* Set ARM agent pool
* Set CMake generator to VS 2019 in ARM
* Use system-wide CMake instead of custom version
Our custom version is too old for VS 2019
* Use DML and build shared lib in ARM CI
* Restore nuget packages in ARM CI
* Disable DML
* Refactor ARM debug/release builds
* Use system packaged Python version
* Remove hardcoded Python path
* Downgrade Python to 3.7 for build
* Remove explicit CMake path
* Disable Attention fusion tests when DISABLE_CONTRIB_OPS is defined (#2529)
* Setup java ci (#2528)
* Add provision in ORT for session options to be parsed when available via model file (#2449)
* Initial commit
* Fix gitmodules
* Nits
* Nits
* Updates
* Update
* More changes
* Updates
* Update
* Some updates
* More changes
* Update
* Update
* Merge
* Update
* Updates
* More changes
* Update
* Fix nits
* Updates
* Fix warning
* Fix build
* Add comment
* PR feedback
* PR feedback
* Updates
* Updates
* Update
* More changes
* Fix build break
* Comment test for now
* Updates
* Updates
* PR feedback
* Updates
* Nits
* Add tests
* Fix build
* Fix build
* Fix build
* Fix build break
* Fix build
* Nits
* PR feedback
* More change
* Expose GetSessionOptions in pybind logic and add unit test for python
* Fix build
* PR feedback
* PR feedback
* Revert "Disable thread pool creation when enabled OpenMP (#2485)" (#2535)
This reverts commit 7c7d5a149c.
* Add dynamic shape support in TensorRT execution provider (#2450)
* remove onnx-tensorrt submodule
* add new onnx-tensorrt submodule (experiment) for trt6
* update engine build for trt6
* update compile and compute for tensorrt6.0
* Update tensorrt_execution_provider.cc
* Update tensorrt_execution_provider.cc
* Update tensorrt_execution_provider.cc
* Update tensorrt_execution_provider.cc
* switch to onnx-tensorrt master for TensorRT6'
* Update tensorrt_execution_provider.cc
* Handle dynamic batch size and add memcpy in TensorRT EP
* update test cases
* Update tensorrt_execution_provider.cc
* update onnx-tensorrt submodule
* Update Dockerfile.ubuntu_tensorrt
* Update Dockerfile.ubuntu_tensorrt
* Update run_dockerbuild.sh
* Update run_dockerbuild.sh
* Update install_ubuntu.sh
* Update concat_op_test.cc
* Update tensorrt_execution_provider.cc
* Upgrade TensorRT to version 6.0.1.5
* Update onnxruntime_providers.cmake
* Update CMakeLists.txt
* Update reduction_ops_test.cc
* Update install_ubuntu.sh
* Update Dockerfile.ubuntu_tensorrt
* Update Dockerfile.tensorrt
* Update BUILD.md
* Update run_dockerbuild.sh
* Update install_ubuntu.sh
* Update onnxruntime_providers.cmake
* Update install_ubuntu.sh
* Update install_ubuntu.sh
* Update gemm_test.cc
* Update gather_op_test.cc
* Update CMakeLists.txt
* Removed submodule
* update onnx-tensorrt submodule
* update header file
* Removed submodule
* add submodule onnx-tensorrt kevin's branch shape-test'
* add debugging code
* Update tensorrt_execution_provider.cc
* Update tensorrt_execution_provider.cc
* merge master
* Removed submodule
* update onnx-tensorrt submodule
* add more changes for dynamic shapes
* Update tensorrt_execution_provider.cc
* update for dynamic shape
* update dynamic shape processing
* fix logger issue
* remove submodule onnx-tensorrt
* add submodule onnx-tensorrt
* add env variable min_subgraph_size
* remove redundency
* update document
* use onnxruntime::make_unique
* fix multi-run issue
* remove some tests to save CI build time
* Add dynamic shape test
* Update TensorRT-ExecutionProvider.md
* Add example of running Faster R-CNN model on TensorRT EP
* Add more details on env variables
* update environment variables
* Update tensorrt_basic_test.cc
* Update model tests
* Update tensor_op_test.cc
* remove --use_full_protobuf
* Update build.py
* User/xianz/telemetry (#2458)
* enabme telemetry
* enable telemetry
* set enable telemetry as default
* for debugging
* remove log and set disable telemetry as default back
* delete private file while testing
* resolve comment: mainly add license header, rename macro and update docs
* rewording in privacy.md
* Fix integer overflow in cuda NonMaxSuppression implementation (#2540)
* add test case that should pass but fail
* fix nms
* extract int_max_output_boxes_per_class
* Introduce container type runtime checks and other improvements (#2522)
Rework TensorSeq in a manner consistent with Tensor and SparseTensor
in terms of type system setup.
Reduce templating. Introduce helpers to ensure the same
data type.
Make OrtValue __dtor not virtual.
Introduce ContainerChecker
* Fix C API tests for centos and mac (#2544)
* change c++14 to c++11
* add ld lib path for centos
* enable csharp tests on macos
* fix C API test on MacOS + fix manylinux dotnet install
* fix manylinux dotnet install
* fix lib link
* Add back executable bit to build.py
* Fix a bug handling negative begin pad values in Pad op (#2550)
* Fix bug in Pad op
* Update
* DNNL CMAKE update (#2548)
* Fix android build (#2558)
* Update win-x86-ci.yml (#2557)
Fix build pipeline break
* Re-enable Windows C# tests (#2564)
* disable onnx_test_runner -x invocations for dnnl (#2568)
* Allow sequence length to be symbolic (#2559)
* setup java ci mac (#2570)
* make layernorm fusion to support opset 11 (#2545)
* Fix a warning found in the latest VS release
* Add more check on SkipLayerNorm and BiasGelu fusion (#2574)
* Fix file not found error during docker build. (#2569)
* Add ConvTranspose1D (#2578)
* Ryanunderhill/packagename test (#2582)
* [Nuphar EP] fixes for some object detection models (#2581)
Update notebook tutorial with multi-threaded int8 GEMM from #2517
* EmbedLayerNormalization Fusion Improvement (#2553)
Embedding layer norm fusion improvements - add more checks
* Update version (#2584)
* Temporarily exclude vgg19 test from Python backend test
1. temporarily exclude vgg19 test which comsumes too much memory, run out of memory on Upsquared device. Single test pass for vgg19, need furture investigation (#2588)
2. Update docker file to decrease the docker image size
* Update docs for Android NNAPI EP (#2586)
* Fix lto bug for protobuf and ubuntu
* add path to build dir before test run (#2590)
* Add missig env variables for mac pipeline test (#2595)
* Fixed an issue in updating realized dims (#2597)
when we update realized dims for scan's output, the sliced axis also
needs to be inclusive, i.e. we should check with "dim >= insert_inclusive_axis",
because the offset in the symbols are based on Scan sugraph.
Otherwise, we would end up with shape mismatch later.
* Java API for onnxruntime (#2215)
* Add support for opset 11 in reshape fusion (#2592)
Support opset verion 11 in reshape fusion
* Rename automl python tools folder to featurizer_ops. (#2593)
* Support opset 11 subgraph of Squad model in Embed Layer Normalization (#2605)
Support opset 11 Squad model that is exported from PyTorch nightly. The embed layer uses Range op which is missed in the transformer.
* symbolic shape inference: fix warnings in GPT-2 model (#2608)
And revise nuphar perf test on BERT squad
* Dump subgraph ID and fused graph ID (#2607)
* Dump subgraph ID and fused graph ID
Dump subgraph ID and fused graph ID for better debugging
* Remove local static fused_count
added a field global_fused_count_ to NupharExecutionProvider class
* EmbedLayerNormalization Fusion For Dynamic Squad Model Opset 10 (#2613)
Support subgraph of SQuAD model exported from pytorch with dynamic input axes
* Allow providers to be set for InferenceSession at construction (#2606)
* Remove unnecessary parameter in some places in GatherElements implementation (#2612)
* Remove unnecessary parameter in some places
* Update
* Update
* Make sure fenced tensor could not reuse other tensor. (#2561)
Fix random error caused by this.
* Improve Embed Layer Norm Fusion for SQuAD with static input shape (#2621)
* fix float16 comparison in initializer (#2629)
* epsilon attribute for layernormalization fusion (#2639)
* removed unnecessary batch file and fix path (#2640)
* Add shape inference to ConvTransposeWithDynamicPads schema (#2632)
* Improve cuda expand() opeator's performance. (#2624)
* Cuda pad optimize when no padding is needed. (#2625)
* Shortcut cuda Pad() when no padding is needed.
* Optimize cuda scatter() on 2D compatible. (#2628)
* Optimize cuda scatter() on 2D compatible.
* Add some comments.
* fix build error for ARM (#2648)
* Improve performance of resize() in Nearest mode (#2626)
Special treatment for 2D, check same size as input image.
And in 2d kernel, template use_expolation.
* Fix memory exception in Layer Norm Fusion (#2644)
* Windows CI changes(#2650)
* Revert "User/orilevari/windowsai master merge (#2674)"
This reverts commit fe26146311.
1. Move Win GPU pipeline to VS2019
2. Move C API pipeline to VS 2019
3. Move nuget mklml pipeline to VS 2019
4. Move windows no contrib ops pipeline to VS 2019
Advance commit to 4df80d5865a9d4e97f6d0b9304d4316115a04d9e
Add generated code for the commit before editing.
Import more featurizers.
Rename Automl ops domain to mlfeaturizers.
Rename conditional compilation macro.
Move and rename files getting rid of automl
Rename --use_automl build switch to --use_featurizers
Rename CMake option accordingly. Rename automl CMake targets.
Adjust CI and packaging pipeline switches.
Rename namespace automl to featurizers.
merge resolutions included pulling in telemetry logic that was merged to master and not windowsai and dereferencing InferenceSession::sessionstate now that it is a unique pointer
1. temporarily exclude vgg19 test which comsumes too much memory, run out of memory on Upsquared device. Single test pass for vgg19, need furture investigation (#2588)
2. Update docker file to decrease the docker image size
* change c++14 to c++11
* add ld lib path for centos
* enable csharp tests on macos
* fix C API test on MacOS + fix manylinux dotnet install
* fix manylinux dotnet install
* fix lib link
* enabme telemetry
* enable telemetry
* set enable telemetry as default
* for debugging
* remove log and set disable telemetry as default back
* delete private file while testing
* resolve comment: mainly add license header, rename macro and update docs
* rewording in privacy.md
* add centos tests to linux cpu ci pipeline
* Disable failing test
* use centos6 instead of centos7
* change back to centos7
* add dotnet runtime dependency
* fix dotnet runtime dependencies
* install dotnet sdk instead of runtimes
* add more dotnet dependencies
* temporary skip failing test
* ix lib path
* reenable failing test
* Support test parameters through CLI arguments
* Add WinML do Windows x86/ARM CI builds
* Code style fixes
* Update googletest
Remove GPUTEST macros everywhere now that GTEST_SKIP is supported
* Refactor main.cpp
* Build scenario tests without DML
* update dockerfiles/README (#2336)
* Make elementwise op run 4 items per thread (#2335)
Description: Describe your changes.
Make elementwise op run 4 items per thread
unroll for loop to leverage ILP
remove unnessary N==0 check inside elementwise GPU kernel
Motivation and Context
Why is this change required? What problem does it solve?
It can improve the performance of GPU elementwise ops. ~2% performance gain on popular NLP bert model.
If it fixes an open issue, please link to the issue here.
* Add CUDA GatherElements kernel (#2310)
* Updates
* Update test
* Update
* Updates
* nits
* PR feedback
* Update
* Update
* PR feedback
* PR comments
* Update
* Fix build
* Fix build
* Nits
* Fix
* Layer Normalization Fusion (#2319)
basic layer normalization transform
* Add FastGelu Cuda Op for Gelu and Add bias fusion (#2293)
* Add FastGelu cuda op
* Add AddBiasGelu for experiment
* Revert "Add AddBiasGelu for experiment"
This reverts commit 5c1ee019858c657e6bb75887265cb85675626e5b.
* Add bias
* Add unit tests
* update comment
* update script
* fix build error
* update coding style
* update for CR feedback
Enable half2 optimization only when cuda arch >= 7.0
* move _Tanh to common.cuh
* implement CPU contrib OP Attention (#2333)
* Remove unused initializer from GraphProto as well as name_to_initial_tensor_ in CleanUnusedInitializers. (#2320)
* Remove unused initializer from GraphProto as well as name_to_initial_tensor_ in CleanupUnusedInitializers.
This means initializers that have been replaced during graph optimizations are not left in the GraphProto when we save an optimized model.
* Handle edge case where a model has an unused initializer with matching graph input by also removing the graph input.
* Use non-const iterators in std::find_if calls to make centos build happy.
* Nuget pipeline changes (#2305)
1. refactor the pipeline, remove some duplicated code
2. Move Windows_py_GPU_Wheels job to Win-GPU-CUDA10. We'll deprecated the "Win-GPU" pool
3. Delete cpu-nocontribops-esrp-pipeline.yml and cpu-nocontribops-pipeline.yml
4. In Linux nuget jobs, run "make install" before creating the package. So that extra RPAH info will be removed
* Cuda Reverse Sequence Op, maping types of same size using same template function. (#2281)
* Set ElementType to String type of node metadata, instead of byte[] (#2348)
* Set ElementType to String type of node metadata, instead of byte[]
* Fix spacing
* Introduce PrimitiveType into a Type System along with an integer constant (#2307)
Improve perf by avoiding GetType<T>() calls. Introduce MLTypeCallDispatcher to switch on Input Type. Add Tensor IsType<T>() fast method.
* Fix/test dim value of 0 handling in a couple of places (#2337)
* Update the CUDA Where implementation broadcasting logic to handle a dim with value of 0.
Add unit test
Also add unit test for unary op with dim value of 0
* Exclude ngraph from Where test with 0 dim.
* Openvino EP R3.1 onnxrt server (#2357)
* onnxrt server with OVEP
* onnxrt server with OVEP
* Update Dockerfile.server.openvino
* onnxrt server OVEP fix reviews
* onnxrt server OVEP fix reviews
* Implement cuda nonzero op. (#2056)
Implement cuda nonzero op.
* Direct use python numpy array's memory if already contiguous. (#2355)
* Direct use python numpy array's memory if already contiguous. This
could greatly improve performance for session with large input,
like big image 1920x1080 fastrcnn, 30~40% speed up could be achieved.
* Add test case enforce contiguous/non-contiguos numpy array as inputs.
* Add helper to create output to minimize binary size. (#2365)
Add ConstEigenTensorMap typedef so we don't unnecessarily const_cast the const input Tensor.
* fix builds enabling onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS (#2369)
* fix builds enabling onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS
* update
* Add Tracelogging for profiling (#1639)
Enabled only if onnxruntime_ENABLE_INSTRUMENT is ON
* test bidaf with nuphar for avx target (#2370)
increase nuphar test coverage a bit
* Fix a bug in TLS refcount that may destabilized CUDA CI (#2374)
* update output size calculation for resize (#2366)
* change how output size is calculated for resize op
* add tests for ver 10 resize
* Extend OneHot CPU kernel to support more types (#2311)
* Extend OneHot CPU kernel to support input int64_t, depth int32_t, output float
* Skip BERT before the test data fix is picked up
* Fix bug with Slice. Need to pass in flattened input dimensions so the initial offset into the input is calculated correctly. (#2372)
* Add opset 11 version of Split to CUDA ops (#2376)
Organize the CUDA ops definitions so all the opset 10 and 11 parts are together (same setup used for CPU ops)
* Layer Norm Fusion Fix (#2379)
* layer norm fusion fix
* Add input shape check in code and unit tests
* Fuse Add + Gelu (#2360)
Implement the transformer to fuse add + gelu
Implement the accurate kernel
* Skip layer norm transform (#2350)
* skip layer normalization transformer
* Another try to stabilize CUDA CI (#2383)
The root cause seems to be failure in CUDA dealloc when tear down. cudaFree return code was ignored before, so should the debug check.
* fix BUILD.md typo (#2375)
build.py: error: argument --config: invalid choice: 'RelWithDebugInfo' (choose from 'Debug', 'MinSizeRel', 'Release', 'RelWithDebInfo')
* Fixed compilation with ngraph (#2388)
* Fix reuse logic in allocation planner. (#2393)
* Fix reuse logic in allocation planner.
* PR comments
* Add helpful comments
* Don't allow reuse across string tensors.
* [NupharEP] Multiple optimizations (#2380)
Fuse transpose into MatMul
Implement Pow and constant scalar simplification
Vectorize ReduceMean
Improve symbolic shape inference
Minor updates for better debugging in fused function name
* Avoid using the default logger in the graph lib and optimizers (#2361)
1. Use the session logger if it is available.
2. Don't disable warning 4100 globally. We should fix the warnings instead of disabling it.
* Change CUDA implementation of Transpose to support all fixed size tensor types (#2387)
* Change CUDA implementation of Transpose to not use a typed kernel so we can support more types with minimum binary size.
Add support for 8, 16, 32 and 64 bit types.
Add unit tests.
Add method so the implementation can be called directly (will be used by CUDA Scan very soon).
* Disable TensorRT for MLFloat16 and int8 unit tests.
* Address PR comment and add support for calling cublas implementation if type is mlfloat16.
* Add opset 11 versions of the existing CUDA operators that had negative axis support explicitly added. (#2398)
* Add opset 11 versions of the existing CUDA operators that had negative axis support explicitly added.
* [NupharEP] force some low/zero cost ops to be inlined (#2409)
* fix cross compile bug (#2415)
* Minor optimization: if a node has already been placed, there's no need to find a kernel for it. (#2417)
* Add Reshape Fusion (#2395)
* Add reshape fusion
* Add some comments
* update comments
* update comment format
* update according to feedback
* update for recent logger change
* fix build error
* (1) Support both input and output edges in find path in graphutils
(2) Add a test case of only one constant initializer of Concat input.
(3) Refactor ReshapeFusion class to allow add more subgraph fusion in the future.
* fix error
* (1) loose constraint on initializer: non constant is allowed for reshape fusion.
(2) Change versions type to vector.
(3) Add logging.
(4) Return false when multiple output edges matched in FindPath. Add comments.
* only allow one direction (input or output) in FindPath
* [NupharEP] Update notebook and docker image (#2416)
Add BERT squad in Nuphar tutorial
Enhance speed comparsion readability
* Fix the issue in matmul_add_fusion (#2407)
Fix the issue in matmul_add_fusion
If Muatmul + Add has shape [K] * [K, N], reset it to [1, K] * [K, N] will make the output shape to [1, N] will also requires a reshape on the output.
Fix: just remove the shape reset to not fuse it.
Add a negative test case for matmul+add fusion
* feat(treeregressor): Update TreeEnsembleRegressor for type support (#2389)
Updates the `TreeEnsembleRegressor` to allow for `double`, `float`,
`int64`, and `int32` inputs to match the upstream specification.
Signed-off-by: Nick Groszewski <nicholas.groszewski@capitalone.com>
* onnxrt server documentation update (#2396)
* Added support for Pad-2 operator in OpenVINO-EP (#2405)
* Add CUDA If operator. (#2377)
* Add CUDA If operator.
Uses CPU operator for implementation.
By adding a CUDA version the inputs/outputs (with the exception of the 'cond' input) stay on GPU, and no other logic is required to avoid a copy to CPU across the control flow node.
* Improved documentation for onnxruntime::utils::SwapByteOrderCopy(), added precondition check.
* Fix the type constraints on CUDA If operator to exclude strings. (#2431)
* add Im2col<uint8_t> (#2438)
* Adjust codegen vectorization width from target (#2439)
* Adjust codegen vectorization width from target
* Add CUDA Scan operator. (#2403)
* Add Scan CUDA op.
Uses CPU implementation for logic.
Added some device specific functors for handling when data needs to be manipulated on a different device.
Added ability to override the materialization logic in the OrtValue slicer so DML can plugin their handling.
* Fix Windows GPU C API packaging pipeline failure (#2440)
Fix Windows GPU C API packaging pipeline failure (#2440)
* Correctly handle implicit inputs for fused nodes (#2390)
* Correctly handle implicit inputs for fused nodes
Previously, nuphar's partitioning function didn't include
node's implicit inputs into the inputs list of MetaDef, and hence
a crash was triggered in the onnx graph checker.
This commit fixed the issue. Furthermore, it also fixed a related
issue where we didn't add implicit inputs into
graph_inputs_excluding_initializers_ in Graph::SetGraphInputsOutputs.
the issue was that graph_inputs_including_initializers_ populated by
SetInputs (e.g. called by FunctionImpl::FunctionImpl) may contain
implicit inputs which were not of any node's initializers in the graph.
Because they were not part of any initializers, these implicit inputs
couldn't be visited by going through all nodes' inputs.
Consequently, they would *not* be added into graph_inputs_excluding_initializers_.
We fixed the issue by first copying the populated graph_inputs_including_initializers_
into graph_inputs_excluding_initalizers_, which then had both initializers and
non-initializers as its initial content. Later, we erase initializers from the
list. In this way, we can ensure all implicit inputs to remain in
graph_inputs_excluding_initializers_.
* refined comments and fixed duplicates
Address CR by revisiting comments in terms of implicit inputs
Also fixed an issue by skipping duplicates while copying inputs
from graph_inputs_including_initializers_.
* address CR
explain why we need to collect nodes' implicit inputs
* don't rely on pointer values for iterating std::set
Previously, openvino relied on iterating a set of NodeArg pointers
to construct inputs and outputs for a fused graph. It could cause
non-determinism. The reason was that although iterating std::set by
itself is stable, pointer values of NodeArgs may vary. Consequently,
we could end up visiting the set's elements in different orders for
different runs for the same test, which resulted in constructing
inputs (and outputs) with different orders to the fused graph.
For example, for the same test, we may have inputs [A, B] in some
runs but inputs[B, A] in others.
Let's use std::string as the key type to avoid such nondeterminism.
This commit also added implicit inputs into meta->inputs while returning
the capability from the openvino provider.
* Fixed another latent issue in openvino's GetCapability function
The issue was that we couldn't simply erase fused_inputs and fused_outputs
while iterating the nodes. For example, an output NodeArg may have multiple
uses, and it's wrong if we erase it from fused_outputs when we encounter only
one of its uses as input.
* Remove DeviceAllocatorRegistry class (#2451)
Remove DeviceAllocatorRegistry class
* CSharp api and test for loading custom op shared library (#2420)
- Added C-API test for loading custom op shared lib.
- Made some changes in C++ api header and C-api implementation to get it working.
- Added C# API and corresponding test for loading custom op shared library.
* Parallel Gelu with ParallelFor (#2399)
Parallel Gelu to get better performance for Gelu
* Clean up build.py (#2446)
* Pull the latest image before running docker build
* Fuse SkipLayerNorm with Bias (#2453)
Fuse SkipLayerNorm with Bias
* Allow more than one invocation of CreateEnv in the same process. (#2467)
* Allow more than one invocation of CreateEnv in the same process.
* Fix centos build
* Symbolic shape inference improvements: (#2460)
* Symbolic shape inference improvements:
- add a mode to guess unknown ops' output rank
- add support for GatherND
- add support for If
- fix a bug in get_int_values when then tensor rank > 1D, by treating it as no sympy data
- add symbol to literal merge when ONNX silently merges dims
- fix a bug in Concat when input dim is 0
- fix a bug in ConstantOfShape that computed dim is not updated
- add support for dynamic shape in ConstantOfShape
- fix a bug in Loop output shape that loop iterator dim is not inserted at dim 0
- add support for dynamic padding in Pad
- add support for dynamic shape in Reshape
- add support for Resize with opset > 10, by treating output dims as dynamic
- fix a bug in Slice when starts/ends are dynamic
- restrict input model to opset 7 and above
- make output model optional to avoid disk write when testing
Run model tests for symbolic shape inference
Reduce 2GB docker image size of nuphar
* add additional test data set for nuget pipeline (#2448)
* add SAS token to download internal test data for nuget pipeline
* update azure endpoint
* fix keyvault download step
* fix variable declaration for secret group
* fix indentation
* fix yaml syntax for variables
* fix setting secrets for script
* fix env synctax
* Fix macos pipeline
* attempt to add secrets to windows download data
* fix mac and win data download
* fix windows data download
* update test data set url and location
* add SAS token to download internal test data for nuget pipeline
* update azure endpoint
* fix keyvault download step
* fix variable declaration for secret group
* fix indentation
* fix yaml syntax for variables
* fix setting secrets for script
* fix env synctax
* Fix macos pipeline
* attempt to add secrets to windows download data
* fix mac and win data download
* fix windows data download
* update test data set url and location
* Pass flags to build/test WinML in CI
* Add initial CMake config for unit tests in WinML
* Set winml_unittests standard to C++17
* Add WinML API tests and port them to googletest
* Install WinML test collateral
* Add LearningModelSessionAPITests ported to googletest
* Fix WinML test files encoding
* Add GPU tests
* Add parameterized test, skip GPU tests
* Enable precompiled header
* Remove unused code and collateral
* Remove brand images
* Add dllload.cpp
* Remove images not used in API tests
* Add LICENSE.md to image collaterals
* Add models with licenses
* Remove FNS Candy tests
* Add API test models
* Add ModelInSubdirectory
* Install collaterals post-build with copy_if_different, split common lib
* fix warnings
* Link to gtest_main
* add option to enable winml telemetry
* add option to enable winml telemetry
* clean logs while developping
* clean the log of GUID
* compile onnxruntime_common with winml telemetry
* use option for use_telemetry
* rename option winml_use_telemetry to onnxruntime_use_telemetry
* little change
1. refactor the pipeline, remove some duplicated code
2. Move Windows_py_GPU_Wheels job to Win-GPU-CUDA10. We'll deprecated the "Win-GPU" pool
3. Delete cpu-nocontribops-esrp-pipeline.yml and cpu-nocontribops-pipeline.yml
4. In Linux nuget jobs, run "make install" before creating the package. So that extra RPAH info will be removed
