Right now we fix the warnings in an ad-hoc way. We run static analysis
in nightly builds, then create work items for the finding it found. Our
CI build pipelines run the same scan but do not break the build. So,
this PR will fix the remaining findings in the CPU EP(including the
training part) and enforce the check. Later on we can continue to expand
the scope.
We still have some warnings left in the JNI part. I will try to address
them later in the next month.
### Description
The existing ROCM profiler has a few shortcomings, which this PR fixes.
### Motivation and Context
The existing ROCM profiler:
1. Is not thread-safe
2. Is not session-aware: i.e., if multiple inference sessions enable
profiling, then events (esp GPU events) get mixed up between the
sessions
3. Has some issues with respect to coding standards.
This PR addresses all of the above by cleanly re-implementing parts of
the ROCM profiler as required.
Attached are 4 profile outputs from a multi-session run of the
StableDiffusion model, as well as a quick-and-dirty script that checks
the profile outputs for the invariants claimed.
[sd_profile_outputs.tar.gz](https://github.com/microsoft/onnxruntime/files/9924608/sd_profile_outputs.tar.gz)
[check_profile_output_wellformedness.zip](https://github.com/microsoft/onnxruntime/files/9924614/check_profile_output_wellformedness.zip)
Co-authored-by: Abhishek Udupa <abhishek.udupa@microsoft.com>
The old runtime optimization format is not readily convertible to the new one without extra information for translating kernel def hashes.
Ignore such saved runtime optimizations and output a warning for now.
### Description
* Add getter/setter to access and update C# OrtEnv log level
* Add C API about updating ort env with custom log level to support the
setter above (Following [pybind
implementation](952c99304a/onnxruntime/python/onnxruntime_pybind_state.cc (L923-L924)))
* Add test case to verify getter & setter
### Motivation and Context
* For C++/Python, the log level can be adjusted via OrtEnv, and this
feature is missing in C# binding
**Description**: Subgraph-level recompute
This PR adds an optional capability trading additional re-computation
for better memory efficiency. Specifically, a pre-defined operator list
used to iterate the Graph to find some subgraphs for recompute, to
reduce some stashed activations whose lifetime across forward and
backward pass.
When training with ORTModule, by default, the graph transformer will
scan the execution graph to find all eligible subgraph to recompute,
along with sizes that can save. An example looks like below.
If we want to enable some of them to recompute, we can define env
variable this way:
`export
ORTMODULE_ENABLE_MEMORY_ALLEVIATION="Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+:1:-1,BiasGelu+:1:-1,BitmaskDropout+Cast+:1:-1,FusedMatMul+:1:-1,Cast+:1:-1,Mul+Add+:1:-1,Mul+Sub+:1:-1"`
```
[1,0]<stderr>:2,022-10-12 14:47:39.302,954,530 [W:onnxruntime:, memory_alleviation.cc:595 PrintSummary]
[1,0]<stderr>:MemoryAlleviation Summary:
[1,0]<stderr>: User config:
[1,0]<stderr>: Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+:1,BiasGelu+:1,BitmaskDropout+Cast+:1,FusedMatMul+:1,Cast+:1,Mul+Add+:1,Mul+Sub+:1
[1,0]<stderr>: =================================
[1,0]<stderr>: Subgraph: BitmaskDropout+
[1,0]<stderr>: AlleviationType: Disabled
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x 1,024 x Frequency:1
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: BiasGelu+
[1,0]<stderr>: AlleviationType: Recompute
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x input_ids_dim1 x 4,096 x Frequency:24
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: Reshape[1,0]<stderr>:+
[1,0]<stderr>: AlleviationType: Disabled
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:labels_dim0 x Frequency:1
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: Unsqueeze+Unsqueeze+Cast+Sub+Mul+Mul+FusedMatMul+Cast+Add+BiasSoftmaxDropout+Cast+
[1,0]<stderr>: AlleviationType: Disabled
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x input_ids_dim1 x Frequency:23
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+
[1,0]<stderr>: AlleviationType: Recompute
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x input_ids_dim1 x Frequency:1
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: Mul+Add+
[1,0]<stderr>: AlleviationType: Recompute
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 1 x Frequency:24
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: FusedMatMul+Cast+Add+Reshape+Cast+
[1,0]<stderr>: AlleviationType: Disabled
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 2 x 4 x Frequency:24
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: Mul+Sub+
[1,0]<stderr>: AlleviationType: Recompute
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 1 x Frequency:24
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: Cast+
[1,0]<stderr>: AlleviationType: Recompute
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:1,024 x 1,024 x Frequency:97
[1,0]<stderr>: PatternShape:3 x 1,024 x Frequency:1
[1,0]<stderr>: PatternShape:8 x 64 x Frequency:24
[1,0]<stderr>: PatternShape:1,024 x 4,096 x Frequency:24
[1,0]<stderr>: PatternShape:4,096 x Frequency:24
[1,0]<stderr>: PatternShape:4,096 x 1,024 x Frequency:24
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: Subgraph: FusedMatMul+
[1,0]<stderr>: AlleviationType: Recompute
[1,0]<stderr>: Patterns:
[1,0]<stderr>: PatternShape:input_ids_dim0 x input_ids_dim1 x 4,096 x Frequency:24
[1,0]<stderr>: --------------------------------
[1,0]<stderr>: =================================
```
"Type config:" whether recompute is enabled by users. 0 - disable, 1-
enable.
"Subgraph" means what kind of subgraph will be recomputed, in this case,
it is a single node "Gelu", and it will be "Recompute".
"Shape && Frequency" means, for this recompute, one tensor of size
(batch size, 500) will be saved because it will be recomputed.
**Baseline**
On a 1P model (DEBERTA V2), sequence length 256, training with 16 A100
GPUs. With latest main branch, we can run batch size 16, and the maximum
batch size < 32. So 16 is usually chosen by data scientists. 65% of 40GB
memory is used during training. The SamplesPerSec=479.2543353561354.
**With this PR**
Gelu is recomputed for saving memory peak, batch size 32 can be run. The
97% of 40GB A100 is used, the SamplesPerSec=562.041593991271 (**1.17X**
of baseline).
**Motivation and Context**
- Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here.
This PR enables ORT to execute graphs captured by TorchDynamo. Major compilation code is in `OrtBackend.compile` in ort_backend.py. `register_backend.py` is for plugging `OrtBackend` into TorchDynamo as a compiler.
- Reverts change to CustomOpApi::GetTensorData introduced by commit 5dae0c477d,
which causes infinite recursion.
- Moves EndsProfilingAllocated to non-const session implementation
(C++ API header).
### Description
<!-- Describe your changes. -->
The Env argument does not need to be mutable to call the underlying C
API. Update the Ort::Session ctor to have a const Env.
All other changes are from clang-format running.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Cleanup
### Description
Detect and report thread creation failure on Windows.
Do not throw out of constructor after the thread is created,
the thread handle is lost and cannot be joined, resulting in a deadlock.
Make setting a thread priority on Linux consistent with windows.
Set thread priority in the thread itself. Log failure properly,
but do not exit the thread.
### Motivation and Context
Address issues https://github.com/microsoft/onnxruntime/issues/13291
And
https://github.com/microsoft/onnxruntime/issues/13285#issuecomment-1278063223
clang-tidy says "Do not implicitly decay an array into a pointer; consider using gsl::array_view or an explicit cast instead"
It is a false positive scattering around all our codebase when using
helper macros. It is becuase for function with 4 char name, say `main`,
the type of __FUNCTION__ and __PRETTY_FUNCTION__ is `char [5]`.
### Description
Deprecate CustomOpApi and refactor dependencies for exception safety and
eliminate memory leaks.
Refactor API classes for clear ownership and semantics.
Introduce `InitProviderOrtApi()`
### Motivation and Context
Make public API better and safer.
Special note about `Ort::Unowned`. The class suffers from the following
problems:
1. It is not able to hold const pointers to the underlying C objects.
This forces users to `const_cast` and circumvent constness of the
returned object. The user is now able to call mutating interfaces on the
object which violates invariants and may be a thread-safety issue. It
also enables to take ownership of the pointer and destroy it
unintentionally (see examples below).
2. The objects that are unowned cannot be copied and that makes coding
inconvenient and at times unsafe.
3. It directly inherits from the type it `unowns`.
All of the above creates great conditions for inadvertent unowned object
mutations and destructions. Consider the following examples of object
slicing, one of them is from a real customer issue and the other one I
accidentally coded myself (and I am supposed to know how this works).
None of the below can be solved by aftermarket patches and can be hard
to diagnose.
#### Example 1 slicing of argument
```cpp
void SlicingOnArgument(Ort::Value& value) {
// This will take possession of the input and if the argument
// is Ort::Unowned<Ort::Value> it would again double free the ptr
// regardless if it was const or not since we cast it away.
Ort::Value output_values[] = {std::move(value)};
}
void main() {
const OrtValue* ptr = nullptr; // some value does not matter
Ort::Unowned<Ort::Value> unowned{const_cast<OrtValue*>(ptr)};
// onowned is destroyed when the call returns.
SlicingOnArgument(unowned);
}
```
#### Example 2 slicing of return value
```cpp
// The return will be sliced to Ort::Value that would own and relase (double free the ptr)
Ort::Value SlicingOnReturn() {
const OrtValue* ptr = nullptr; // some value does not matter
Ort::Unowned<Ort::Value> unowned{const_cast<OrtValue*>(ptr)};
return unowned;
}
```
**Description**: This PR adds Ascend CANN execution provider support.
**Motivation and Context**
- Why is this change required? What problem does it solve?
As the info shown in the issue. CANN is the API layer for Ascend
processor. Add CANN EP can allow user run onnx model on Ascend hardware
via onnxruntime
The detail change:
1. Added CANN EP framework.
2. Added the basic operators to support ResNet and VGG model.
3. Added C/C++、Python API support
- If it fixes an open issue, please link to the issue here.
https://github.com/microsoft/onnxruntime/issues/11477
Author:
lijiawei <lijiawei19@huawei.com>
wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: FFrog <ljw1101.vip@gmail.com>
This changes are to align OV 2022.2 Release with ORT . Changes
CPU FP16 Support, dGPU Support, RHEL Dockerfile, Ubuntu 20 Dockerfile
**Motivation and Context**
- This change is required to ensure ORT-OpenVINO Execution Provider is
aligned with latest changes.
- If it fixes an open issue, please link to the issue here.
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: shamaksx <shamax.kshirsagar@intel.com>
Co-authored-by: pratiksha <pratikshax.bapusaheb.vanse@intel.com>
Co-authored-by: pratiksha <mohsinx.mohammad@intel.com>
Co-authored-by: Sahar Fatima <sfatima.3001@gmail.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: nmaajidk <n.maajid.khan@intel.com>
Co-authored-by: Mateusz Tabaka <mateusz.tabaka@intel.com>
Co-authored-by: intel <intel@iotgecsp-nuc04.iind.intel.com>
# Motivation
Currently, ORT minimal builds use kernel def hashes to map from nodes to
kernels to execute when loading the model. As the kernel def hashes must
be known ahead of time, this works for statically registered kernels.
This works well for the CPU EP.
For this approach to work, the kernel def hashes must also be known at
ORT format model conversion time, which means the EP with statically
registered kernels must also be enabled then. This is not an issue for
the always-available CPU EP. However, we do not want to require that any
EP which statically registers kernels is always available too.
Consequently, we explore another approach to match nodes to kernels that
does not rely on kernel def hashes. An added benefit of this is the
possibility of moving away from kernel def hashes completely, which
would eliminate the maintenance burden of keeping the hashes stable.
# Approach
In a full build, ORT uses some information from the ONNX op schema to
match a node to a kernel. We want to avoid including the ONNX op schema
in a minimal build to reduce binary size. Essentially, we take the
necessary information from the ONNX op schema and make it available in a
minimal build.
We decouple the ONNX op schema from the kernel matching logic. The
kernel matching logic instead relies on per-op information which can
either be obtained from the ONNX op schema or another source.
This per-op information must be available in a minimal build when there
are no ONNX op schemas. We put it in the ORT format model.
Existing uses of kernel def hashes to look up kernels are replaced
with the updated kernel matching logic. We no longer store
kernel def hashes in the ORT format model’s session state and runtime
optimization representations. We no longer keep the logic to
generate and ensure stability of kernel def hashes.
**Description**: Describe your changes.
XNNPACK takes pthreadpool as its internal threadpool implemtation, it
couples calculation and parallelization. Thus it's impossible to
leverage ORT's threadpool (EIGEN/OPENMP based). So we enabled
pthreadpool in XNNPACK EP in this PR.
Case 1: Pthreadpool coexist with ORT-threadpool simply
Expriments setup
hardware:RedMi8A with 8 cores, ARMv7
The two threadpool has the same pool size form 1 to 8.
Two models: mobilenet_v2 and mobilenet_egetppu.
we can see the picture below and draw a conclusion, latency are even
higher from 5 threads or more.
Case 2:
For the reason of performance regression with 5 more threads,
ORT-threads are spinning on CPU and diddn't realease it after
computation finished. It's equivalent of creating 5x2 threads for
parallelization while we have only 8 cpu cores.
So I mannuly disabled spinning after ort-threadpool finished and enabled
it when enter ort-threadpool.
The result is quite normal now.
Case 3:
Even we achieved a reasonable results with disabling spinning, Will
ORT-threadpool still impact performance of pthreadpool?
we have expriment setting up as: Setting ORT-threadpool size
(intra_thread_num) as 1, and only pthreadpool created.
Attention that, almost a third of ops are running by CPU EP. we are
surprisingly find that disabling ort-threadpool is even better in
performance than creating two threadpool.
Case 4:
Use a unified threadpool between CPU ep and XNNPACK ep.
It's the fastest among all. But if we take the similar workload
partition strategy as ORT-threadpool, it could be faster.
**Motivation and Context**
- Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here.
Co-authored-by: Jicheng Wen <jicwen@microsoft.com>
* upgrade emsdk to 3.1.19
* fix build break
* ignore '-Wunused-but-set-variable' in eigen
* add malloc and free in exported functions
* EXPORTED_FUNCTIONS
* Add first pass of rocm kernel profiler
* Clean up rocm_profiler. Format args. Demangle kernel names.
Add Api EventRecords
* Remove debug output
* Temporarily disable profiling unit test 'api record check' for cupti
* Fix compile error for non-gpu builds
* Use common file for demangle and pid/tid. Namespace ThreadUtil. Fix gpu buffer clearing.
* Merge demangle into profiler_common
* Merge demangle into profiler_common part 2
* Style cleanup
* Resolve linking issues via ProviderHost interface
* Demangle cuda kernel names
* Clean up comments
* Fix formatting
* Fix anal retentive formatting
LLVM compiler complains the std::hash<const char*> and suggests std::hash<const void*>. But the intention is to hash the name string instead of the pointer. So use std::hash<std::string> to be explicit.
* Add ability to use ORT format model flatbuffer directly for intiializers by leveraging the TensorProto external data infrastructure.
Requires user to provide ORT format model bytes when creating the session, and set both `session.use_ort_model_bytes_directly` and `session.use_ort_model_bytes_for_initializers` to 1 in SessionOptions config entries (AddSessionConfigEntry in C API).
Add a graph optimization that convert u8s8 matrix multiplication to u8u8 if needed
In x86/64 platforms, specifically SSE4.1, AVX2 and AVX512 CPUs provide better performance computing u8s8 matrix multiplications. Unfortunately, the higher performance comes with value overflow problems, as described in:
https://www.intel.com/content/www/us/en/develop/documentation/onednn-developer-guide-and-reference/top/advanced-topics/nuances-of-int8-computations.html
In this change we added a session option "session.x64quantprecision" (default off). For operators that calls u8s8 matrix multiplications, e.g. QAttention, we convert them to u8u8 when the following conditions are all satisfied:
1. Current CPU is SSE4.1, AVX2 or AVX512 with no VNNI support
2. Session option "session.x64quantprecision" is on.
3. Constant weight tensor contains values outside of [-64, 63] range
Note that when weight tensor is not constant, QDQS8ToU8Transformer should already convert it to u8.
* create op from ep
* read input count from context
* create holder to host nodes
* fix typo
* cast type before comparison
* throw error on API fail
* silence warning from minimal build
* switch to unique_ptr with deleter to host nodes
* fix typo
* fix build err for minimal
* fix build err for minimal
* add UT for conv
* enable test on CUDA
* add comment
* fix typo
* use gsl::span and string view for Node constructor
* Added two APIs - CopyKernelInfo and ReleaseKernelInfo
* pass gsl::span by value
* switch to span<NodeArg* const> to allow for reference to const containers
* fix typo
* fix reduced build err
* fix reduced build err
* refactoring node construction logic
* rename exceptions
* add input and output count as arguments for op creation
* refactor static member
* use ORT_CATCH instead of catch
* cancel try catch
* add static value name map
* format input definition and set err code
* fix comments
* fix typo
* Revert "Revert "Refactor ExecutionFrame and SessionState to reduce memory all… (#11888)"
This reverts commit d2cbae3a04.
* Revert prepacked_weights to avoid indirect inclusion in CUDA and TRT code that breaks the build.
Minor wording update to warning message to clarify that the function style Compile API is deprecated now and will be removed soon.
Also updated some code comments.
* Rework the EP factory creation setup so we're not cut-and-pasting function declarations in multiple places.
Convert append EP for SNPE to be generic, and also use for XNNPACK.
Add XNNPACK to C# API
* Don't need stub for MIGraphX as it's using provider bridge.
* Remove old 'create' functions that aren't applicable now that the EPs are built as separate libraries.
* Only use EPs that require the layout transform if the opset is supported by the layout transformer.
* Update wasm registration of xnnpack.
* C API version 0.001
* fix linker issues
* fixes for save checkpoint api
* plus fixes based on tests
* plus test_runner and other changes
* Plus cosmetic updates
* remove unnecessary headers
* plus some updates
* plus more changes
Co-authored-by: Ashwini Khade <askhade@microsoft.com@orttrainingdev10.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Reserve the first core for the main thread
Currently in "auto affinity" mode the worker threads are affinized to cores 0..(N-1), leaving the very last core for the main thread. This patch preserves core #0 for the main thread, and affinizes the worker threads to cores 1..N.
* Avoid unneeded spin_pause in thread pool's worker threads
Remove unneeded PAUSE instruction (0.1-0.2 usec latency) after a worker thread finds a task to execute.
* MLAS/x86: optimize QLinearConv on hybrid CPUs
Existing 4x task granularity for task partitioning on hybrid CPUs is
not sufficient to compensate the difference of VNNI instructions
throughput
between performance and efficient cores. This patch...
* Increases granularity for QLinearConv by 2x, to have 2x more tasks
with 2x
smaller output count
* Limits QLinearConv task count from above, to avoid output count per
task
getting smaller than kernel's capability
* Remove hardcoded task count for QLineConv as it limited scaling on
16+ cores CPUs
* MLAS/x86: optimize QLinearConv on hybrid CPUs
Existing 4x task granularity for task partitioning on hybrid CPUs is not sufficient to compensate the difference of VNNI instructions
throughput between performance and efficient cores. This patch...
* Increases granularity for QLinearConv by 2x, to have 2x more tasks
with 2x smaller output count
* Limits QLinearConv task count from above, to avoid output count per
task getting smaller than kernel's capability
* Remove hardcoded task count for QLineConv as it limited scaling on
16+ cores CP
* Addressing comments
* combining x86 ARM branches in qlinearconv threaded job partition
* revert first core assignment
Co-authored-by: Saurabh <saurabh.tangri@intel.com>
Co-authored-by: Chen Fu <fuchen@microsoft.com>
* aten op for inference
* fix build error
* more some code to training only
* remove domain from operator name
* move aten_op_executor ext out from ortmodule
* add pipeline
* add exec mode
* fix script
* fix ut script
* fix test pipeline
* failure test
* rollback
* bugfix
* resolve comments
* enable aten for python build only
* fix win build
* use target_compile_definitions
* support io binding
* turn off aten by default
* fix ut
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
Co-authored-by: zhijxu <zhijxu@microsoft.com>
* Rework allocator sharing to work for multiple devices.
* Update SessionState to not use allocator name in matching for consistency with IExecutionProvider. The name doesn't have any clear meaning (e.g. we use the same name for the per-thread allocator in the CUDA EP as the shared allocate there and in the TRT EP).
* NOTE: this means we will have one allocator per OrtMemType+OrtDevice.
* Reverse order when doing allocator setup in SessionState. This will result in the CPU and CUDA EPs allocators being preferred (they are the most configurable), and also means the per-thread CUDA allocator for default GPU memory will be used even when TRT is enabled.
* NOTE: Combined with the change to remove the allocator name from the key this will mean that if CUDA and TRT or ROCM and MIGraphX are both enabled the CUDA/ROCM per-thread allocator will be used to allocate GPU memory.
* Use InsertAllocator instead of TryInsertAllocator. Each EP should be registered once, and we should only enter RegisterAllocator once, so the 'try' should not be required and would indicate an unexpected setup was involved. i.e. better to fail and figure out if we need to support that setup.
* Add some clarifying comments around how replace allocator works.
* Add unit testing for setup where EP has local allocator that may get out of sync with values in the IExecutionProvider base class.
* Fix invalid check of whether data is on CPU to use device info instead of allocator name.
This reverts commit 1f2c926. Because it makes our packaging pipeline crash
Error message:
[ RUN ] QLinearConvTest.Conv3D_S8S8_Depthwise
Test #1: onnxruntime_test_all ...................Subprocess killed***Exception: 838.24 sec
We haven't successfully reproduced the bug on a real ARM64 hardware. Currently we only saw it showed up with qemu. More investigations are on-going.
* Initiate Ort SNPE EP
* fix snpe ep windows build which is caused by the utility method (ToUTF8String) name change on master
* correct the source path for libonnxruntime.so while building for andorid package
* add AdditionalDependencies for amr64
* On MS-Windows, the patchfile must be a text file, i.e. CR-LF must be used as line endings. A file with LF may give the error: "Assertion failed, hunk, file patch.c, line 343," unless the option '--binary' is given.
* fix build failure if snpe is not enabled
* update doc for contrib op
* separate out snpe ep settings to onnxruntime_snpe_provider.cmake
* renaming according review comments
* update according review comments
* Implement XNNPACK support via an EP.
* Layout transform uses the GraphPartitioner infrastructure.
* Node fusion is supported.
* Conv and MaxPool implementations were ported from Changming's PR.
* Added optional mutex in InferenceSession::Run as we only want to allow sequential calls if xnnpack is enabled
* 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>
* draft kernel creation
* setup eager context
* call into kernel in eager mode
* redefine test case
* refact eager context
* add comment
* remove header
* rename argument
* redefine API definition with types
* list outputs as argument
* switch to int to represent length
* fix compile err
* create attribute API
* add test case for topk
* remove bool from c api
* add gru test case
* remove var
* fix compile warnings
* rename status
* fix compile err
* exclude sparse tensor
* fix comments
* fix comments
* fix build err
* rename file and move location
* format code
* move file to session folder
* fix comments
Co-authored-by: Randy <Randy@randysmac.attlocal.net>
This reverts commit 4983d6e5d6. We can't destroy OrtEnv through python's atexit function, because at that time there might be many other ORT python objects alive.
* 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>
Prior to this, certain shape and type errors were surfaced only when
the model was using the latest known op set version.
Providing users an explicit option allows for better testing of code
that produces models, which includes unit tests within this repo and
other repos such as the TF-ONNX and PT-ONNX converters.
Remove the previous behavior which seems quite counter-intuitive:
an otherwise identical model with a later op set version should be treated
identically in this regard.
The option defaults to false to avoid causing errors for users that
rely on the previous permissive behavior.
Turned on the strict enforcement by default in OpTester, which revealed a few
disagreements between ORT and ONNX on what the correct output shape should
be.
Fix shape inference bug in ReduceSumTraining with noop_with_empty_axes=1
which was revealed.
Fix TensorOpTest.Unsqueeze_scalar, which was testing negative axes on an
op set version where the op did not actually support negative axes.
Fixes#9506.
Rework initializer.cc to eliminate code duplication and add type enforcement.
Address review comments. Add literal operators for MLFloat16 abd BFloat16 and tests.
I disabled some tests temporarily. I will move them to a separated executable file in another PR.
In the future, I want to combine onnxruntime::Environment and OrtEnv classes. Now we have 3 env classes, it is too confusing:
1. onnxruntime::Env
2. onnxruntime::Environment
3. OrtEnv
Our python binding uses onnxruntime::Environment, while all other language bindings use OrtEnv. So python doesn't unload EPs but the others do. It's better to make them consistent.
Please note even I added the call, currently the unload function still is a no-op on Linux. So, currently on Windows we must unload the EPs while on Linux we must not do it.
* 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>
* 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>
Add runtime optimization support to ONNX -> ORT format conversion script.
Replace `--optimization_level`, `--use_nnapi`, and `--use_coreml` with a new `--optimization_style` option.
