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118 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
William Tambellini
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c033cfee92
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Check python version (#356)
Check python version in build.py |
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stevenlix
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da653ccdac
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Upgrade TensorRT to version 7.0.0.11 (#2973)
* update onnx-tensorrt submodule to trt7 branch * add fp16 option for TRT7 * switch to master branch of onnx tensorrt * update submodule * update to TensorRT7.0.0.11 * update to onnx-tensorrt for TensorRT7.0 * switch to private branch due to issues in master branch * remove trt_onnxify * disable warnings c4804 for TensorRT parser * disable warnings c4702 for TensorRT parser * add back sanity check of shape tensort input in the parser * disable some warnings for TensorRT7 * change fp16 threshold for TensorRT * update onn-tensorrt parser * fix cycle issue in faster-rcnn and add cycle detection in GetCapability * Update TensorRT container to v20.01 * Update TensorRT image name * Update linux-multi-gpu-tensorrt-ci-pipeline.yml * Update linux-gpu-tensorrt-ci-pipeline.yml * disable rnn tests for TensorRT * disable rnn tests for TensorRT * disabled some unit test for TensorRT * update onnx-tensorrt submodule * update build scripts for TensorRT * formating the code * Update TensorRT-ExecutionProvider.md * Update BUILD.md * Update tensorrt_execution_provider.h * Update tensorrt_execution_provider.cc * Update win-gpu-tensorrt-ci-pipeline.yml * use GetEnvironmentVar function to get env virables and switch to Win-GPU-2019 agent pool for win CI build * change tensorrt path * change tensorrt path * fix win ci build issue * update code based on the reviews * fix build issue * roll back to cuda10.0 * add RemoveCycleTest for TensorRT * fix windows ci build issues * fix ci build issues * fix file permission * fix out of range issue for max_workspace_size_env |
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Changming Sun
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abb626ff60
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Provide alternative std::mutex implementation on Windows (#3000)
Provide alternative std::mutex implementation on Windows. OrtMutex is no longer an alias of std::mutex. We do it because: 1. This new thing is faster and much much simpler. 2. Static constructors are considered harmful. We should avoid such thing as possible as we can. |
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Ori Levari
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0beb75ce77
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populate file metadata for onnxruntime.dll (#2978) | ||
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smk2007
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c32cedc6c9
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Merge windowsai (winml layering) into master (#2956)
* Initial Commit * Merged PR 3985217: add onecoreuap_apiset.lib in order to avoid linking against kernel32.lib etc (#2346) add onecoreuap_apiset.lib in order to avoid linking against kernel32.lib etc and violating our OS layering requirements. We linked against onecoreuap_apiset.lib in VB so we will continue doing this, but I am still unsure why not to link against onecore instead since that is where we ship. However, since Sheil is the owner of this code we will wait to discuss with him before changing anything. * Initial changes for layering * more snipping to get core into ort * update build instructions to include --build_shared_lib (#2358) * update build instructions to include --build_shared_lib * fix line breaks * Task 23998197: add winml_lib_core into onnnxruntime.dll (#2368) * Task 23998197: add winml_lib_core into onnnxruntime.dll * PR feedback build break on perf_test * return proper error when the model path isn't found (#2391) * LearningModelSession is cleaned up to use the adapter, and parts of b… (#2382) this is a big PR. we are going to move it up to layer_dev , which is still a L3 so we are still safe to do work there agile. we are going to move this into the L3 so that ryan can start doing intergration testing. we will pause for a full code review and integration test result prior to going into the L2. >>>> raw comments from previous commits >>> * LearningModelSession is cleaned up to use the adapter, and parts of binding are. * moved everything in the winmladapter made it all nano-com using, WRL to construct objects in the ORT side. base interfaces for everythign for winml to call cleaned up a bunch of winml to use the base interfaces. * more pieces * GetData across the abi. * renamed some namepsace cleaned up OrtValue cleaned up Tensor cleaned up custom ops. everything *but* learnignmodel should be clean * make sure it's building. winml.dll is still a monolith. * model moved over. everything builds clean. step ! * weak ref comment * Layer dev paulm (#2408) * model moved over. everything builds clean. step ! * weak ref comment * added a wrapper for RoGetActivationFactory to hook back into winml for creating winml objects. fixes model load. * Layer dev paulm (#2414) * model moved over. everything builds clean. step ! * weak ref comment * added a wrapper for RoGetActivationFactory to hook back into winml for creating winml objects. fixes model load. * User/xianz/win ml telemetry (#2410) * 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 * fixed some lifetime management. fixed the debug build. squeezenet passes using winmlrunner for CPU and GPU * Layer dev paulm (#2423) * model moved over. everything builds clean. step ! * weak ref comment * added a wrapper for RoGetActivationFactory to hook back into winml for creating winml objects. fixes model load. * fixed some lifetime management. fixed the debug build. squeezenet passes using winmlrunner for CPU and GPU * PR feedback. * Layer dev paulm (#2424) * model moved over. everything builds clean. step ! * weak ref comment * added a wrapper for RoGetActivationFactory to hook back into winml for creating winml objects. fixes model load. * fixed some lifetime management. fixed the debug build. squeezenet passes using winmlrunner for CPU and GPU * PR feedback. * couple of fixes and coded getmutabledata() * Layer dev paulm (#2425) * model moved over. everything builds clean. step ! * weak ref comment * added a wrapper for RoGetActivationFactory to hook back into winml for creating winml objects. fixes model load. * fixed some lifetime management. fixed the debug build. squeezenet passes using winmlrunner for CPU and GPU * PR feedback. * couple of fixes and coded getmutabledata() * fixed 2 more heap corruptions * Layer dev paulm (#2426) * model moved over. everything builds clean. step ! * weak ref comment * added a wrapper for RoGetActivationFactory to hook back into winml for creating winml objects. fixes model load. * fixed some lifetime management. fixed the debug build. squeezenet passes using winmlrunner for CPU and GPU * PR feedback. * couple of fixes and coded getmutabledata() * fixed 2 more heap corruptions * Add opset and IR check when loading model (#2413) * Add opset and IR check. * Add test case for future opsets. https://github.com/microsoft/onnxruntime/issues/2371 * fixed map and sequence when passing stl types across the ABI . found a leak in nvidia driver, but skipped it. all winmlapitests pass now * Moved SessionOptions over to the abi * WinML CI (#2412) * 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 * Register WinML TraceLogging provider on Onnxruntime.dll (#2455) * Register WinML TraceLogging provider on Onnxruntime.dll * Add ifdef to make sure trace logging provider has telemetry option when LAYERING_DONE * No need for ifdef for TraceLoggingOptionMicrosoftTelemetry * PR feedback * Move etw registration into lotus environment constructor and deresgister in lotus environment destructor * Brianma/cpuwinml (#2466) * allow building winml cpu without dml. * Brianma/breaks (#2469) * fix some more breaks * learning model doesn't need lotusEnvironment and CPU shouldn't include dmlEP headers * move dml checks out of winml and into the adapter * better error handling * Brianma/fi (#2470) * learning model doesn't need lotusEnvironment and CPU shouldn't include dmlEP headers * User/xianz/win ml telemetry (#2410) * 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 * Add opset and IR check when loading model (#2413) * Add opset and IR check. * Add test case for future opsets. https://github.com/microsoft/onnxruntime/issues/2371 * WinML CI (#2412) * 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 * fix bad merge * Checking in a staging checkpoint point so that Ryan can work with me in parrallel * build break. * Brianma/testfails (#2473) * add missing ir version to dictvectorizer-string.onnx * add missing ir version to relu.onnx * add missing ir version to zipmap*onnx * add IR version to manually generated models * remove an unnecessary ifdef dml * Brianma/windowsai fi (#2475) * 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 * Revert "Brianma/windowsai fi (#2475)" This reverts commit |
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Changming Sun
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7ff5c0e5a3
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CMake changes (#2961)
1. Add support for vstest. 2. Add support for vcpkg. To use it: ```bat vcpkg install zlib:x64-windows benchmark:x64-windows gtest:x64-windows protobuf:x64-windows pybind11:x64-windows re2:x64-windows mkdir build cmake ..\cmake -DCMAKE_BUILD_TYPE=Debug -A x64 -T host=x64 -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake -DVCPKG_TARGET_TRIPLET=x64-windows -Donnxruntime_PREFER_SYSTEM_LIB=ON ``` 3. New cmake option: onnxruntime_PREFER_SYSTEM_LIB, which allows user using the preinstall libs instead of the things in onnxruntime submodule. 4. New cmake option: onnxruntime_ENABLE_MEMLEAK_CHECKER, which allows user turn on/off the memory leak checker by @RyanUnderhill in Windows Debug Build. The checker doesn't work with vstest. 4. Fix the post merge pipeline(Mainly for test coverage report). 5. Ignore the compile warning from the Featurizer library code 6. Apply "/utf-8" VC compile flag to our code. Without this, you can't build onnxruntime on Chinese Windows. 7. Remove the SingleUnitTestProject cmake option because it's deprecated more than one year and nobody is using it. 8. Move opaque api tests to onnxruntime_test_all 9. Enable "/W4" on CUDA ep's C++ code(Not the *.cu files), and fix some warnings, add some extra checks. 10. Delete the onnxruntime::test::TestEnvironment class. 11. Add a DLLmain for onnxruntime.dll. 12. Allow dynamic link to libprotobuf |
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edgchen1
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79f1756d8e | Removed build.py unused test_data_url and test_data_checksum options. | ||
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Yingge WAN
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07502ec14e |
Fix dnnl wheel package name (#2823)
* Append '-dnnl' to whl package name when --use_dnnl * Update build.py |
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George Wu
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31200ed92c
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speed up Windows TRT CI (#2811)
* don't run cuda tests if building with tensorrt * remove unnecessary build options for win trt ci * refactor win gpu tensorrt ci yml * --numpy_version=1.17 * update * update * azcopy and cuda path |
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Changming Sun
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fd334aff44
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Update numpy to 1.18 (#2758)
* Update numpy to 1.18 |
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Changyoung Koh
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b40a85a0e8 |
Fix build for Python 3.8 (#2747)
* Fix build for Python 3.8 |
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Changming Sun
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c7a9c6b488
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Split onnxruntime server to a separated folder (#2744) | ||
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Changming Sun
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b42cb61904
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Packaging pipeline changes for VS 2019 (#2711) | ||
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Changming Sun
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89d6bfaa94
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VS 2019 build pipeline changes (#2693)
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 |
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Dmitri Smirnov
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ce7a180f21
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Import more featurizers with tests (#2685)
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. |
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Adam Pocock
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35ceb1a6a6 | Java API for onnxruntime (#2215) | ||
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shahasad
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eeb28a80c0
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setup java ci mac (#2570) | ||
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George Wu
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73c682b97c
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disable onnx_test_runner -x invocations for dnnl (#2568) | ||
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Changming Sun
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bec4abf074 | Add back executable bit to build.py | ||
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Xiang Zhang
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3e7aaf8fa1 |
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 |
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stevenlix
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293b15480b |
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 |
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Sreekanth Yalachigere
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31ea11a696 |
Renaming MKL-DNN as DNNL (#2515)
* DNNL: Moving Files to rename file names * DNNL name change * azure pipeline updated * disable ceil/dialation and enable Opset10 * disable ceil/dialation tests in Python * mlperf_ssd_resnet34_1200 disabled |
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Scott McKay
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1fdf1006ac
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Various fixes coming out of discussions in #2436 (#2497)
- Add --skip_tests option to build.py based on github feedback - Add debug output at end of run_subprocess so it's clearer when the output is from a different process running - Add check for scipy as it's required by gen_test_models.py for the onnx tests - Use log.warning instead of warnings.warn for consistency. We use the logger almost everywhere and somewhat randomly used warnings.warn in two places. - Add check for 'wheel' dependency not being found in setup.py and handle more gracefully - Fix invalid input name in Keras tests |
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Changming Sun
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0341ee9060
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Clean up build.py (#2446) | ||
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Patrick Foley
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151075790d |
[OpenVINO-EP] Update to latest version: OpenVINO 2019 R3.1 (#2308)
* Updates OpenVINO EP to latest version: 2019 R3.1 * Reviews fixed * Update Dockerfile.openvino * Addressed PR comments and disabled model tests temporarily * Update Dockerfile.ubuntu_openvino |
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Klein Hu
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ac9425c119 |
Update ORTSRV integration test model path (#2225)
* Update ORTSRV integration test model path |
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Adrian Tsai
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4090d0d0de
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Add DirectML Execution Provider (#2057)
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 |
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Hector Li
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640f71c91b
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Enable Gpu multi-device test for CUDA EP and Trt EP
Enable multi-device test for GPU * Add build pipeline for TensorRT multi-GPU test * Add code to disable fp16 test if hardware architecture not supported * Add option to set the device id in onnx_test_runner for model tests |
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RandySheriffH
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f501b6e234
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pack pyop in nightly build (#2018)
* pack pyop in nightly build * correct logic * add comment * exclude debug build * add dependency * reset postbuild rule * remove dep |
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stevenlix
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544e53e24e |
Update TensorRT to version 6.0.1.5 (#1966)
* 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 * Add Ubuntu18.04 build option * Add Ubuntu18.04 build option * Add Ubuntu18.04 build option * Add Ubuntu18.04 build option * Remove redundency * Fix issue that it does not add memcopy node correctly if some nodes fall back to CUDA EP. e.g. after partition, there's TRT_Node -> Cuda_node (with CPU memory expected), we still need to add memcpy node between them. * update for Trt Windows build * Update onnxruntime_providers.cmake * Disable opset11 tests on TensorRT * Update pad_test.cc * Update build.py * update scripts for ubuntu18.04 * Disable warning for Windows build |
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daquexian
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e071a1249b | Android CI (#1600) | ||
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suryasidd
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ceaaff0f81 |
[OpenVINO-EP] Enabling VAD-F in OpenVINO Execution Provider (#1885)
* Added support for Hetero plugin Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Fixed spelling error in cmake for hetero plugin Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Added listener to print messages from the plugin Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Updated Documentation for VAD-F enablement Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Added VAD-F option for FPGA *Disabled unit tests and backed tests because FPGA only accepts NCHW models Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Added comment for why tests need to be disabled on VAD-F Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> |
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kile0
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125900c961 |
Enable integration with mimalloc memory allocator (#1673)
* add mimalloc submodule * basic hooks into execution provider header and build script option * pull mimalloc into build * windows has to use the override vcxproj already set up, and disable bfcarena when using mimalloc * fix import_location * generalize build msbuild command * add mimalloc dependency to python package as well as various commenting cleanups * update mimalloc commit as stop gap * include mimalloc changes from master * create capi directory if doesn't exist for mimalloc copying over * disable runtime hooks and remove old comment * temporary change to test CI * fetch the mimalloc output name property * uniformly call target_link_libraries * query cmake to get the correct windows sdk to target * revert change to trailing directory slash * pickup windows sdk off msbuild path if possible * copy the produced dll/so at install time, not configure time * deal with mimalloc unimplemented atomic * move to dev branch of mimalloc to avoid atomic issues on gcc * for windows specify solution settings (x86) rather than individual project settings * pin mimalloc submodule to updated commit * typo * Revert "temporary change to test CI" This reverts commit 764867376936a5d307dded3cc37f00a34e3b0c96. |
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KeDengMS
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58fe5a6bf1
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Enable Nuphar docker build, and reinstate Nuphar tests (#1757)
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 |
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Changming Sun
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94d9161166
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Add nuphar to Linux CI build (#1750) | ||
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KeDengMS
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c9240f4e93
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Implementation of Nuphar execution provider (#881)
* 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. |
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Ashwini Khade
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961b14ac4a
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use MLAS for QGEMM in matmulInteger and convInteger (#1692)
* use mlas qgemm for u8u8_s32 gemms * update test |
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Dmitri Smirnov
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17c8fe44e3
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Integrate featurizers (#1573)
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. |
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shahasad
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0c5d2c998b
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Generate documentation from the registered operator kernels (#1395)
- Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations |
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S. Manohar Karlapalem
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05bbb3065c |
[OpenVINO-EP] Update hardware branding of VAD-R as VAD-M (#1552)
Replaces all occurrences of VAD-R/VAD_R with VAD-M/VAD_M. Aligns with the official hardware branding. |
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daquexian
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93cb29f958 | [WIP] NNAPI EP Update (#1540) | ||
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daquexian
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ec3c553501 |
NNAPI EP Update (#1483)
* 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 |
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Xavier Dupré
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7717ed71a9 | Fix for issue #1397, disable onnxruntime_PYBIND_EXPORT_OPSCHEMA is --gen_doc is not set up (#1398) | ||
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kile0
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7a681fb964 | Improve build throughput and enable using the Visual Studio 2019 cmake generator (#1411) | ||
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Colin Versteeg
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5ee0f185dc |
Add GRPC support to ONNX Runtime Server (#1144)
* add grpc * add-submodule * Revert "add-submodule" This reverts commit e35994b25035ce310a98909658582bff759ee358. * fix submodule * IT BUILDS * Initial commit of prediction_service_impl.cpp * Server builds and runs! * add request id, health and reflection. GRPC is done * enable channelz for monitoring * GRPC unit tests * clang format * add unit tests * Add function tests for GRPC * add grpc to model_zoo_tests * revert update protobuf to 3.7.0 * update submodules * builds but runs some gflags tests which fail * get build working * confine build changes to onnxruntime_server.cmake * update build files * code reveiw comments * Maik's code review comments * update cares version to fix compilation issue * update build to fix c-ares * code review comments * update cgmanifest.json * remove extraneous file * Klein comments. * update ci based on discussions for go dependency * fix tag issue * fix build issues * remove stray submodule * update dockerfile and build script * dynamic linking changes * update build script * code review comments * update dockerfile * update script for mount * code review comments |
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Maik Riechert
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bfda9ca1c1 |
Make sure submodule urls are up-to-date (#1357)
This extends build.py to run git submodule sync --recursive before running git submodule update --init --recursive. This makes sure submodule URLs are up-to-date. |
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Colin Versteeg
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a8ff209ab6 |
Refactor Onnx runtime Server to only use public APIs (#1271)
* 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 |
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Matthieu Darbois
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04d581995d |
Use manylinux2010 image to build linux python wheels (#1282)
* 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 |
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S. Manohar Karlapalem
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8d15ffd8f5 |
Initial commit for OpenVINO Execution Provider (#935)
* Initial commit for OpenVINO Execution Provider OpenVINO Execution Provider provides the interface for ONNX Runtime applications to access Intel's hardware accelerators using Intel's OpenVINO Toolkit. * Fixed bug in GetCapability to disable custom ops Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Added OPENVINO ci pipeline Added new pipeline for openvino provider, made changes to support the docker build and onnxruntime build with openvino. Signed-off-by: Luis Daniel Castellanos <luis.daniel.castellanos@intel.com> * Enabled all unit tests for OpenVINO EP Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Fixed syntax issue in run_docker_build.sh file * Added missing default OPENVINO_VERSION Default value for OPENVINO_VERSION env was missing causing the build to fail * Added install Model Optimizer deps step * Fixed python unit tests and some tests from onnx_backend_test_series Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Fixed indentation bug Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled some of the python backend tests for OpenVINO Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled some model tests Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Remove Duplicate checks for openvino in build.py Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Modified GetCapability for FP16 Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled GPU FP32 tests that are not supported Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Convert modelProto to string and use it in compile Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Pass byte-array input args to MO * Serialized ModelProto passed in-memory to MO ModelOptimizer python module receives the serialized ModelProto in-memory. Uses appropriate ONNX function to load the serialized bytes. * Make Py_Finalize compatible with older python versions Also, remove pFunc unassigned variable possibility. * Fallback if input dims of Matmul is greater than 2 Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * fixup: Device #define syntax * Updated the documentation Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Enable dynamic dim value * removed commented out code * Added Dockerfile for openvino EP Updated instructions on dockerfiles/README.md file Signed-off-by: Luis Daniel Castellanos <luis.daniel.castellanos@intel.com> * Disabled fp16_inception_v1 test Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Code formatting with clang-format Uses style from the .clang-format file in root directory. * fixup: docker tag and build error fixes * Heuristics to automatically detect batching Distributes slices from batch into parallel infer-request objects. * Handle disabled tests in GetCapability Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled average pool and max pool if ceil_mode is 1 Also dilations are not supported if they are greater than 1 Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled Unsqueeze int32 test Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * changes to fix output results bug * Disabled a few C++ unit tests for MYRIAD FP16 Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Manually revert '9fe162bb Enable dynamic dim value' Reverts compile time setting of dynamic shape Reverting manually due to significantly huge auto-revert conflicts. * Fixed unused variable warning Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled Mul test for GPU_FP16 due to accuracy issue Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * VPU documentation update * Disabled inception_v1 for MYRIAD and HDDL *Also disabled few C++ accuracy tests for HDDL Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * updates from upstream * use the new CustomOpApis for I/O interfacing * Pass initializers as subgraph meta-def inputs in GetCapability() Requirement due to API changes introduced with PR# 1019. * Remove obsolete functions * Save indexes of graph inputs from fused_node info Both inputs and initializers are passed as data inputs to the infer function. To identify only inputs among them, save thier index info from fused_node in Compile function. * Documentation changes to enable VPU * Fix VPU related changes in documentation * Fix minor changes in documentation * Fix VPU related changes in documentation * Use Node.In/OutputDefs() to track graph inputs and outputs. Don't use graph_viewer's GetInputs() or GetInputsIncludingInitializers(). * Permit "SAME_UPPER" auto_pad attribute from MaxPool * Disabled fp16_tiny_yolov2 in onnx model tests * Updated documentation to include configuration guides for myriad and hddl Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Use 8 Infer requests only for VAD-R * disable debug prints * Clang-format source files * Updated BUILD.md with OpenVINO R5 links Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled same upper python tests Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Update test exclusion syntax * Change path of install_onnx.sh Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disable tiny_yolov2 in broken tests Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Revert "Change path of install_onnx.sh" This reverts commit ba9db165f3be430f2aff1ef413299ed04637196a. This change is only required for Intel internal CI pipeline until the settings are matched with the upstream's CI pipeline. * Added debug statements for debugging CI error Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Add --build_wheel to linux openvino pipeline Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Added -v option to onnx_test_runner for debugging Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Removed path change patch Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Added -c 1 to onnx_test_runner Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Refactor MO python invocation in separate function Cleans up Model Optimizer python invocation check and conversion logic. Invokes MO only once in GetCapability() and passes the IR strings (xml and bin) to the Compiler as meta-def attributes. * Add comments * code cleanup and comments * Code cleanup for GetCapability Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Removed unnecessary files Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Revert "Added -v option to onnx_test_runner for debugging" This reverts commit d1dd70938a94d648df1a1dbbc2e48d0b97e49ec8. * Revert "Added debug statements for debugging CI error" This reverts commit b86d41afed2aa29c3508155d6f9c8d3a7263cc60. * incorporate Status Code changes * ComputeFunc returns Status::OK() on success * Use test names to disable tests for MYRIAD and VAD-R Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Rename local identifiers from CNNNetwork to OpenVINO network CNNNetwork is an OpenVINO's API class that represents more than just convolutional neural networks (CNNs). Renaming helps to avoid confusion that the API's only support CNN type models. * Added error message if building on windows * Removed duplicate option in Cmake * Removed unnecessary parameters in activation_opt_test Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Refactor Map search and access logic for efficiently and cleanliness. * use C++ style casts * Use os.path.join for python directory path operations * use C++ style casts * EP classes should use onnxruntime namespace * Clean up fixes from PR comments * Don't explicitly shutdown Py interpreter * Remove debug print statements Prints will be re-enabled later with a logging mechanism with debug/verbose printing options. * Decrement ref counts for used pyObjects * Restore build instructions for other compilers Content under the "Using other compilers" section has been accidentally deleted by a previous commit. Restoring back that content from the latest upstream repo. * CMake code cleanup Code clean up, commenting and formatting of CMake code. * Don't pass the unused device_info parameter to OpenVINOGraph ctor. * Add support for multiple I/O data types Adds support for the following tensor data types for graph inputs and outputs: 1) float 2) float16 3) int32 4) int16 5) int8 6) uint16 7) uint8 * cleanup setup.py module list definition * Deduce index of input using tracked input index map Ignores initializers in case they are ordered before inputs. * Removed debug statement in MO code Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * PR feedback * Removed per_sample_tolerance for openvino * Removed unnecessary disabled tests Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Removed debug function Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled tiny_yolo_v2 due to accuracy issues Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Changed the disabled reason for broken tests Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Disabled Reshape with no input Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Python formatting with Autopep8 * Minor fix for MYRIAD devices * Added zero dimension check *Removed setting batch size for the network Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Set the threshold to larger value for MNIST Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Removed setting higher threshold in provider_test_utils Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> * Check for --use_openvino in python wheel setup.py Add openvino modules to the setup script for building the wheel package only for --use_openvino a build option. * Removed nullptr checks for GetNode() Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com> |
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Sreekanth Yalachigere
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24d6b0f5c4 |
MKL-DNN Subgraphs (#1116)
* subgraph with memcpy fix * Linux compile errors fix * Linux compile errors fix * subgraph with memcpy fix * Linux compile errors fix * Linux compile errors fix * memcpy (PR1020) fix implemented * check graph viewer GetNode for nullptr at other plances * documents * Review changes (UseSubgraph simplified) * static_cast<int> removed * static_cast<int> removed 2 * fall back to CPU implementation in GetCapability() * check shape for null. fall back to CPU implementation in GetCapability() * backend data errors fixed * PR review changes * disable Opset10 tests * removed tests from main.cc of test runner. added a check at GetCapability() * backend data and Model-Zoo related fixes * subgraph with memcpy fix * Linux compile errors fix * Linux compile errors fix * subgraph with memcpy fix * Linux compile errors fix * memcpy (PR1020) fix implemented * documents * Review changes (UseSubgraph simplified) * static_cast<int> removed * fall back to CPU implementation in GetCapability() * check shape for null. fall back to CPU implementation in GetCapability() * backend data errors fixed * PR review changes * disable Opset10 tests * removed tests from main.cc of test runner. added a check at GetCapability() * backend data and Model-Zoo related fixes * patch to run tests and models separatly |