### Description
This PR is to update the win-ort-main branch to the tip main branch as
of 2025-01-23.
### PR List
ddf0d377a7 [QNN EP] Add LoggingManager::HasDefaultLogger() to provider
bridge API (#23467)
05fbbdf91f [QNN EP] Make QNN EP a shared library (#23120)
1336566d7f Add custom vcpkg ports (#23456)
2e1173c411 Update the compile flags for vcpkg packages (#23455)
1f628a9858 [Mobile] Add BrowserStack Android MAUI Test (#23383)
009cae0ec8 [js/webgpu] Optimize ConvTranspose (Continue) (#23429)
04a4a694cb Use onnx_protobuf.h to suppress some GCC warnings (#23453)
2e3b62b4b0 Suppress some strict-aliasing related warnings in WebGPU EP
(#23454)
b708f9b1dc Bump ruff from 0.9.1 to 0.9.2 (#23427)
c0afc66b2a [WebNN] Remove workarounds for TFLite backend (#23406)
8a821ff7f9 Bump vite from 6.0.7 to 6.0.11 in
/js/web/test/e2e/exports/testcases/vite-default (#23446)
220c1a203e Make ORT and Dawn use the same protobuf/abseil source code
(#23447)
b7b5792147 Change MacOS-13 to ubuntu on for
android-java-api-aar-test.yml. (#23444)
19d0d2a30f WIP: Dp4MatMulNBits accuracy level 4 matmul for WebGPU EP
(#23365)
95b8effbc4 [QNN EP]: Clean up QNN logging resources if an error occurs
during initialization (#23435)
626134c5b5 Bump clang-format from 19.1.6 to 19.1.7 (#23428)
0cf975301f Fix eigen external deps (#23439)
f9440aedce Moving RN_CI Android Testing to Linux (#23422)
1aa5902ff4 [QNN EP] workaround for QNN validation bug for Tanh with
uint16 quantized output (#23432)
7f5582a0e2 Seperate RN andriod and IOS into 2 separated Stages. (#23400)
73deac2e7f Implement some missing element wise Add/Sub/Mul/Div/Neg
operations for CPU and CUDA EPs (#23090)
949fe42af4 Upgrade Java version from react-native/android to Java 17
(#23066)
0892c23463 Update Qnn SDK default version to 2.30 (#23411)
94c099bcec Fix type cast build error (#23423)
d633e571d1 [WebNN EP] Fix AddInitializersToSkip issues (#23354)
e988ef00e2 [QNN EP] Fix regression for MatMul with two quantized/dynamic
uint16 inputs (#23419)
7538795f6b Update onnxruntime binary size checks ci pipeline's docker
image (#23405)
6c5ea41cad Revert "[QNN EP] Clean up correctly from a partial setup
(#23320)" (#23420)
e866804bbe Enable comprehension simplification in ruff rules (#23414)
0a5f1f392c bugfix: string_view of invalid memory (#23417)
4cc38e0277 fix crash when first input of BatchNormalization is 1-D
(#23387)
033441487f Target py310 and modernize codebase with ruff (#23401)
87341ac010 [QNN EP] Fix segfault when unregistering HTP shared memory
handles (#23402)
### Motivation and Context
This update includes the change to make QNN-EP a shared library.
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Peishen Yan <peishen.yan@intel.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Hector Li <hecli@microsoft.com>
Co-authored-by: Jian Chen <cjian@microsoft.com>
Co-authored-by: Alexis Tsogias <1114095+Zyrin@users.noreply.github.com>
Co-authored-by: junchao-zhao <68935141+junchao-loongson@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: sushraja-msft <44513542+sushraja-msft@users.noreply.github.com>
Co-authored-by: Wanming Lin <wanming.lin@intel.com>
Co-authored-by: Jiajia Qin <jiajiaqin@microsoft.com>
Co-authored-by: Caroline Zhu <wolfivyaura@gmail.com>
Bump ruff version and remove pylint from the linter list. Fix any new
error detected by ruff.
### Motivation and Context
Ruff covers many of the pylint rules. Since pylint is not enabled in
this repo and runs slow, we remove it from the linters
### Description
Disable two PERF* rules in ruff to allow better readability. Rational
commented inline. This change also removes the unused noqa directives
because of the rule change.
### Motivation and Context
Readability
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at
bottom):
* __->__ #16789
Bump ruff to 0.0.278 and fix new lint errors. I added noqa to all
existing RUF012 errors which requires mutable class variables to be
annotated with `ClassVar`, as well as all PERF issues.
Signed-off-by: Justin Chu <justinchu@microsoft.com>
### Description
The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ
as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA
API to cast float/half to float8 if CUDA>=11.8, a custom implementation
if CUDA<11.8.
* It implements, Cast, QuantizeLinear, DequantizeLinear for all types on
CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA.
* It extends the supported types for control flow operator, Shape,
Reshape, Identity, If, Loop, Scan, Reshape
* It implements Equal(19).
* Cast, QuantizeLinear, DequantizeLinear operators now support a
parameter `saturate` only valid for float 8 types. It is true by
default. In that case, any value out of range is converted into the
maximum float 8 value. If false, it is infinite.
* QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA
(and ROCm by extension), scale = 1D tensor with one scale per channel
### Motivation and Context
Supports latest onnx version.
Fixes
[AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395)
---------
Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
`lintrunner` is a linter runner successfully used by pytorch, onnx and
onnx-script. It provides a uniform experience running linters locally
and in CI. It supports all major dev systems: Windows, Linux and MacOs.
The checks are enforced by the `Python format` workflow.
This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors
in Python code. `lintrunner` now runs all required python lints
including `ruff`(replacing `flake8`), `black` and `isort`. Future lints
like `clang-format` can be added.
Most errors are auto-fixed by `ruff` and the fixes should be considered
robust.
Lints that are more complicated to fix are applied `# noqa` for now and
should be fixed in follow up PRs.
### Notable changes
1. This PR **removed some suboptimal patterns**:
- `not xxx in` -> `xxx not in` membership checks
- bare excepts (`except:` -> `except Exception`)
- unused imports
The follow up PR will remove:
- `import *`
- mutable values as default in function definitions (`def func(a=[])`)
- more unused imports
- unused local variables
2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than
flake8 and is more robust. We are using it successfully in onnx and
onnx-script. It also supports auto-fixing many flake8 errors.
3. Removed the legacy flake8 ci flow and updated docs.
4. The added workflow supports SARIF code scanning reports on github,
example snapshot:
5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant
### 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. -->
Unified linting experience in CI and local.
Replacing https://github.com/microsoft/onnxruntime/pull/14306
---------
Signed-off-by: Justin Chu <justinchu@microsoft.com>
Description: Format all python files under onnxruntime with black and isort.
After checking in, we can use .git-blame-ignore-revs to ignore the formatting PR in git blame.
#11315, #11316
Add runtime optimization support to ONNX -> ORT format conversion script.
Replace `--optimization_level`, `--use_nnapi`, and `--use_coreml` with a new `--optimization_style` option.
* schema change
* cc channges
* remove temp debug code
* Adding fbs namespace to session_state_flatbuffers_utils.h
* Add fbs namepsace to all ort format utils
* Change the strided copy to switch on data size not data type.
Move to header so we can reduce on the enabled types.
Setup type reduction for Concat now that it's using this implementation.
* Include ORT format model conversion scripts and infrastructure in ORT python package.
- tweak existing script setup so it can be easily run directly and from the ORT python package
Add config file and readme for Android minimal build package
Update ORT Mobile doco
Disable warning if 'all' optimizations are enabled but NCHWc transformer is excluded (device specific optimizations don't apply in this scenario so the warning is moot).
* Address PR comments
* Updates to some operators to always support int32 and int64 based on testing of Android package build config with a minimal build.
If an operator can be used for shape manipulation (int64) it is frequently used for indices manipulation (int32), so we enable both types for that set of ops.
- e.g. BERT models take indices as input
- Scatter/Gather ops utilize indices
Misc. fix to python bindings to exclude call that fails in a minimal build.
Enable type reduction for Scatter/ScatterElements CPU kernels. Some refactoring to reduce binary size.
Add MLTypeCallDispatcher methods.
Minor cleanup for Pad CPU kernel.
Enable type reduction for Shrink, Sign, SplitToSequence CPU kernels.
Some other type reduction changes including refactoring to specify element types in a single place.
Update the kernel def hashing in ORT format models. The new hashing logic ignores the ordering of type constraint types.
This is a backward compatibility breaking change, but we don't guarantee backward compatibility yet.
* Add support for custom ops library to the ORT model conversion script
Simplify model conversion now that we read ops from the ORT format model.
Enable custom ops in the python bindings if custom ops are turned on in a minimal build.
* Add test of model conversion involving custom ops.
* Add type reduction support to Min, Max and Pow
Update the C++ type reduction infrastructure to allow specifying an opset for the supported types list, as those can change across opset versions.
Minor updates to the type usage tracking script
* Add 'all opsets' macros and constant
* Add ability to generate configuration that includes required types for individual operators, to allow build size reduction based on that.
- Add python bindings for ORT format models
- Add script to update bindings and help info
- Add parsing of ORT format models
- Add ability to enable type reduction to config generation
- Update build.py to only allow operator/type reduction via config
- simpler to require config to be generated first
- can't mix a type aware (ORT format model only) and non-type aware config as that may result in insufficient types being enabled
- Add script to create reduced build config
- Update CIs
