The ONNX Runtime Release Roadmap on our website is not very easy to find
right now, so I'm adding a link here to make it more accessible.
### Description
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### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
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Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
### Description
Condition for [BrowserStack support for open-source
projects](https://www.browserstack.com/open-source)
### Motivation and Context
- Considering using BrowserStack for our end-to-end tests for iOS and
Android
Fixed a small spelling error.
### Description
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Small spelling error fix.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
It is documentation for the product, and it misspells the word
documentation. This reflects on your product and the quality of the
work.
* Simplified version of WebAssembly support to keep most of existing data structures and add cmake using Ninja and emcmake
* Clean up CMakeLists.txt and add an example to create and compute a kernel
* Load a model from bytes and remove graph building steps
* Add all cpu and contrib ops with mlas library
* WebAssembly build with Onnxruntime C/CXX API
* Use protobuf cmakefile directory instead of adding every necessary source file
* Fix invalid output at example
* add missing files
* Change an example to use Teams model and support ort mobile format
* add API for javascript
* fix input releasing in _ort_run()
* update API
* Let onnxruntime cmake build WebAssembly with option '--wasm'
* allow one-step building for wasm
* Make build script working on Linux and MacOS
* Fix broken build from Windows command
* Enable unit test on building WebAssembly
* Resolve comments
* update build flags
* wasm conv improvement from: 1) GemmV; 2) Depthwise direct convolution 3x3; 3) Direct convolution 3x3
* Cleaned mlas unittest.
* use glob
* update comments
* Update baseline due to loss scale fix (#6948)
* fix stream sync issue (#6954)
* Enable type reduction in EyeLike, Mod, random.cc CPU kernels. (#6960)
* Update EyeLike CPU kernel.
* Update Mod CPU kernel.
* Update Multinomial CPU kernel.
* Slight improvement to Pad CPU kernel binary size.
* Update RandomNormal[Like], RandomUniform[Like] CPU kernels.
* Fix warning from setting multiple MSVC warning level options. (#6917)
Fix warning from setting multiple MSVC warning level options. Replace an existing /Wn flag instead of always appending a new one.
* MLAS: quantized GEMM update (#6916)
Various updates to the int8_t GEMMs:
1) Add ARM64 udot kernel to take advantage of dot product instructions available in newer cores. Some models run 4x faster than the stock implementation we used before.
2) Refactor the x64 kernels to share common code for AVX2(u8u8/u8s8/avxvnni) vs AVX512(u8u8/u8s8/avx512vnni) to reduce binary size.
3) Extend kernels to support per-column zero points for matrix B. This is not currently wired to an operator.
* Implement QLinearAveragePool with unit tests. (#6896)
Implement QLinearAveragePool with unit tests.
* Attention fusion detect num_heads and hidden_size automatically (#6920)
* fixed type to experimental session constructor (#6950)
* fixed type to experimental session constructor
Co-authored-by: David Medine <david.medine@brainproducts.com>
* Update onnxruntime_perf_test.exe to accept free dimension overrides (#6962)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* Fix possible fd leak in NNAPI (#6966)
* Release buffers for prepacked tensors (#6820)
Unsolved problems:
1. One test failure was caused by a bug in Cudnn rnn kernels, when they can allocate a buffer and partially initialize it, the garbage data near tail of the buffer caused problem in some of the hardware. To attack this problem in a broader sense, should we add code in our allocators, and during a memory fuzzing test, fill an allocated buffer with garbage before returning to the caller?
2. Prepacking is used more widely than we know. For instance, Cudnn rnn kernels also cache their weights. They mix several weight tensors together into a single buffer, and never touch the original weight tensor anymore. This is the same idea with pre-pack, but they didn't override the virtual function, and they never tried to release those weight tensors, leading to memory waste. It also seems to me that there are some other kernels have similar behavior. Wonder how much memory we can save if we try to cleanup those too.
3. Turning off memory pattern planning does increase memory fragmentation, leading to out of memory error in some training test cases. Perhaps we can revisit the idea of pushing kernels-creation stage earlier, and then during initializer deserialization, we only avoid tracing those that will be prepacked.
* Enable type reduction for Range, ReverseSequence, ScatterND, Split, and Unique CPU kernels. (#6963)
* add CI
* fix test in ci
* fix flags for nsync in wasm build
* add copyright banner
* fix wasm source glob
* add missing exports
* resolve comments
* Perf gain by make packb wide to 4 from 16 on GEMM for WASM.
Remove no need direct conv in previous perf tuning.
* fix buildbreak introduced from latest master merge
* fix buildbreak in mlasi.h
* resolve all comments except MLAS
* rewrite packb related 3 functions for WASM_SCALAR seperately rather than using #ifdef in each.
and other changes according to PR feedback in mlas.
* More complete scalar path in sgemm from Tracy.
* Fix edge case handling in depthwise conv2d kernel 3x3. where:
*) support input W==1 and H==1
*) recalc in accurate pad_right and pad_bottom
*) support hidden pad_right == 2 or pad_bottom == 2 when W == 1 or H==1 and no pad left/top
* Add more test coverage for conv depthwise from Tracy.
Fix one typo according to PR.
* resolve comments
* replace typedef by using
* do not use throw in OrtRun()
* output error message
Co-authored-by: Sunghoon <35605090+hanbitmyths@users.noreply.github.com>
Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Tracy Sharpe <42477615+tracysh@users.noreply.github.com>
Co-authored-by: David Medine <david.eric.medine@gmail.com>
Co-authored-by: David Medine <david.medine@brainproducts.com>
Co-authored-by: Ori Levari <ori.levari@microsoft.com>
Co-authored-by: Ori Levari <orlevari@microsoft.com>
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
Co-authored-by: Chen Fu <chenfucs@gmail.com>
* Fix broken links to EP docs
* Fix another link
* Simplify content to link to docs site
* Update README.md
* Add build pipeline status
* Fix openvino pipeline widget
1. Fix Nuget package build break caused by #6225
2. Delete Dockerfile.centos_gpu. It is not used anywhere.
3. Fix Linux CUDA 10.2 build error caused by glibc upgrade
* Remove nGraph Execution Provider
Pursuant to nGraph deprecation notice: https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md#deprecation-notice
**Deprecation Notice**
| | |
| --- | --- |
| Deprecation Begins | June 1, 2020 |
| Removal Date | December 1, 2020 |
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features
previously available through nGraph have been merged into the OpenVINO™
toolkit. As a result, all the features previously available through
ONNX RT Execution Provider for nGraph have been merged with ONNX RT
Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated
starting June 1, 2020 and will be completely removed on December 1,
2020. Users are recommended to migrate to the ONNX RT Execution Provider
for OpenVINO™ toolkit as the unified solution for all AI inferencing on
Intel® hardware.
* Remove nGraph Licence info from ThirdPartyNotices.txt
* Use simple Test.Run() for tests without EP exclusions
To be consistent with rest of test code.
* Remove nGraph EP functions from Java code
* Fix Windows AI version
* Update text to extend telemetry coverage
Includes all official binaries
* Update text about EP pluggability
* Update CUDA/cuDNN versions
* Add link to reduce operator kernel page
* Update roadmap
* Add preview for migraphx
* Move Rockchip under IoT/Edge
* Update text to include ORT for Mobile doc link
* update java API docs
* fix link
* rearrange
* update platforms, use table
* use javadoc.io
* craigacp tested it in java 14
* update link
* fix broken link
* fix testdata link
* Add amd migraphx execution provider to onnx runtime
* rename MiGraphX to MIGraphX
* add migraphx EP to tests
* support multiple program output
* disable more tests
* backup changes related to program multiple outputs
* remove logging code
* remove unnecessary changes in migraphx_execution_provider.cc
* add migraphx EP to tests
* add input requests of the batchnorm operator
* add to support an onnx operator PRelu
* update migrapx dockerfile and removed one unused line
* chagnes related to support dynamic input shape
* fix build error
* code backup
* code backup
* version that has 106 models run correctly
* code backup
* code backup
* remove unnecessary print info
* code backup
* code backup
* code backup
* code backup
* code backup
* code backup
* changes corresponding to migraphx change
* fix merge conflict
* minor code cleanup
* code cleanup
* remove unnecessary code
* remove unnecessary code
* add to support more constant folding analysis
* more constant folding checking for shape input
* add env var to control whether fp16 is enabled. Modify docker file to use ROCM3.3
* fix function name to avoid build error
* add build and execution instruction for migraphx execution provider
* added more build instructions
* fixed a small format error
* a minor change
* fix review comments
* another minor change
* additional refinement of the documents
* additional changes
* remove unnecessary changes in the dockfile
* additional changes for the dockerfile
* code change backup
* fix errors related to a few unit tests
* fix a build error related to api change
* fix unit test errors by either disabling the test or fix related isssues
* remove unnecessary log info
* sync submodule tvm with master
* remove unnecessary changes
* remove an unnecessary code line
* refine documents for addition example
* Initial update of readme
* Readme updates
* Review of consolidated README (#3930)
* Proposed updates for readme (#3953)
I found some of the information was duplicated within the doc, so attempted to streamline
* Fix links
* More updates
- fix build instructions
- nodejs doc reorganization
- roadmap update
- version fixes
* Update ORT Server build instructions
* More doc cleanup
* fix python dev notes name
* Update nodejs and some links
* sync eigen version back to master
* Minor fixes
* add nodsjs to sample table of content
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* address PR feedback
* address PR feedback
* nodejs build instruction
* Update Java instructions to include gradle
* Roadmap refresh
Reformat some data, fix link, minor rewording
* Clarify Visual C++ runtime req
Co-authored-by: Nat Kershaw (MSFT) <nakersha@microsoft.com>
Co-authored-by: Prasanth Pulavarthi <prasantp@microsoft.com>
Co-authored-by: manashgoswami <magoswam@microsoft.com>
* api goverannce draft
* Update CONTRIBUTING.md
updated for ABI proposals
* Update CONTRIBUTING.md
* Update CONTRIBUTING.md
* Incomplete, a draft iteartion of 2 more changes - api docs and high levle design
* pushing to see how the picture size works on screen.
* added 2 charts on api choice and distribution choice
* details on contract checking
* lint cleanup and links
* PR feedback.
* fixed markdown and lists
* more markdown and lists
* fixed broken links
* PR feedback
* commas
* PR comments from nick
* PR feedback
* fixed build section
Co-authored-by: Nick Geisler <36938193+ngeisler11@users.noreply.github.com>
* Spacing fix for code block
* Update instructions
Include java, acl, and nn api instructions on build page
* Update build instructions to link to build.md
* typo
* Update build instructions to link to build.md
* Include other minor build.md page updates
* Update CUDA version
* Fix dockerfile links
This change adds a new execution provider powered by [DirectML](https://aka.ms/DirectML).
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning on Windows. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers.
The DirectML execution provider is capable of greatly improving evaluation time of models using commodity GPU hardware, without sacrificing broad hardware support or requiring vendor-specific extensions to be installed.
**Note** that the DML EP code was moved verbatim from the existing WindowsAI project, which is why it doesn't yet conform to the onnxruntime coding style. This is something that can be fixed later; we would like to keep formatting/whitespace changes to a minimum for the time being to make it easier to port fixes from WindowsAI to ORT during this transition.
Summary of changes:
* Initial commit of DML EP files under onnxruntime/core/providers/dml
* Add cmake entries for building the DML EP and for pulling down the DirectML redist using nuget
* Add a submodule dependency on the Windows Implementation Library (WIL)
* Add docs under docs/execution_providers/DirectML-ExecutionProvider.md
* Add support for DML EP to provider tests and perf tests
* Add support for DML EP to fns_candy_style_transfer sample
* Add entries to the C ABI for instantiating the DML EP
