrunner config. This change
includes the directory and adds the clang-format config file specific to
winml/ that fits existing style.
---------
Signed-off-by: Justin Chu <justinchu@microsoft.com>
### Description
clean unused parameter in ORT_UNUSED_PARAMETER
### Motivation and Context
clean unused parameters in ORT_UNUSED_PARAMETER which are introduced
from #15833
### Description
Remove AllocatorManager class
### Motivation and Context
After the refactor PR #15833 is in, AllocatorManager class is not
referenced anymore.
### Description
This PR is to refactor ExecutionProvider API for memory management,
which is to move allocators from EP level to SessionState level and
indexed by OrtDevice
### 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 is to refactor ExecutionProvider API for memory management,
which is to move allocators from EP level to SessionState level and
indexed by OrtDevice. By this change, EP level will shift the burden of
maintaining allocators, which will be user friendly for EP developers
---------
Co-authored-by: Lei Cao <leca@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Disable a test with random failure in Windows GPU CI Pipeline like the
following:
```
11: [ OK ] BatchTest/BatchTest.BatchSupport/163 (0 ms)
11: [ RUN ] BatchTest/BatchTest.BatchSupport/164
11: D:\a\_work\1\s\winml\test\image\imagetests.cpp(186): error: Expected: m_model_binding.Bind(output_data_binding_name, output_video_frames) doesn't throw an exception.
11: Actual: it throws.
11: D:\a\_work\1\s\winml\test\image\imagetests.cpp(211): error: Expected: m_result = m_session.Evaluate(m_model_binding, L"") doesn't throw an exception.
11: Actual: it throws.
11: total errors is 0/2073600, errors rate is 0total errors is 0/2073600, errors rate is 0total errors is 0/2073600, errors rate is 0[ FAILED ] BatchTest/BatchTest.BatchSupport/164, where GetParam() = ((L"fns-candy_Bgr8_Batch3.onnx", 0, { L"1080.jpg", L"fish_720_Gray.png", L"fish_720.png" }, 3, false), 0, 1, 1, 1, 4-byte object <02-00 00-00>) (3203 ms)
```
Since https://github.com/microsoft/onnxruntime/pull/15468 merged to
main, about 10~15% build job failed in the test.
### Description
1. Disable XNNPack EP's tests in Windows CI pipeline
The EP code has a known problem(memory alignment), but the problem does
not impact the usages that we ship the code to. Now we only use XNNPack
EP in mobile apps and web usages. We have already pipelines to cover
these usages. We need to prioritize fixing the bugs found in these
pipelines, and there no resource to put on this Windows one. We can
re-enable the tests once we reached an agreement on how to fix the
memory alignment bug.
2. Delete anybuild.yml which was for an already deleted pipeline.
3. Move Windows CPU pipelines to AMD CPU machine pools which are
cheaper.
4. Disable some qdq/int8 model tests that will fail if the CPU doesn't
have Intel AVX512 8-bit instructions.
This change moves the DML CI pipeline to the A10 machines and fixes or
disables tests that were failing from this change.
- Max error rate threshold was increased for Image Tests
- Some failing batch tests were disabled
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
### Description
Implement Optional Type metadata support in the library.
Implement optional support in C# API along with metadata.
Implement Sequence, Map, Optional test data support
and test execution.
Prune tests and provide more details for failing tests in C# code.
Note, this PR does not enable running onnx test models in C++.
### Motivation and Context
Opset18 optional type support.
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.
### Description
Remove the parameter device_id out of ExecutionProvider::GetAllocator()
function
### Motivation and Context
The parameter device_id is not necessary. We can fully rely on the
second parameter OrtMemType mem_type to determine the device_id when
getting allocator from executionProvider.
### Description
The existing CUDA profiler is neither session-aware, nor thread-safe.
This PR ensures both.
### Motivation and Context
[PR 13549](https://github.com/microsoft/onnxruntime/pull/13549) brought
thread-safety and session-awareness to the ROCm profiler. This PR brings
the same goodness to the CUDA profiler as well.
Sample outputs of a profiling run from the StableDiffusion model (this
model was chosen because it requires orchestration of multiple sessions,
and verifies that the profilers are now indeed session-aware) on both
CUDA and ROCm EPs are attached, along with a script that checks that the
trace files generated by the profile are well-formed.
Update 11/29: Updated the profile outputs. The older profile outputs
exhibited an issue where some timestamps were wildly out of range,
leading to problems visualizing the traces. The bug has been fixed and
the profile outputs have been updated, along with an update to the check
script to ensure that timestamps are monotonically increasing.
[sd_profile_outputs_cuda.tar.gz](https://github.com/microsoft/onnxruntime/files/10118088/sd_profile_outputs_cuda.tar.gz)
[sd_profile_outputs_rocm.tar.gz](https://github.com/microsoft/onnxruntime/files/10118089/sd_profile_outputs_rocm.tar.gz)
[check_profile_output_well_formedness.zip](https://github.com/microsoft/onnxruntime/files/10118090/check_profile_output_well_formedness.zip)
Co-authored-by: Abhishek Udupa <abhishek.udupa@microsoft.com>
### Description
- Use same data type as input for mask_index tensor which is used as DML
GEMM API's C parameter.
- Remove gsl header include as it is already gets included transitively.
### Motivation and Context
- Why is this change required? What problem does it solve?
Bug found in internal conformance testing.
- If it fixes an open issue, please link to the issue here.
N/A
### Description
1. update model name structure in model_tests.cpp with source name. To
avoid
`Condition test_param_names.count(param_name) == 0 failed. Duplicate
parameterized test name 'BERT_Squad_opset10_CPU'`
2. skip some failed models https://github.com/onnx/models/issues/568
### 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. -->
# Motivation
Currently, ORT minimal builds use kernel def hashes to map from nodes to
kernels to execute when loading the model. As the kernel def hashes must
be known ahead of time, this works for statically registered kernels.
This works well for the CPU EP.
For this approach to work, the kernel def hashes must also be known at
ORT format model conversion time, which means the EP with statically
registered kernels must also be enabled then. This is not an issue for
the always-available CPU EP. However, we do not want to require that any
EP which statically registers kernels is always available too.
Consequently, we explore another approach to match nodes to kernels that
does not rely on kernel def hashes. An added benefit of this is the
possibility of moving away from kernel def hashes completely, which
would eliminate the maintenance burden of keeping the hashes stable.
# Approach
In a full build, ORT uses some information from the ONNX op schema to
match a node to a kernel. We want to avoid including the ONNX op schema
in a minimal build to reduce binary size. Essentially, we take the
necessary information from the ONNX op schema and make it available in a
minimal build.
We decouple the ONNX op schema from the kernel matching logic. The
kernel matching logic instead relies on per-op information which can
either be obtained from the ONNX op schema or another source.
This per-op information must be available in a minimal build when there
are no ONNX op schemas. We put it in the ORT format model.
Existing uses of kernel def hashes to look up kernels are replaced
with the updated kernel matching logic. We no longer store
kernel def hashes in the ORT format model’s session state and runtime
optimization representations. We no longer keep the logic to
generate and ensure stability of kernel def hashes.
Jake Mathern
Justin Chu
mindest
Changming Sun
Patrice Vignola
Rachel Guo
Sheil Kumar
Yi Zhang
Pranav Sharma
cao lei
Jeff Bloomfield
Numfor Tiapo
Tianlei Wu
Dmitri Smirnov
RandySheriffH
Abhishek Udupa
Sumit Agarwal
Brian Martin
Edward Chen