* Bump onnx to latest
Update onnx.in.proto with changes for SparseTensor.
* add temp skip tests
* remove passed tests from skip list
* skip more tests for new ops in opset 11
* skip crashing tests
* update handling of new attribute types sparse tensor and sparse tensors
* advance onnx commit and remove skip cpu_flaky_tests
* temporarily skip yolo3 model test due to resize opset10 shape inference regression
* update proto for onnxruntime server
* advance onnx commit further
C/C++ Opage APIs
Add new virtual interfaces for NonTensorType
Implement entry points.
Add shared header for the data container.
Add export symbols.
Add serialization/deserialization.
Implement model with Opaque types.
Rework opqaue_api_test as a standalone executable.
Refactor the SGEMM kernels to resynchronize the code between Windows/Linux and remove unneeded binary bloat from a different zero/add mode kernel. Another goal is to get to a cleaner state for then doing a DGEMM kernel.
Enable Nuphar EP docker build
Revert back to LLVM 6.0.1
Reinstate disabled Softmax tests caused by LLVM 8.0.1
Reinstate Nuphar Python test due to stale sympy version
Increase build timeout of Linux CI
* Implement Nuphar execution provider
Nuphar execution provider is a TVM-based compilation provider. It has shown great speedups for RNN models using Scan.
This PR is mainly for a preview of the shared codegen library for other TVM-based providers.
* Fix submodules
* Fix TVM submodule
* Update Nuphar to latest and resolve confliction
* Remove stale files caused by merge -X theirs
* Revert heap buffer change to not introduce onnxruntime_framework into onnxruntime_perf_test
* Fix bad merge
* Merge from Nuphar
* Fix warning treated as error, revert some unnecessary changes
* Revert some more test changes
* Some more test revert or comments to make review easier
New tests could be added later
* One more revert of unnecessary changes
* More change revert. Test could be added back later.
* update MKLML which has bugfix for thread hang. move PATCH_COMMAND outside BUILD_FOR_NATIVE_MACHINE check.
* MKLML_VERSION 2020.0.20190813 is for windows only.
1.Let mlas use session thread pool
2.Remove onnxruntime_USE_MLAS cmake option
3. Remove the win32 thread pool code inside mlas
mlas will:
1.use ort thread pool if it get passed in
2.use openmp if the threadpool parameter is nullptr
3.run single threaded if the threadpool parameter is nullptr and openmp is disabled.
Added Sample Featurizer and Infrastructure
Make featurizers and unit tests compile and run with GTest.
Create definitions for the first featurizer kernel.
Add new operator domain.
Create datetime_transformer kernel and build.
Move OPAQUE types definitions for featurizers kerneles out to a separate cc.
Register them with the type system.
Provide unit tests for new AutoML DateTimeTransformer kernel.
Make necessary adjustments to the test infrastructure to make it run
with new types.
* Mention OrtCreateSessionFromArray in C API doc
* Fix perf test executable due to removal of certain C APIs
* fix linux build
* Avoid duplication
* Fix mem leak
* Update nGraph to 0.21 and adjust the EP
* Share the graph initializers between custom ops
* Update nGraph to 0.22 and exclude Gather entirely
* Enable building on Windows with nGraph v0.21.1-rc.0
* Disable the unsigned input Shrink op tests for nGraph until the next update
* Line-shortening code refactor
* Fix for the master branch merge artifact
* MKLDNN patches adjustment for Windows
* Exclude MatMulInteger for non-const zero points
* Exclude ConvInteger for non-const zero points
* Enable full Cast op support
* Use the v0.22.1 tag
* Skip ConvTranspose_InvalidKernelShape test for ngraph provider
* Create sub-graph ModelProto from fused_node
* remove memory copy between CUDA and TRT
* add info to RegisterExecutionProvider input
* use new IDeviceAllocator for trt allocator
* remove SetDefaultInputsMemoryType from TRT EP
* remove onnx-tensorrt 5.0
* add submodule onnx-tensorrt branch 5.1
* remove redundancy
* Update transformer_memcpy.cc
* Update tensorrt_execution_provider.cc
* switch to TensorRT 5.1.5.0
* update python binding
* disable failed test case on TensorRT
* Update activation_op_test.cc
* upgrade to TensorRT container 19.06
* update according to feedback
* add comments
* remove tensorrt allocator and use cuda(gpu) allocator
* update onnx-tensorrt submodule
* change ci build cuda directory name
* Update DNNLibrary
* Allow fp16 by default
* Add nnapi build in ci
* Fix nnapi ep after #1268
* Remove unused variables
* Support nnapi in onnx_test_runner
* Update DNNLibrary to fix tests
* Update build.py for android build support, solve conflict of
tools/ci_build/build.py
* Support non-ARM Android build, solve conflict of tools/ci_build/build.py
* Enable android test by x86_64 android emulator
* Add dnnlibrary/NNAPI support in build.py
* suppress the verbose adb output
* Remove debug logs
* Install cmake by pip
* Fix undefined host_protoc_path
* cmake==3.13.2 in pypi is actually 3.12.2, so install 3.13.2.post1 instead
* Fix Android ARM64 build
* Use android ndk r20 instead of r19c, fix conflicts in install_deps_android.sh
* sync onnx to get equal op with float support
* doc update
* fix test failure because of updated shape inference logic for roialign.
* filter consum test cases since it's not implemented yet.
Description: Describe your changes.
Change the logic to find cublas dll
Motivation and Context
Why is this change required? What problem does it solve?
The name pattern of cublas changed since 10.1. It doesn't include minor version in its name anymore.
If it fixes an open issue, please link to the issue here.
Add MlasGetPreferredBufferAlignment() for use by CPUAllocator::Alloc to get the byte alignment for CPU tensors. Using MLAS allows the value to be based on the platform the binary is running on instead of a constant value fixed at compile time.
* Add arm64 nocontribops pipeline
* minor fix
* Added new template for arm build -- disable all tests
* fix build command
* add arm64 flag for msbuild
* add arm leg as upstream dependency
* update platform to arm64 for msbuild
* remove test task from arm build
* remove ESRP signing of C# dlls in arm build
* Updated to work for both --arm and --arm64
* Make the cross compiling cmake flags symmetric
* Add dynamic check for /Wno-error flag, instead of extra build option
* remove extra full-stop
* replace log sinks
* limit headers to include dir
* first changes to do dynamic linking
* wip for using cxx api
* remove weird dangling dependency
* building with tests failing
* finish updating converters
* fix const
* intital introduction of typedef
* change logging to use spdlog
* get tests passing
* clang format
* map logging levels better
* clean up unused imports
* trent cr comments
* clang-format
* code review comments
* changing buffer use to reserve
* Dynamically link
* revert tvm
* update binary uploading
* catch exceptions by const-ref
* Revert "revert tvm"
This reverts commit 387676dd1018134d15eb71fa126f7caf94380800.
* fix typo
* update versioning of lib
Description:
This change adds the common part of TVM based codegen library. It includes following parts:
* Microsoft TVM Inventory (MTI): a set of TVM ops for neural networks, similar to TOPI
* Compiler pass for traversing ONNX graph and generate TVM ops
* Compiler pass for traversing generated graph and specify TVM schedule
* Compiler pass for handling weight layout
* Utils for debugging
Motivation and Context:
TVM is an open deep learning compiler stack for cpu, gpu and specialized accelerators. To leverage it in ONNX, we built an execution provider named Nuphar. Currently, Nuphar gets good performance on CPUs with AVX2 on quantized LSTM models.
This codegen library was part of Nuphar execution provider. It is split out for sharing with other execution providers, as we'd like to reuse TVM in more devices.
* init
* Update DNNLibrary
* Update DNNLibrary, set compiler flags, it compiles now
* Add more missing flags, add test
* Update DNNLibrary
* Update Compile method, fix allocator and some other bugs
* Update DNNLibrary
* Implement CopyTensor
* Not delete state explicitly since it is managed by unique_ptr
* Add the missing files when SingleUnitTestProjct is ON
* misc changes
* Fix wrong name in provider factory
* Add my own test
* Update the code of add node into graph, and add the missing initializer into graph
* Fix the bug that re-build the graph produces extra output
* Update DNNLibrary
* Transpose nchw (ONNX) -> nhwc (NNAPI)
* Add license
* Add GetSupportedNodes method (implement it later)
* Rename onnxruntime_nnapi_test->onnxruntime_nnapi_squeezenet_test
* Update squeezenet_test.cpp after rebase master
* Remove squeezenet_test.cpp since it is almost same with the c++ sample
* Update DNNLibrary for GetSupportedNodes
* Update GetSupportedNodes
* Revert "Remove squeezenet_test.cpp since it is almost same with the c++ sample"
This reverts commit a97575fd9ff49e50ba1dc8d8154790d8cd86c48d.
* Update DNNLibrary
* Fix multiple outputs bug
* Remove GetKernelRegistry
* Revert "Revert "Remove squeezenet_test.cpp since it is almost same with the c++ sample""
This reverts commit 2a0670e9cbf10ea654111ce39e198a4be0ddd838.
* Set default memory type of NNAPI EP
* Add CPUOutput allocator
* Update DNNLibrary for multiple outputs
* Fix bug of nhwc->nchw
* Remove GetExecutionHandle()
* Update cuda for python wheels
* Update cuda for python wheels
* Update cuda for python wheels
* Update azure-pipelines-py-packaging.yml
* Update to cuda 10
* Only test win gpu
* Update cuda for python wheels
* Use manylinux2010 image to build linux python wheels
Allow wheels built to truly be compliant with a manylinux policy
Implementation of the MLAS changes for NCHWc convolution/pooling support. These changes adopt the blocking format used by MKL-DNN and other convolution libraries for better performance.
* move all contrib ops to one place
* namespace changes
* bug fix - remove redundant file after merge master
* plus more minor bug fixes
* bug fix
* fix extra space in include header + namespace fix
* fix linux build failure:
* fix test group names
* remove redundant test
