* Checkpoint API Implementation
* fix build issues
* fix undefined reference for ParseData of type string.
* refinements
* resolve some comments
* expose python api
* make save and load test pass
* some clean up
* make optimizer save/load test pass
* make custom property save/load test pass
* formatting
* fix comments - fix wave - code placement, remove legacy ckpt logic dependency, remove external data support
* fix comment - wave 2 - Remove ParseData/ParseStringData, Use UnpackTensor, Simplify CheckpointProperty usage
* fix comment - wave 3 - rename all api_test namespace to api
* fix comment - wave 4 - load/save trainable/nontrainable param seperately.
* Rename Load/SaveORTCheckpoint
* renaming API && remove CheckpointUntils. api::LoadCheckpoint/SaveCheckpoint is the exposed interfaces.
* revert unnecessary format change for onnxruntime/core/framework/tensorprotoutils.h/cc
* formatting
* re-org the class folders for better dependency managerment
* save_checkpoint accpeting TensorProto as inputs
* More clean up
* clean up the naming
* refactor a bit type constraints on custom property
* fix comment - file read/write && report error when file read/write failed
* extract LoopDir to FilterFilesFromDirectory
* fix build
* Add disentangled attention TRT plugin as contrib op
* update plugin name & remove null character
* update onnx-tensorrt submodule with my beta version
* use suggested plugin name & simpler shape propagation
* update onnx-tensorrt gitsubmodule to temporary fork
* update onnx-tensorrt to temporary commit
* redirect submodule back to latest 8.2-GA release of onnx-tensorrt repo
Co-authored-by: HHH-ComputeLab <haohangh@nvidia.com>
* use the lightweight compile api as default; use dnnl ep for testing
* apply to tensorrt ep
* fix the missing files
* fix build
* fix the copy issue on linux
* migrate migraphx and openvino ep
* fix openvino build break
* fix linux build
* fix unused parameter
* fix coreml build
* use graph view's filtered initializers
* fix openvino break
* fix tvm compile api
* fix tvm / rknpu / vitisai ep build
* add IsInitializedTensor in graph_viewer; fix nuphar build
* use serializer directly as tvm ep is still static lib
* fix the type mismatch
* fix the type mismatch
* fix merge conflict
* add a comment
* fix minimal build
* fix the DML EP's legacy approach
* save type/shape in dnnl IR
* fix linux break
* fix tvm failure
* dnnl ep: move initializer referenced out of dnnl subgraph
* Revert "add IsInitializedTensor in graph_viewer; fix nuphar build"
This reverts commit 1cc3c7f08c16fee4fe3309a67209eb769d479587.
* add IsInitializedTensor to graph viewer
* add the legacy code for nuphar build to temporarily make nuphar build work
* ignore internal test for nuphar
* remove the out of date tests
* keep the legacy API in EP for a while
* turn serializer into a static function
* update comments
* fix tvm build
* Update include/onnxruntime/core/framework/execution_provider.h
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* Update include/onnxruntime/core/framework/execution_provider.h
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* Update onnxruntime/core/framework/execution_provider.cc
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* updatee comments; add warning message for legacy compil call
* add a flag to control out of scope arg in serialization
* fix trt build; improve the test
* resolve merege errors
* fix a typo
Co-authored-by: Cheng Tang <chenta@microsoft.com>
Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* update TVM
* small fixes
* update TVM with new set_input and NDArray API
* use set_input instead of set_one_input
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
* initial fix
* refactor the function handle
* update the implementation
* fix linux build break
* fix training build
* fix minmal build
* fix gradient checker
* deprecate the local function members in graph. host it in model
* fix changming's comments
* fix comments about inlined containers
* fix a missed inlined container
* fix training build
* avoid const for std string_view
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* Add stub implementation of the NNAPI interface so that model builder code can be unit tested on all platforms.
Needed to fix a lot of type mismatch warnings. As these don't occur on Android builds used static_cast for simplicity.
* add api test runner
* add build flag for training_api
* address review comments
* some fixes
* address more comments
* make the build pass by filling in empty implementation
* fix more
* Disable training code in DNNL LayerNorm code
The capability code already does not claim the LayerNorm and
SkipLayerNorm that require more than one output. However,
building with training enabled was causing issues.
The training specific code has been removed even when building with
training enabled.
Signed-off-by: George Nash <george.nash@intel.com>
* Fix for DNNL FusedMatMul op.
The bug was in the transpose code.
Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Use agreed upon memory format type when runnig Pooling Gradient in dnnl ep
The dnnl ep does not currently have a way to pass memory_format information
between the forward pooling primitive to the backward pooling primitive.
This change explicitly sets the memory_format to use match that of Onnxruntime.
For both the forward and backward pooling code. This will prevent using un-matched
memory format that could result in an `unimplemented` error from dnnl ep.
Signed-off-by: George Nash <george.nash@intel.com>
* Update dnnl ep to use OneDNN v2.6
Do not run ReduceInfLogSum on the kDnnlExecutionProvider due to a
calculation bug when doing Log or infinity valuse. The fix for this
issue will be part of the next OneDNN release.
Signed-off-by: George Nash <george.nash@intel.com>
* Update PrintMemory function in dnnl ep
This modification can be used to enable/disable memory printing
for dnnl ep develpers. This is considered a developer only feature
and is disabled by default. It must be enabled and code recompiled
to use.
Even if it is enabled it will not actually print any memory because
the developer needs to take the extra step of spefifying the memory
that will be printed to the screen.
Signed-off-by: George Nash <george.nash@intel.com>
* Update binary ops to run on intel GPU when using dnnl ep
Binary ops (i.e. Add, Div, Mul, and Sub ) was updated to no longer
call GetMemoryAndReshape in the past this would move the memory from
CPU to the GPU. This extra call is no longer needed since it is taken
care of by the GetMemoryInOrtFormat call. Removing the GetMemoryAndReshape
prevented copying the memory to GPU twice.
Signed-off-by: George Nash <george.nash@intel.com>
Co-authored-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
This patch implement bilinear interpolation for Upsample/Resize 4-D input with
the outermost and innermost scale (usually channel of NHWC) as 1. It is
parallelized with output_height * output_width instead of one dimension only.
Besides, I also revert the HandleResize back to the original implementation for
TransposeOptimizerTests.TestResize* tests.
Finally, I add microbenchmark BM_NhwcUpsampleBilinear.
* Remove unnecessary target_include_directories for cpuinfo
Headers already exposed as public by CMake target: 5916273f79/CMakeLists.txt (L213)
* Link to cpuinfo library only if supported
* Enabling ov-ep for 2022.1 Release
->Added ov-ep 2022.1 flow
->Validated CPU Unit tests with OV
Master using onnxruntime_test_all unit
tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix for output mismatch b/w OpenVINO and ONNX
Refer:
https://jira.devtools.intel.com/browse/CVS-60310
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling Adobe ops
->Enable Resize op for iGPU
->Enable Add op for iGPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing irrelevant conditions
->Removing some conditions from
GetCapability() which are now not
required. (Removed conditions for
OV version support less than 2021.2)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable upsample op
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable Adobe proxy-e model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing any extra conditions for Opset13 ops
* Opset13 changes
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Exception handling for devices
* Added comments
* Implement GPU Throttling feature
*Added GPU Throttling feature for iGPU's.
when user enables it as a runtime option,
it helps in reducing overall CPU usage
of the application
*Added changes to exercise this option
using onnxruntime_perf_test application.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Renaming the runtime config option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added the user to video and users group
* Handling_GPU.0_GPU.1
* Handling special conditions
->Handling corner cases for
device_type checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Modification to include new api 2.0 changes in the code
* Added opset13 changes
->Enabled Few ops
->Added Debug info for case 3b in getcapability()
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling ov-ep for 2022.1 Release
->Added ov-ep 2022.1 flow
->Validated CPU Unit tests with OV
Master using onnxruntime_test_all unit
tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix for output mismatch b/w OpenVINO and ONNX
Refer:
https://jira.devtools.intel.com/browse/CVS-60310
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling Adobe ops
->Enable Resize op for iGPU
->Enable Add op for iGPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing irrelevant conditions
->Removing some conditions from
GetCapability() which are now not
required. (Removed conditions for
OV version support less than 2021.2)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable upsample op
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable Adobe proxy-e model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing any extra conditions for Opset13 ops
* Opset13 changes
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Exception handling for devices
* Added comments
* Implement GPU Throttling feature
*Added GPU Throttling feature for iGPU's.
when user enables it as a runtime option,
it helps in reducing overall CPU usage
of the application
*Added changes to exercise this option
using onnxruntime_perf_test application.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Renaming the runtime config option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added the user to video and users group
* Handling_GPU.0_GPU.1
* Handling special conditions
->Handling corner cases for
device_type checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added opset13 changes
->Enabled Few ops
->Added Debug info for case 3b in getcapability()
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Log comments updated
* Changes to enable 2.0 api
* Enabling ov-ep for 2022.1 Release
->Added ov-ep 2022.1 flow
->Validated CPU Unit tests with OV
Master using onnxruntime_test_all unit
tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix for output mismatch b/w OpenVINO and ONNX
Refer:
https://jira.devtools.intel.com/browse/CVS-60310
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling Adobe ops
->Enable Resize op for iGPU
->Enable Add op for iGPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing irrelevant conditions
->Removing some conditions from
GetCapability() which are now not
required. (Removed conditions for
OV version support less than 2021.2)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable upsample op
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable Adobe proxy-e model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing any extra conditions for Opset13 ops
* Opset13 changes
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Exception handling for devices
* Added comments
* Implement GPU Throttling feature
*Added GPU Throttling feature for iGPU's.
when user enables it as a runtime option,
it helps in reducing overall CPU usage
of the application
*Added changes to exercise this option
using onnxruntime_perf_test application.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Renaming the runtime config option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added the user to video and users group
* Handling_GPU.0_GPU.1
* Handling special conditions
->Handling corner cases for
device_type checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added opset13 changes
->Enabled Few ops
->Added Debug info for case 3b in getcapability()
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix build issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes issues
*Fixes compiler warnings c4458 on windows.
*Fixes the bug in device_type check logic
*Adds print info for enable_opencl_throttling
option in onnxruntime_perf_test
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* commit to make openvino_2021.4 compatible
* Fixed IO Buffer Optimization
* Fix output names issue
* Fix 2021.3 branch
* Bug Fix for Multiple inputs/outputs
- Assigns the right output_name and
input_name for the graph when
returned by CompiledModel::inputs()
OV function.
- Also takex care of output mismatch
issue b/w openvino output and onnx
output
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Add comments for the changes made
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* IO Buffer Changes
* Commit for Disabling GPU Throttling for 2021.4
* Updated branch
* Fix windows build
->Fixed windows build in debug mode
->Disabled scatternd3_tensor_int64
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed CPP Unit tests for CPU
-Fixed shrink, MVN, ReduceL2, Maxpool,
upsample, scatter, slice, reshape,
unsqueeze.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed first set of GPU Tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed additional failing tests on GPU
->Added conditions to disable certain ops
under certain conditions
->Disabled certain tests
->Added some op supports for no_dimension
supported
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added Expand op support for CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added condition for squeeze op
->Shape can't have empty axes attribute
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Add support for LessOrEqual op function
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* OV Interface wait for replaced by indefinite wait call
* use names from ONNX model to access OV tensors
This chnage is to use the input/output names
retrieved from original onnx model to access
OV tensors and to check if there's any input
or output names mismatch b/w ONNX naming
and OV naming.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes Myriad unit tests and other issues
->Fixes Myriad CPP unit tests
->Fixes output mismatch issue with models with
sub graph partitioning
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix segfault issue
->Fixed case 3b condition in get_capability()
which was causing the segfault issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed build isuse with ov 2021.4 with I/O buffer
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disables performance counters for I/O Buffer
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed inputs/outputs mismatch for HDDL with 2022.1
Signed-off-by: Mohammad Amir Aqeel <mohammadx.amir.aqeel@intel.com>
* Fix to enable GPU FP16
* Enabled mlperf_ssd_mobilenet_300 model fully on CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added ov version specific dll packaging for nuget
* Fixed conditions for few ops
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Dockerfile updates
* Updated License Info
-Updated the copyrights License Info
-modified FP16 transformations with OV 2022.1
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disabling mlperf_ssd_mobilenet_300 model
->Disabled this model for openvino. The
test is failing in Internal_CI pipelines.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disabling failing python CPU Tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed flake8 python errors
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: hdgx <harinix.d.g@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: mohsinmx <mohsinx.mohammad@intel.com>
Co-authored-by: Mohammad Amir Aqeel <mohammadx.amir.aqeel@intel.com>
ARM a55 micro-architecture (with dot product instructions), similar to a53, is widely used as little cores in big.Little configurations. A55 has a narrower memory load/store hardware, where a 128b load instruction would block the pipeline for 2 whole cycles, during which no other instructions can be executed. On the other hand, a 64b load instruction can be duo issued with many other instructions.
This change adds a Symmetric Quant indirect Conv kernel for a55 micro-architecture, where we replace
ldr q4,[x1],
with
ldr d4,[x1],
ldr x11,[x1],
ins v4.d[1],x11
so that we can try to hide the memory load cycles behind computing cycles in the kernel.
With this new kernel, cartoongan model shows significant perf improvement on Pixel5a little cores (2 threads running on two little cores):
new kernel: 2188.59 ms
old kernel: 2360.61 ms
* improve NonZero
* fix megatron_fp16 optimzier, fix the doc
* multi_tensor_applier
* resolve comment
* fix building warning
* fix build error when enabling training and use tensorrt
* backup debugging information related to debugging a jira ticket
* fixed a bug in checking whether an input can be constand folded
* added more operators that are supported by migraphx
* revert unnecessary changes
* remove unused logger parameter
* rename function to make name style consistent
* backup code changes
* fix review comments
* refactor graph utility functions to add unit tests
* backup additional changes
* fixed a link error in build migraphx_basic_test
* add unit test for some migraphx utility functions
* add more supported ops in migraphx
* rename info to options for TVM EP
* transfer options processing from TVMExecutionProvider to TVMEPOptions
* transfer TVMRunner to separated files
* implement TVMCompiler class
* replace CompileFunc by TVMCompiler object. update TVMRunner. now it does not depend on TvmExecutionProvider
* correct logging of TVM EP options
* RunnerImpl, GERunnerImpl and VMRunnerImpl were implemented
* add prepareComputeInfo method
* remove update_output_shapes flag
* embed all TVM EP dependences to tvm namespace. transfer model compilation from TVMRunner. connect TVMRunnerImpl to TVMRunner
* refactor compileModel method
* small cleaning
* separate TVM EP options data store and processing
* replace TvmTensorShape by InlinedVector with max_size 5
* correct indentation
* update TVM hash
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
* Tweaks to the model utils
* Add handling for a dim_value of -1 when replacing the entire input shape. This occurs in models exported from PaddlePaddle
* make pytorch helpers accessible in package
* make QDQ helpers accessible in package
Add runtime optimization support to ONNX -> ORT format conversion script.
Replace `--optimization_level`, `--use_nnapi`, and `--use_coreml` with a new `--optimization_style` option.
This PR is just for making onnxruntime passing Binskim rules.
Below is how I made it:
git clone absl repo, checkout the version we are using
Then apply our patch file
Make modifications
Regenerate the patch file by "git diff > C:\src\onnxruntime\cmake\patch\xxx.patch"
Then submit the change to our repo
You will need to repeat the steps when you need to advance the absl commit or add more changes to it.
ARM a55 micro-architecture (with dot product instructions), similar to a53, is widely used as little cores in big.Little configurations. A55 has a narrower memory load/store hardware, where a 128b load instruction would block the pipeline for 2 whole cycles, during which no other instructions can be executed. On the other hand, a 64b load instruction can be duo issued with many other instructions.
This change adds a Symmetric QGEMM kernel for a55 micro-architecture, where we replace
ldr q4,[x1],#16
with
ldr d4,[x1],#8
ldr x11,[x1],#8
ins v4.d[1],x11
so that we can try to hide the memory load cycles behind computing cycles in the kernel.
Co-authored-by: Chen Fu <fuchen@microsoft.com>
This code is valid only when -mcpu is set to utilize POWER9 technology
or above. A compatible code for POWER8 was created as well, but it
was not tuned for performance.
* POWER10: QGEMM optimization
This patch makes use of POWER10 MMA feature for QGEMM function.
This optimization includes signed and unsigned cases.Tested and
there are no new failures with gcc11 and clang-14.
* Changes as per review comments
Co-authored-by: Rajalakshmi Srinivasaraghavan <rajis@linux.ibm.com>
* add executor option (vm or graph) and support virtual machine methods
* nullptr check for compile and run methods (see also PR#10211 from microsoft:onnxruntime)
* get output shapes for VM
* remove run_with_benchmark. remove run methods from python api, get it from native side
* get outputs method for VM was implemented
* support multiple input for VM
* update python logging and exception
* small fix
* update tvm with patch for VM API
* update nhwc transformations for TVM EP
* add data alignment check and support set_input_zero_copy for GE in TVM EP
* fix logger name
* return back to apache/tvm with VM fixes instead of local dev branch
* hide customized tvm logger while issue is not resolved. fix tvm warning related to target_host
* flake8 fix
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Work on minimizing memory management calls by
reducing number of allocations and copies.
Replace std::unordered_set to InlinedHashSet
and add usage of InlinedVector.
Employ std::move() to minimize copying and memory allocations.
Remove copying of the const shared data into each of the
PropagateCast transformer instances.
Move inlined_containers.h header to include/common
Adjust AsSpan imlementation for C++ < 17
* add support for bool type
* add TVM EP support for tests
* include TVM EP in python test pool
* fix pylint
* moved technical imports to a separate file
* clean up post build actions & move _ld_preload.py extension to CMake level
* add files for include TVM EP into CI
* implement custom logger for TVM
* replace TVM logging with ONNX RT logging
* update link for TVM EP tutorial
* clean up TVM EP cmake
* add pybind auto enabling for TVM EP
* fix blank spaces
* code review fixes
* replace print with comment
* add list of EP without TVM EP
* enable onnx tests
* disable contrib ops and ml ops
* reuse Dockerfile.ubuntu
* Move install_tvm_test_dependencies.sh out of Docker context dir, update build definition.
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Disable warning about padding for abseil-cpp flat_hash_map.
Disable some warnings from compiling the test proto. This also required removing a line in CMakeList.txt where we move a level 4 warning to level 3. That ends up later on the command line and overrides the `/wd4800`. Couldn't find a way to handle that nicely. As we compile with `/W4` the value of moving 4800 to level 3 in dev mode is unclear so simplest was to remove that. Open to suggestions if there's a better way.
* Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input.
* fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes.
* Add check for invalid type constraints.
* Fix invalid registrations for other kernels.
* Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed.
* Add tests
* Add layout transformer for NNAPI
* plus merge fixes
* plus some more merge fixes
* test fixes
* comments + cleanup
* plus updates
* post merge changes
* enable layout transformer in extended minimal build
* plus more comments
* more tests + fix CI
* plus updates per review
* more updates per review
* fix file name
* fix qdq tests
* plus more updates
* plus updates
* typo fix
* fix qdq selection in 2nd optimization pass
* fix typo
* fix a test
* update dependency structure for layout transformer
* plus updates
* more updates
* plus change
* more updates to fix linker error in minimal build
* remove unnecessary headers
Update QDQ propagation transformer to insert new QDQ nodes instead of moving the existing one. This creates a more consistent `DQ -> op -> Q` pattern for other components to recognize.
Upgrade this transformer to a basic level optimization as it yields a valid ONNX graph.
* expand model tests name
* skip cpu/cuda for trt when running onnxruntime_test_all
* only run trt ep for c++ unit test
* Update CMAKE_CUDA_ARCHITECTURES for T4
* Use new t4 agent pool
* Update YAML for run T4 on Windows
* revert code
* Update CMAKE_CUDA_ARCHITECTURES
* fix wrong value
* Remove cpu/cuda directly in model tests
* add only CMAKE_CUDA_ARCHITECTURES=75
* remove expanding model test name to see difference
* revert code
* Add fallback execution provider for unit test
* Add fallback execution provider for unit test (cont)
* add conditional to add fackback cuda ep
* Reduction op takes much longer time for TRT 8.2, so we test smaller range of inputs
* use M60
* revert code
* revert code
* add comments
* Modify code and add comment
* modify comment
* update comment
* add comment
Adding S8S8 kernels for symmetric quantized indirect conv and depthwise conv.
Perf number with single thread:
Nokia G10 (baseline / new) in ms Pixel 4 (baseline/new) in ms
mobilenet_edgetpu 220 / 213 18.5 / 17.6
cartoongan 8537 / 8521 967 / 928
Co-authored-by: Chen Fu <fuchen@microsoft.com>
* add qdqgroup as input for NodeUnit
* minor update
* hookup nnapi_ep
* minor update
* update compiler setting
* Add a simple UT
* Pipeline change to add build minimal extended with NNAPI for Android
* move GetAllNodeUnits to node_unit.h, add UT for NodeUnits, minor updates
* minor updates
* address CR comments
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
Adding code for symmetric quantized matrix multiplication. Used in quantized convolution, achieving significant perf gain.
TODO, use Symmetric Quantized GEMM in other operators!
TODO address activation buffer overread in custom allocators and tensors supplied by users.
DOT kernel perf test:
Pixel 5a:
Cartoongan 513.539 ms 471.786 ms
Efficient 57.5169 ms 56.4174 ms
Edgetpu 14.6673 ms 13.5959 ms
NEON kernel perf test
Pixel 3a
Cartoongan 1423.53 ms 1069.92 ms
Efficient 114.086 ms 107.968 ms
Edgetpu 39.2632 ms 36.9839 ms
Co-authored-by: Chen Fu <fuchen@microsoft.com>
Add abseil and inlined containers typedefs
Introduce TensorShapeVector for shape building.
Use gsl::span<const T> to make interfaces accept different types of vector like args.
Introduce InineShapeVectorT for shape capacity typed instantiations
Refactor cuda slice along with provider shared interfaces
Refactor Concat, Conv, Pad
Build with Conv Einsum and ConvTranspose refactored.
Remove TesnorShape::GetDimsAsVector()
Refactor SliceIterator and SliceIteratorBase
Refactor broadcast
Refactor Pads for twice as long
Remove memory planner intermediate shapes vector
Refactor orttraining
Fix passing TenshroShapeVector to tests
Remove abseil copy and submodule, use FetchContent_Declare/Fetch
Path with separate command
Make RocmAsyncBuffer accept anything convertible to span. Adjust Linux GPU pipeline.
* clearing map for eager mode backends
* clearing map for eager mode backends manager
* making OrtBackendsManager an extern variable and trying to delete it
* cleaning backends manager when the python interpret exits
* adding ifdef for eager mode code
* disabling warning for pybind state file
* disabling warning for python module file
* running clang auto format and reducing redundancy
* remove new line
* moving declaration to a new header file
* adding the header file for eager mode for python module
* removing source files for eager mode
* add source file for python module in eager mode
* Update orttraining/orttraining/python/orttraining_python_module_eager.h
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Although github works with both, this is more precise.
Having an extension also makes it easy to match with regex, when we want to inject code to reroute traffic to our own git mirror.
In a reduced ops build, some source files get updated. This change moves the updated files into the build directory. This way, it is easier to simultaneously manage different build directories (with possibly different reduced ops configurations) based on a single source directory.
* squashed commit for standalone tvm execution provider
* critical fix for correct python build with stvm ep
* get tuning log file from ep options. It has priority over AUTOTVM_TUNING_LOG
* updates and fixes
* update parsing of stvm provider options
* add support of external data for onnx model
* add conditional dump of subgraphs
* remove unused code
* get input tensor shapes through provider options. get output shapes for fixed input ones by TVM API
* support AUTO_TVM tuning log file inside ORT. Selector for Ansor and Auto_TVM is provider option (tuning_type)
* add fp16
* add functionality of conversion of model layout to NHWC if need. Necessary parameter was added to STVM provider options
* fix license text in header. fix log format
* small fixes
* fix issues from flake8
* remove model proto construction from GetCapability
* reserve memory for vector of DLTensors
* add simple tutorial for STVM EP
* STVM docs
* jroesch/tvm -> apache/tvm
* remove dead code, unneccessary logs and comments
* fix in readme
* improve tutorial notebook
* tvm update
* update STVM_EP.md
* fix default value
* update STVM_EP.md
* some TODOs for the future development
* shorten long lines
* add hyperlink to STVM_EP.md
* fix Linux CI error
* fix error in csharp test
Co-authored-by: Jared Roesch <jroesch@octoml.ai>
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
* update base image from 11.4.0 to 11.4.2
* update Linux TRT GPU pipeline to TRT 8.2
* update onnx-tensorrt to 8.2-GA
* disable failing TensorRT 8.2 tests.
* update pad test.
* fix
* update win trt ci pipeline to trt 8.2
* test run with cuda 11.4 and cudnn 8.2
* increase timeout
* revert
* revert
* update packaging pipelines to use trt 8.2
* fix typo
* update trt gpu perf pipeline to trt 8.2
* increase timeout
* delete deprecated ci-perf-pipeline.yml
* bump timeout
* adjust timeout packaging
Adding a symmetric quantized convolution kernel for ARM64
Note:
Indirect conv performs worse for shallow convs (input channels are small). This is much more so for low end pre-dot CPUs, where only 128 or deeper conv is faster with indirect conv. With DOT-CPUs, 32 deep conv is already faster
Co-authored-by: Chen Fu <fuchen@microsoft.com>
* Add QAttention to DNNL EP
Add QAttention to DNNL EP (limited support and disable for gpu)
update ONEDNN version to 2.4.4
bug fix in getcapability
add memory debug print
Signed-off-by: Wang <zhaoyang.wang@intel.com>
* Address Code Review + MatMulInteger Fix
clean up code and add comments
fix matmulinteger and add fusion rule to enable initialized vector weight zero
points of 0s
update DNNL_TAG to v2.5
Signed-off-by: Wang <zhaoyang.wang@intel.com>
* Linux Compile Fix + rollback ONEDNN to 2.4.4
Signed-off-by: Zhaoyang Wang <zhaoyang.wang@intel.com>
* Fix QAttention Debug build
Signed-off-by: Wang <zhaoyang.wang@intel.com>
* Fix QAttention build if USE_DNNL not specified
Signed-off-by: George Nash <george.nash@intel.com>
Co-authored-by: Wang <zhaoyang.wang@intel.com>
Co-authored-by: MTC <63478620+jeyblu@users.noreply.github.com>
* fix error C4996
* remove wd4996 and fix error C4966
* fix typo
* remove wd4996 for onnx-tensorrt
* remove more /wd for onnx-tensorrt
* gix bug for strncpy_s of (Buffer is too small && 0)
* fix code to remove warning 4244
* fix code to remove warning 4267
* remove /wd4267 /wd4244
* fix bug
* change int to size_t
* using size_t instead of int
* use float instead of double
* Use size_t instead of int
* use size_t instead of int
* use size_t instead of int. Also fix typo
* Changes to ensure openvino build go through in Windows
* Modified Hetero plugin Logic
*Modified Hetero Feature logic. In Hetero,
if the operator to be marked true in getcapability(),
it should be supported by either of the devices
specified with HETERO in the device_type.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* OV updated to 2021.4.2 version
* OV updated to 2021.4.2 version
* Updated OV to 2021.4.2 version, mono download link and dotnet version
* Copying Managed nugets in openvino c# docker file
*Copying Managed nuget to nugets artifacts
directory
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: saharfraza <sfatima.3001@gmail.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
* POWER10: Add optimized dgemm kernel
This patch makes use of POWER10 matrix multiply assist feature and
adds new DGEMM kernel.
* Indentation update
Co-authored-by: Rajalakshmi Srinivasaraghavan <rajis@linux.ibm.com>
* Arm64 Depthwise Convolution 3x3.
* Add 5x5 intrinsic dwqconv for arm64
* rebase to master, remove no-need logic after arm64 convsym enabled.
* Some more adjustment on the instrunction pipeling.
* Add specific test cases.
* Fix test dimension too small.
* Fix build warning as error on some CI.
* better format, etc.
* Added checks for Hetero/Multi
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Remote Context Plugin
* changes for IO Buffer plugin
* erronous couts added
* erronous entry rectified
* Set the Openvino OP Buffer also as output
* Enable AUTO plugin in OpenVINO EP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Remote Context Plugin
* changes for IO Buffer plugin
* erronous couts added
* erronous entry rectified
* Added checks for Hetero/Multi
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Set the Openvino OP Buffer also as output
* Enable AUTO plugin in OpenVINO EP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Please commit error message and rectification of param.context
* Alignment fixed
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Changed the string to OpenVINO_GPU
* hanged OpenVINO to to OpenVINO_CPU
* Onnxruntime updated API for memory location
* Removing Duplicate LOG Error
* Tensor.h removed DeviceType function. Updated comment
* API Comments updated
* Removing changes to Provider Indo
* Erronous commit
* Removing Extra logs
* Merge CMAKE
* Not copy from a local location
* Duplicate Entry
* Remove extra line
Co-authored-by: MaajidKhan <n.maajidkhan@gmail.com>
Adding ARM64 depthwise convolution kernel for symmetric quantization
Motivation and Context
Two improvements against current kernel code :
1. Signed int8 based instructions, no need to extend from 8b to 16b before multiplication.
2. Unrolled loop with manual software pipelining
Co-authored-by: Chen Fu <fuchen@microsoft.com>
* Only serialize runtime optimization records container if non-empty.
* Remove runtime optimizations from onnxruntime/core/flatbuffers/schema/README.md as it's not completely implemented yet.
* Disable partial runtime optimization implementation by default.
ORT format model runtime optimization implementation is in progress.
This change adds a build.py option to disable the partial runtime optimization implementation, adds CI builds to test it, and disables runtime optimizations in mobile package builds.
* explicit link with libtorch instead of use cmake var to avoid introduce mkl dependency
* use find_lib to get libtorch lib name
* temp fix
* add missing libraries
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* libonnxruntime_providers_rocm.so and libonnxruntime_providers_shared.so are not included in python package.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Add Xamarin support to the ORT nuget packages.
- Update C# code to support Xamarin builds for iOS and Android
- refactor some things to split out common code
- include iOS and Android ORT native shared library in native nuget package
* POWER: Add Dgemm kernel for POWER processor
This patch adds new dgemm kernel specific to POWER processor.
* POWER: Restrict new functions to VSX in header
* Remove warning check in header
* POWER: Dgemm Adjust indentation
Fixing indentation based on review comments.
Co-authored-by: Rajalakshmi Srinivasaraghavan <rajis@linux.ibm.com>
* optimize python overhead of _post_amp_backward
* overwrite apex amp's zero_grad for faster implementation
* move unscale_fp16_grads_into_fp32_grads into C++ impl
* improve the efficiency furthur, reducing 3.5ms to 1.7ms for unilm.
* unilm 1.7ms to 338us: 1). optimize python list <==> std::vector copy, 2). launch the kernels as long as num_elem reach thresh hold. This help reduce the CUDA idel time.
* refine the logic a bit after validating
Co-authored-by: Baiju Meswani <bmeswani@microsoft.com>
Add kernels for QLinearConv with symmetric quantized filter, e.g., filter type is int8 and zero point of filter is 0. This PR includes kernels for avx2, avxvnni, avx512 and avx 512 vnni. Will adds kernels for ARM64 in following PR.
Kernels uses direct input buffer directly for pointwise, and in-direct buffer for depthwise and non-group conv.
The advantages of those new kernels are:
no need to compute the sum of each pixel output image, and sum/offset of filter can be combined with bias.
with in-direct buffer, im2col returns an array of buffer pointers instead of memcpy'ing the original data. This saves memcpy time and reduces the size of the intermediate buffer needed to hold the im2col transform. In the future, will compute im2col ahead of time for input with fixed input size.
* re-hipify all rocm EP sources
* fix all other files affected by re-hipify
* add cuda_provider_factory.h to amd_hipify.py
* do not use cudnn_conv_algo_search in ROCm EP, missing reduce min registration
* Fix ReduceConsts template specialization introduced in #9101.
Fixes the error when building for ROCm 4.3.1:
error: too many template headers for onnxruntime::rocm::ReduceConsts<__half>::One (should be 0)
* fix flake8 error in amd_hipify.py
* speed up hipify with concurrent.futures
* flake8 fix in amd_hipify.py
* removing warnings which are causing errors from torch and changing flags for Windows
* adding MKL library resolution and comments
* cleaning up the code
* fixing onnxruntime_python file for windows build
* fix the include order to aovid the python_d.lib issue on win debug build
* changes for warnings, typos and other comments
* merge conflict
* adding fix for mkl library error
* Revert "adding fix for mkl library error"
This reverts commit 73b87c73c2.
* fix for dll path for windows
* typo for dll path
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* model caching changes for 2021.4
Signed-off-by: Your Name <you@example.com>
* changed the ov version check
* Minor changes added
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added support for external data format
Starting from OpenVINO 2021.4 version, OpenVINO-EP
will support onnx models with Weights saved in external
file location.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Introduced Hetero/Multi options for perf_test
Enabled to use HETERO/MULTI device feature from
OpenVINO-EP using the onnxruntime_perf_test tool.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* cleaned up CMake code for older OV version support
OV 2020.3 is now longer supported by OpenVINO-EP.
This check is not required now.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Add option to disable graph partitioning
Added a option to diable graph partitioning
during build time for OpenVINO-EP.
with this option, when the model is not fully
supported on OpenVINO-EP, the model fully fall
backs to default CPU EP (MLAS).
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Changed the flag for diabling graph partitioning
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes the flake8 check error
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes for disable graph partition option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed flake8 indentation error
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: Your Name <you@example.com>
* implement cuda provider
* define profiler common
* call start after register
* add memcpy event
* add cuda correlation
* format code
* add cupti to test path
* switch to CUpti_ActivityKernel3
* reset cupti path
* fix test case
* fix trt pipeline
* add namespace
* format code
* exclude training from testing
* remove mutex
* make work for both rocm 4.2 and rocm 4.3.1
* fix rocm 4.3.1 docker image reference
* fix CUDA_VERSION to ROCM_VERSION
* fix ReduceConsts conflict def
* add ifdef to miopen_common.h as well
* trailing ws
* Include pytorch_export_contrib_ops in inference builds
Rename / move it from tools/python/register_custom_ops_pytorch_exporter
to onnxruntime/python/tools/pytorch_export_contrib_ops.
Rationale for inclusion in inference builds:
This code is potentially useful for anyone using ORT, not just training.
Rationale for new name:
"Contrib op" is the nomenclature used within ORT to refer to the set of
ops that are not in the standard op set but are included by default with
ORT. This is more specific than "custom op", which is what the PyTorch
exporter uses to refer to any non-standard op.
Step 1 of addressing #8818. After this is merged I will update the docs.
* Enable test_pytorch_export_contrib_ops.py in CI
Fixes AB#1342330
* Use PROTOBUF_LIB instead of protobuf::libprotbuf
* Moved setdlopenflags to _pybind_state.py
* Copy the generated _pybind_state.py to required location for Windows.
* Add command to skip tests
* Remove support for OV_2021.3_LTS and ov_2021.1
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removed request_id parameter from all references
request_id parameter was being used with ov_2020.3
release. Starting from 2020.4 OV release, input_name
paramater is being used instead to get the
KernelContext_GetInput.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling CI Logs in the branch
* CI Commits to enable logs
* Enable CI Print
* Added Imagescaler op to the supported op's list
Fixes test_tiny_yolo_V2 opset 8 model to support
fully on OV-EP. This model is the older variation
of tiny_yolo_v2 model which has Imagescaler op.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added ops to fully support yolov3 model
-Added changes to support yolov3 opset 10 model
fully on CPU_FP32.
-This also increases the operator coverage for GPU
hardware. There by enabling yolov3 model on GPU
with fewer subgraphs.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling tiny_yolov3 model fully on CPU
->Enabled tiny_yolov3 model fully on CPU.
-> Also reduces the number of subgraphs
to infer this model on GPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Adding GatherND op support for CPU and GPU
->This enables yolov3_pytorch model to work
with fewer subgraphs on CPU and GPU Devices.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes Albert model for ISV customer
ConvTranspose op was getting rejected
due to a condition. Fixed it.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disabling this 4 cpp tests for openvino-ep
These unit tests are failing with special conditions
for conv_transpose op with output_shape attribute.
so disabling them for now.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Docker file changes for 2021.4-v3.1
* Remvoing duplicate code
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* ReduceMax No dimension supported
* Fixes failing protobuf issue for docker
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Excluding openvinoep type for convtranpose test
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disabled 2 Failing convtranspose tests with TensorRT EP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
* Expose symbols in onnx and protobuf namespaces in python when building with --enable_external_custom_op_schemas
* Add external onnx and protobuf files to wheel
* Added an example to demonstrate external custom ops use-case
* Added a Linux build pipeline to test external custom ops
* Enable selecting custom ops in onnxruntime-extensions.
* Move cmake_helper.py.
* Remove over-indented spaces.
* Add doc.
* Remove onnxruntime-extensions from git submodules, and user should pass path of onnxruntime-extensions for build.
* Modify doc.
* Remove argument --enable_onnxruntime_extensions and use --onnxruntime_extensions_path.
* Fix build error.
* Fix build error.
* Use onnxruntime_extensions_path.
* support both submodule and external source folders
* refinement
* Update cgmanifest.json
* Support building onnxruntime-extensions from either git submodule or pre-pulled path.
* Update doc.
* more standard name
* update docs
* add the copyright header
Co-authored-by: Zuwei Zhao <zuzhao@microsoft.com>
Co-authored-by: Wenbing Li <wenbingl@outlook.com>
Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com>
* seperate the training python module; share the execution proivder instance
* fix build break
* fix cuda test crash; reorg the python module code base
* se correct env
* use provider customized hash func
* fixbuild break
* fix rocm break
* use const ref in argument
* rename the file
* move hash func to trainiing module
* Revert "Cleanup C# bindings to add EP (#8810)"
This reverts commit b21ea00020.
* Add back in a minimal set of changes.
Provide stubs in for a limited set of things
- things called from C# using a static lib of ORT built for mac/ios
- things in OrtApis that are not included in the build by default
- things in OrtApis that are excluded in a minimal build
* Cleanup order or EPs in test
* Fix unused function in ROCM build
Add cmake parameter and #ifdefs to allow for disabling sparse tensor support. This comes with a significant binary size cost so we want to be able to exclude it in a minimal build.
Fix C# add EP bindings.
Add stubs to ORT so that if EP is not included in the build we return a graceful error message.
Move declaration of stubs into C API and out for EP so they're in one place and are easier to use (no extra header required in the C/C++ world and consistent with the CUDA EP setup).
Fix inconsistency in ROCM EP.
Cleanup a few other things.
