* Enabling save/Load blob feature for OpenVINO-EP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes to enhance save/load feature
->This feature applies only for MYRIAD device target
->cleaned up the code and added error checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabled the feature only for MyriadX and only for Linux
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed compilation issues on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes to fix const subgraph issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed issues on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes for the feature
-> Removed default location dir dump using cmake
-> Enabled saving blob dumps at the executable path
by default
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Made save/load dump path configurable
-> The save/load blob dump path is now also made configurable
using a c/python Api's.
-> Introduced a flag named blob_dump_path
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor fixes added
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed python API issues
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Using GetEnvironmentVar to get the path
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed python runtime option issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes import network issue on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* 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>
With this change, differentiating CUDA EP and ROCm EP is not needed in training script when mem_limit option needs to be set.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Enable type reduction for Scatter/ScatterElements CPU kernels. Some refactoring to reduce binary size.
Add MLTypeCallDispatcher methods.
Minor cleanup for Pad CPU kernel.
* Allow specific optimizers to be disabled.
- replace unused ability to specify just the optimizers to run
- never used so not needed
Allow the disabled list to be specified via the python bindings
- expected usage is internal, so using kwargs for that so as not to pollute the documentation with stuff no user is likely to need
Update the ORT format model conversion script to disable NCHWc transformer when level is 'all'
- currently there aren't any known use cases where we'd want the NCHWc transformations to run as they create a device specific model and aren't used on ARM
- the ORT format model is not expected to be generated on the target device (e.g. generate on Windows/Linux/macOS to deploy to Android/iOS so there's a good chance we'd generate a useless/invalid model
- default to 'all' as ARM and MLAS prefer NHWC and the NHWC transformer runs at that level
* Add matching changes to optimizer generation in training code
Changes include:
* Revert Event Pool changes
* Add copyright and revert unrelated changes
* Add DLPack as submodule and remove to_dlpack and from_dlpack from public API
* Update golden numbers for DHP Parallel tests
* Update ORTTrainer unit test numbers
* Rollback to DLPack v0.3
* Disable flaky test
* Update third party notices and CG manifest file
* Minor refactoring of ORTValue API
Add functionality to the Graph class to be dumped to protobuf using an external binary file for the float initializers.
This change is meant to avoid hitting the 2GB protobuf limit when dumping large graphs.
This limit was particularly easy to exceed when dumping graphs after auto-diff.
The use of the external file is limited to initializers larger than a user-specified threshold.
This gives the possibility to users to include in the onnx file shape constants used by Reshape and Transpose used by Shape Inference.
* 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.
* add config allow_spinning
* add config allow_spinning
* set true as default
* split configures for inter and intra ops
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
In the previous shared providers there aren't many OpKernel classes, and the existing Provider_OpKernel wrapper was fine. With the opposibility of making Cuda a shared provider, having this need to be changed per OpKernel adds a lot of complexity.
It was fairly straightforward to make OpKernel work with shared providers with minimal changes.
In this change, the ONNX_OPERATOR_* macros can also be shared with the shared providers.
* model building
* fix build
* winml adapter model building api
* model building
* make build
* make build again
* add model building with audio op
* inplace and inorder fft
* add ifft
* works!
* cleanup
* add comments
* switch to iterative rather than recursive and use parallelization
* batched parallelization
* fft->dft
* cleanup
* window functions
* add melweightmatrix op
* updates to make spectrogram test work
* push latest
* add onesided
* cleanup
* Clean up building apis and fix mel
* cleanup
* cleanup
* naive stft
* fix test output
* middle c complete
* 3 tones
* cleanup
* signal def new line
* Add save functionality
* Perf improvements, 10x improvement
* cleanup
* use bitreverse lookup table for performance
* implement constant initializers for tensors
* small changes
* add matmul tests
* merge issues
* support add attribute
* add tests for double data type windowfunctions and minor cleanup
* stft onesided/and not tests
* cleanup
* cleanup
* clean up
* cleanup
* remove threading attribute
* forward declare orttypeinfo
* warnings
* fwd declare
* fix warnings
* 1 more warning
* remove saving to e drive...
* cleanup and fix stft test
* add opset picker
* small additions
* add onnxruntime tests
* add signed/unsigned
* fix warning
* fix warning
* finish onnxruntime tests
* make windows namespace build succeed
* add experimental flag
* add experimental api into nuget package
* add experimental api build flag and add to windows ai nuget package
* turn experimental for tests
* add minimum opset version to new experimental domain
* api cleanup
* disable ms experimental ops test when --ms_experimental is not enabled
* add macro behind flag
* remove unused x
* pr feedback
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* ortmodule v0.2
* use pt module for eval
* get user outputs in yield op
* pass output grads to yield output without copy
* Disable mem_pattern for ORTModule
* Avoid allocating output buffer for Yield op
* Change to WaitAndReset to avoid overriding signal
* remove unnecessory signal/wait at the end of bg thread
* Return Session.Run result as a std::future
* export model with torch.no_grad()
* Handle bg thread's early return in Forward call
* Removed duplicated Yield kernel
* Silence "CUDA kernel missing log"
* Add missing transforms, clear iobinding (#6532)
* revert ortmodule.py to a working state first
* Apply ortmodule.py change from dev branch
* Rename to YieldOp
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: ashbhandare <ash.bhandare@gmail.com>
Co-authored-by: Sherlock <baihan.huang@gmail.com>
Move ORT_ENFORCE()'s within MLTypeCallDispatcher to helper class functions to reduce the size of function names in ORT_ENFORCE().
ORT_ENFORCE() captures the containing function's name in the error message. For some usages of MLTypeCallDispatcher (i.e., with numerous types or long type names), the function name is quite long and can contribute significantly to the binary size. Usage in the Cast CPU kernel is a notable example.
This change moves the ORT_ENFORCE() checks from a class template member function template with variable length name to a helper function with a fixed length name.
* Add infrastructure so that a kernel definition has the full list of supported types and a list of types enabled in this build. We need to use the full list when calculating the kernel hash so that the hash value in an ORT format model is stable across builds with and without type reduction enabled.
Remove condition from ORT_RETURN_IF[_NOT] macro output as repeating the condition doesn't add much value compared to the explicit error message, and the error message includes the file and line anyway so it's easy enough to find the condition if needed.
Update the few places where the macros were used without an explicit error message to provide an explicit error message.
Saves 12.5KB in a minimal MinSizeRel build with all DNN ops, 16KB in full release build.
* Support to allow user to specify compute stream per session
Create computation cuda stream explicitly rather than use default legacy stream or per-thread default stream.
remove some redudant cudaStreamSynchronize
fix gpt2 model test failures
don't use default stream in nccl either.
add stream schronization in OnRunEnd()
using cub::DeviceScan::InclusiveSum which can be called with stream specified.
fix topK failure due to latest rebase
fix tensorrt
support user specified stream
add user_stream support in tensorrt EP
use same stream for both tensort and CUDA EP.
fix ScatterND
specify stream for adasum and p2p kernels.
fix loop
fix CApiTest.custom_op_handler
fix CApiTest.varied_input_custom_op_handler
change for cudaMemcpyFromSymbol
improve provider options for user specified compute stream
* add changes for ROCM EP
* fix GatherGrad UT for ROCM EP
* clean code and fix NonMaxSuppression
* use default stream for ROCM now
* fix CApiTest.custom_op_handler:OrtFormatCustomOpTests.ConvertOnnxModelToOrt
* fix tensorrt ut: CApiTest.io_binding_cuda
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
