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.
Fix CUDA 10.2 compile error due to inlined_containers.h inclusion
into a common CUDA header.
Use NumberOfNodes() to reserve space in a hash table
Prefer separate call to reserve() rather than passing in the
hash table constructor. They have somewhat different meaning.
Add an optimizer that can remove leftover Q->DQ pairs. Depending on the model this may help with performance and/or improve accuracy. Optional as it could make things worse so user needs to be aware of this and test what works best for their scenario. Enable with SessionOptions config param `session.enable_quant_qdq_cleanup`
Hide Inlined Hash set and maps guts behind template forward declarations.
Currently CUDA 10.2 compiler can not compile abseil but provider interfaces
use those types in their signatures. InlinedVector seems to be fine.
Introduce core/common/inlined_containers_fwd.h header
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 restrictions for hybrid cpus
* add unit test to mock hybrid cpu
* attach hybrid flag
* add mocking interface to CpuInfo
* make is_hybrid
* make mock function const
* add force_hybrid for thread pool
* remove header
* 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
Add abseil cgmanifest declaration. Update coding standards for InlinedContainers
Adjust coding guidelines. Add default N calculation for InlinedVector<T, N> for general use.
Rename T from InlinedShapeVectorT. Fix Eager build
Add LLVM Copyright with modified derived code notice.
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.
* add new field constant_initializers in metadef and remove constant initializers from trt node inputs
* remove redundancy
* use GetConstantInitializer() to get constant initializers
* add ORT_ENFORCE check
Co-authored-by: Ubuntu <azureuser@orteplinuxdev.bxgbzpva45kedp3rhbsbit4phb.jx.internal.cloudapp.net>
* 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>
* 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>
* schema change
* cc channges
* remove temp debug code
* Adding fbs namespace to session_state_flatbuffers_utils.h
* Add fbs namepsace to all ort format utils
* Construct valid graphs for ONNX checker for IR version < 4.
Previously the constructed graph was not guaranteed to have its
initializers be a subset of its inputs, which is required for IR
version < 4. This resulted in spurious failures.
Fixes#9663
Add support for saving graph runtime optimizations in an ORT format model. The idea is to allow some optimizations to be "replayed" at runtime in a minimal build. The replaying part will be in a future change.
* 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
* Remove unused NodeArgs
* Handle case where a node arg from an initializer from initializer_names_to_preserve
* Fix CI failure
* update test
* Fix outer scope node args failure
* Use NodeArg* as the key of the std::set instead of string
* Minor updates
* 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
Add documentation for these C API functions:
RunOptionsGetRunLogSeverityLevel
RunOptionsGetRunLogVerbosityLevel
RunOptionsGetRunTag
RunOptionsSetRunLogSeverityLevel
RunOptionsSetRunLogVerbosityLevel
RunOptionsSetRunTag
Update some existing documentation.
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* draft - enable model local function
* enable model local functions in ORT
* update to latest rel onnx commit
* plus tests
* plus more updates
* plus updates
* test updates
* Fix for nested functions + shape inference
* plus bug fix and updates per review
* plus fixes per review
* plus test updates
* plus updates per review
* plus fixes
* fix a test
* support register external ep lib inforation; make eager mode share the same ep pools with training workloads
* fix inference code
* fix build break
* fix the message
* 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.
Add IsSparseTensor
Add CreateSparseTensor
Add utilities and test fully sparse instantiation
Fully sparse blocksparse
Add test and docs for fully sparse tensor instantiation
Rework creation API
Use API
Non string API
Retrofit of existing String API
Add tests
Add documentation
Address build issues (Winml pending)
Add inference test
Bump binary size
Add ifdef DISABLE CONTRIB
* Do not copy the model_data when session is started by CreateSessionFromArray
* Add config option for disabling copy model bytes
* Add one additional test
* Address CR comments
SparseTensor support
Implement Builder pattern
Fix support for 1-D and 2-D COO indices
Implement and test CSR support.
Handle shape inference for SparseTensors
Implement conversion for COO, CSR and tests.
Address the case where constant sparse initializer is the output.
Implement test infra for SparseTensors
Implement SparseDenseMatMul for Csr and COO and tested it.
Add hash for SparseToDenseMatMul
Finish shared provider refactor
Refactor GetOrCreate to Create
Working on py interface
Expose OrtDevice and use it in allocate_numpy
Adjust Sparse interfaces, add support for string SparseTensor. Add tests.
Add and test to_cuda()
Add accessors to format specific indices
Test values and indices views, read-only flag, after GC access
Add sparse related methods to OrtValue
Re-work SparseTensor wrapper, add OrtValue methods
Rework numpy_array_to_cuda/to_cpu
Add run_with_ort_values
Add models and test sparse_mat_mul with run_with_ort_values
Refactor sparse tensor to use a single buffer
Ifdef x86 Eigen CSR sparse matmul implementation
Exclude broken test, check for string type when copying cross device
Split pybind schema, regenerate docs, add exclusion
Conditionally exclude schema module
Update docs fix cuda build
Add test to a filter and renerate JS docs
Add conversion and test string support for sparse tensors
Exclude conversion utils from minimal build
Add CUDA Memcpy and adjust provider interfaces
* Add helper to check if node provides a graph output. The current approach unnecessarily creates a vector when most of the optimizers only care about a true/false response.
* Undo accidental change
* Fix a couple of issues due to copying from larger set of changes.
Switched the code to C++17. To build ONNX Runtime on old distros like CentOS 7, you need to install a newer GCC from additionary repos. If you build onnxruntime with the newer GCC, typically the result binary can't be distributed to other places because it depends on the new GCC's runtime libraries, something that the stock OS doesn't have. But on RHEL/CentOS, it can be better. We use Red Hat devtoolset 8/9/10 with CentOS7 building our code. The new library features(like std::filesystem) that not exists in the old C++ runtime will be statically linked into the applications with some restrictions:
1. GCC has dual ABI, but we can only use the old one. It means std::string is still copy-on-write and std::list::size() is still O(n). Also, if you build onnxruntime on CentOS 7 and link it with some binaries that were built on CentOS 8 or Ubuntu with the new ABI and export C++ symbols directly(instead of using a C API), the it won't work.
2. We still can't use std::optional. It is a limitation coming from macOS. We will solve it when we got macOS 11 build machines. It won't be too long.
3. Please avoid to use C++17 in CUDA files(*.cu). Also, the *.h files that they include(like core/framework/float16.h). This is Because CUDA 10.2 doesn't support C++17. You are welcome to use the new features in any *.cc files.
* prepare for C# to configure provider options
* add c# code
* revert modification
* Add update provider info configuration in trt ep side
* fix bugs
* fix bug for compiler error C2259
* Add c# test
* fix bug
* fix bug
* Properly deal with string
* Add c# api for accepting trt provider options
* fix bug
* Modify C# test
* add shared lib test
* Add get provider options functionality
* clean up
* clean up
* fix bug
* fix bugs for CI
* Fix bugs for CI and documentation
* Move TRT EP provider options related functions out of C API
* revert
* fix bug
* refactor
* add check for provider options string
* code refactor
* fix CI bug
* Fix CI bugs
* clean up
* fix bug
* Fix bug for Post Analysis
* fix accidental bug
* Add API_IMPL_BEGIN/API_IMPL_END
* clean up
* code refactor
* code refactor
* fix CI fail
* fix bug
* use string append
* Change the code to better handle strncpy and string append
* Output error message to android log instead of stderr
* Address CR comments, move macro to a helper function
* Address CR comments
* Fix ort minimal build break
* Update Vitis-AI EP support multiple DPU targets & specifically arm64 dpuczdx8g target
* Fix Vitis AI docker and default PyXIR versions
Co-authored-by: Jorn Tuyls <jornt@xilinx.com>
Co-authored-by: Jorn Tuyls <jornt.tuyls@gmail.com>
* First iteration of making cuda a shared provider.
Separated out shared OpKernel change, so doing this to merge with that change.
* More cuda shared library refactoring
* More cuda shared library refactoring
* More build options tested, converted the training ops over.
* Fix merge breaks
* Fix submodules
* Fix submodules
* Fix submodules
* Fix python
* Fix compile errors
* Duplicate symbol fix
* Test fix for ROCM provider
* Another ROCM test workaround
* ROCM Build Test
* ROCM build fix
* ROCM
* ROCM
* ROCM
* ROCM
* ROCM
* ROCM test
* Reduce header dependencies
* Remove redundant namespace
* Test fix for linux
* Fix linux build
* Fix Eigen build error
* Fix unused parameter warning
* Test link error
* Another linker test
* Linker test
* Linker test
* Another test
* Another build test
* Fix linux link error
* Build test
* Fix control flow ops to use common base class with core code
* Remove extra qualifiers
* Fix template syntax for linux
* Fix cuda memory leak
* Fix pybind
* Test disabling cast
* Cleanup
* Restore cuda in test
* Remove more header dependencies
* Test not adding cuda provider to session
* Make GetProviderInfo_CUDA throw
* No-op cuda provider creation
* Fix some setup issues
* Fix memory cleanup on unload
* Diagnostics
* Don't unload library
* Add diagnostics
* Fix deleting registry at right time.
* Test disabling profiler
* Fix merge break
* Revert profiler change
* Move unloading of shared providers into Environment
* Free more global allocations before library unloads
* Add more diagnostics
* Move unloading back to the OrtEnv as there are multiple Environments created during a session.
Remove some library dependencies for tests.
* Fix more cmake files
* ERROR -> WARNING
* Fix python shutdown
* Test not using dml in pipeline
* Change python version and disable dml
* Update python version
* Test adding unload method for shared providers
* Disable DLL test
* Python test
* Revert "Python test"
This reverts commit c7ec2cfe98.
* Revert "Disable DLL test"
This reverts commit e901cb93aa.
* Revert "Test adding unload method for shared providers"
This reverts commit c427b78799.
* Point to RyanWinGPU
* Revert python version
* Fix id_to_allocator_map
* Another python exit test
* Remove extra debug messages
Try a more clean python shutdown through DllMain
* Revert DllMain idea, it didn't work
* Merge conflicts
* Fix merge with master issues.
* Comments
* Undo edit to file
* Cleanup + new training ops
* Revert yml changes
* Fix another merge error
* ROCM fix
* ROCM fix v2
* Put back Linux hack, it is necessary
* Stupid fixes
* Fix submodule out of sync
* ROCM fix 3
* ROCM 4
* Test java fix
* Fix typos
* Java test on my VM
* Fix build error
* Spotless fix
* Leave temp file around to load properly
* Fix cleanup on exit
* Fix break
* Java comments
* Remove LongformerAttentionBase workaround
* Spotless fix
* Switch yml back to regular build pool
* Revert "Switch yml back to regular build pool"
This reverts commit be35fc2a5a.
* Code review feedback
* Fix errors due to merge
* Spotless fix
* Fix minimal build
* Java fix for non cuda case
* Java fix for CPU build
* Fix Nuphar?
* Fix nuphar 2
* Fix formatting
* Revert "Remove LongformerAttentionBase workaround"
This reverts commit 648679b370.
* Training fix
* Another java fix
* Formatting
* Formatting
* For orttraining
* Last orttraining build fix...
* training fixes
* Fix test provider error
* Missing pass command
* Removed in wrong spot
* Python typo
* Python typos
* Python crash on exit, possibly due to unloading of libraries.
* Remove test_execution_provider from training build
Only enable python atexit on windows
Remove assert on provider library exit
* Still can't unload providers in python, alas.
* Disable Nvtx temporarily
* MPI Kernels for Training
* MPI Kernels part 2
* Patch through INcclService
* Oops, wrong CMakeLists
* Missing namespace
* Fix missing ()
* Move INcclService::GetInstance around to link nicer
* Missing }
* Missing MPI libraries for Cuda
* Add extra GetType functions used by MPI
* Missing Nccl library
* Remove LOGS statements as a test
* Add in a couple more missing GetType methods
* Update comments
* Missed a logging reference in mpi_context.h
* Convert aten_op to shared (due to marge with master)
* Test moving DistributedRunContext instance into shared provider layer
(with purpose error to verify it's being built properly)
* Test passed, now with fix
* Missing static
* Oops, scope DistributedRunContext to just NCCL
* Merge related issues and code review feedback.
* Merge error
* Bump to rel-1.9.1 (#7684)
* Formatting
* Code review feedback for Java build on non Windows
* Remove cupti library dependency from core library
* Test Java pipeline fix
* Linux build fix
* Revert "Linux build fix"
This reverts commit a73a811516.
* Revert "Remove cupti library dependency from core library"
This reverts commit 6a889ee8bf.
* Packaging pipeline fixes to copy cuda shared provider for tensorrt & standard packages
* Add cuda to Tensorrt nuget package
* onnxruntime_common still has a cuda header dependency
Co-authored-by: ashbhandare <ash.bhandare@gmail.com>
* Merge set custom allocator to master
* Add documentation for the new API.
Reset global env in testCustomArenaAllocator so won't have a registered allocator of type arena (from previous test)
* Add a session option config that will allow to disable loading model with initializers that have an external data (+test it).
* Add the model used for the test and its external initializers data
* Change the session config option that disable external initializers to a build option.
* Addressing PR comments
* Moved GraphTransformerConfiguration to a separate file and added strategy option to PropagateCastOps transformation.
* Added testing both FloodFill and InsertAndReduce stratigies for cast propagation.
* Added AddConsumer and RemoveConsumer functions to in graph.h for efficient graph editing.
* Added PropagateCastOps code documentation
* Added GraphTransformationConfiguration class hierarchy information
* Added RemoveInputOutputUpDownCasts
Fix an issue where a log message got skipped.
A log call like this:
```
LOGS(...) << "message";
```
expands to something like this:
```
if (<output enabled>)
logging::Capture(...).Stream() << "message";
```
This if statement without brackets is handy for logging arbitrary arguments with the `<<` operator. However, it has other drawbacks like possibly associating with a subsequent `else`.
```
if (cond)
LOGS(...) << "a";
else
<do something> // not run when !cond
// equivalently:
if (cond)
if (<output enabled>)
logging::Capture(...).Stream() << "a";
else
<do something> // not run when !cond
```
Updated the logging macros to handle this case by replacing `if (<enabled>) logging::Capture(...).Stream()` with `if (!<enabled>) {} else logging::Capture(...).Stream()`.
Thanks @tlh20 for the idea for the fix!
[ PR previously merged as https://github.com//pull/7372, then reverted pending investigation of lost-wake-up issue seen with ParallelExecutor. Issue was a missing test for new work pushed to thread concurrent with a worker blocking. Change from 7372 is the addition of: https://github.com/microsoft/onnxruntime/blob/tiharr/dev-sticky-4/include/onnxruntime/core/platform/EigenNonBlockingThreadPool.h#L1473-L1492 ]
Description: This change updates the heuristics used when a thread selects which worker threads to push work to on entering a parallel loop. Previously, worker threads would maintain a best-effort bitmap of "good worker hints" indicating the threads that were likely to be spinning waiting for work. This change uses a simpler heuristic where a thread records which workers ran its previous loop, and then re-submits its next loop to those same workers. The aim is to retain affinity between a thread and a set of workers, and to avoid maintaining the "good worker hints" bitmaps.
Motivation and Context: Profiling suggested that maintaining the "good worker hints" was taking unexpected time, particularly on NUMA systems. In addition, when running many concurrent workloads, the hints did not provide a way to help retain locality of workers and hence data in caches. Testing to confirm no regressions on microbenchmark (./build/Linux/Release/onnxruntime_benchmark --benchmark_filter=BM_ThreadPoolParallelFor) and on Linux mobilenet_v1_1.0_224.onnx, comparing p50 and p99 with vs without this change:
1 concurrent:
p50 0.0172s vs 0.0181s
p99 0.0204s vs 0.0216s
2 concurrent:
p50 0.0172s vs 0.0181s
p99 0.0213s vs 0.0221s
* Check whether nvcc supports -Wstrict-aliasing before adding the compiler flag in CMakeList.txt.
* Removed reinterpret_cast to not cause strict aliasing violation errors or require -Wno-strict-aliasing when it is not available.
* initial draft for kernel invoke api
* initial implementation of kernel invoker
* [eager] fix build on Mac
* [eager] increment input name in kernel invoker
* temp fix for type in eager mode
* use global default log manager
* rollback the previous commit since it break linux build
* Revert "rollback the previous commit since it break linux build"
This reverts commit 58c2c3423a.
* Eager Mode: fix linking on macOS
* optimizer_execution_frame: ignore unused lambda capture (model_path)
* fix link issue
* ORTInvoker: set correct input argument tensor element proto types
Do not set a type proto on output arguments to allow ORT to deduce them
* ORTInvoker: create only one logging manager
* Minor fix to set execution provider type correctly. (#7000)
Co-authored-by: Chandru Ramakrishnan <chandru-r@github.com>
* training fix
* support config output ml values in frame, so we can use it to implement inplace update
* Fix range loop error while building. (#7087)
Co-authored-by: Chandru Ramakrishnan <chandru-r@github.com>
* Conditionally link with nsync_cpp if not windows. (#7151)
Co-authored-by: Chandru Ramakrishnan <chandru-r@github.com>
* Fixed initialization order in ORT kernel invoker (#7342)
* Updated constructor of ort_kernel_invoker to take a logger.
* Changed linking order.
* Updated test.
* add inplace ut
* add build option
* Update include/onnxruntime/core/eager/ort_kernel_invoker.h
Co-authored-by: Derek Murray <Derek.Murray@microsoft.com>
* resolve comments in pr
* fix build break;merge from master
* fix build break
Co-authored-by: Cheng Tang <chenta@microsoft.com>
Co-authored-by: Aaron Bockover <abock@microsoft.com>
Co-authored-by: Chandru Ramakrishnan <41447659+chandru-r@users.noreply.github.com>
Co-authored-by: Chandru Ramakrishnan <chandru-r@github.com>
Co-authored-by: Derek Murray <Derek.Murray@microsoft.com>
* wait for dispatch done in RunParallelSection
* pass worker_fn by value
* cancel move
* only move work_fn when it is lastly referred
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
* add async dispatch
* minor renamings
* build py38
* restore yml
* fix sync up issue between dispatch thread and main
* fix comments
* refactor SummonWorker and rename to RunInParallelInternal
* Enabling save/Load blob feature for OpenVINO-EP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes to enhance save/load feature
->This feature applies only for MYRIAD device target
->cleaned up the code and added error checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabled the feature only for MyriadX and only for Linux
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed compilation issues on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes to fix const subgraph issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed issues on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added changes for the feature
-> Removed default location dir dump using cmake
-> Enabled saving blob dumps at the executable path
by default
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Made save/load dump path configurable
-> The save/load blob dump path is now also made configurable
using a c/python Api's.
-> Introduced a flag named blob_dump_path
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor fixes added
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed python API issues
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Using GetEnvironmentVar to get the path
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed python runtime option issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes import network issue on windows
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Simplified version of WebAssembly support to keep most of existing data structures and add cmake using Ninja and emcmake
* Clean up CMakeLists.txt and add an example to create and compute a kernel
* Load a model from bytes and remove graph building steps
* Add all cpu and contrib ops with mlas library
* WebAssembly build with Onnxruntime C/CXX API
* Use protobuf cmakefile directory instead of adding every necessary source file
* Fix invalid output at example
* add missing files
* Change an example to use Teams model and support ort mobile format
* add API for javascript
* fix input releasing in _ort_run()
* update API
* Let onnxruntime cmake build WebAssembly with option '--wasm'
* allow one-step building for wasm
* Make build script working on Linux and MacOS
* Fix broken build from Windows command
* Enable unit test on building WebAssembly
* Resolve comments
* update build flags
* wasm conv improvement from: 1) GemmV; 2) Depthwise direct convolution 3x3; 3) Direct convolution 3x3
* Cleaned mlas unittest.
* use glob
* update comments
* Update baseline due to loss scale fix (#6948)
* fix stream sync issue (#6954)
* Enable type reduction in EyeLike, Mod, random.cc CPU kernels. (#6960)
* Update EyeLike CPU kernel.
* Update Mod CPU kernel.
* Update Multinomial CPU kernel.
* Slight improvement to Pad CPU kernel binary size.
* Update RandomNormal[Like], RandomUniform[Like] CPU kernels.
* Fix warning from setting multiple MSVC warning level options. (#6917)
Fix warning from setting multiple MSVC warning level options. Replace an existing /Wn flag instead of always appending a new one.
* MLAS: quantized GEMM update (#6916)
Various updates to the int8_t GEMMs:
1) Add ARM64 udot kernel to take advantage of dot product instructions available in newer cores. Some models run 4x faster than the stock implementation we used before.
2) Refactor the x64 kernels to share common code for AVX2(u8u8/u8s8/avxvnni) vs AVX512(u8u8/u8s8/avx512vnni) to reduce binary size.
3) Extend kernels to support per-column zero points for matrix B. This is not currently wired to an operator.
* Implement QLinearAveragePool with unit tests. (#6896)
Implement QLinearAveragePool with unit tests.
* Attention fusion detect num_heads and hidden_size automatically (#6920)
* fixed type to experimental session constructor (#6950)
* fixed type to experimental session constructor
Co-authored-by: David Medine <david.medine@brainproducts.com>
* Update onnxruntime_perf_test.exe to accept free dimension overrides (#6962)
Co-authored-by: Ori Levari <orlevari@microsoft.com>
* Fix possible fd leak in NNAPI (#6966)
* Release buffers for prepacked tensors (#6820)
Unsolved problems:
1. One test failure was caused by a bug in Cudnn rnn kernels, when they can allocate a buffer and partially initialize it, the garbage data near tail of the buffer caused problem in some of the hardware. To attack this problem in a broader sense, should we add code in our allocators, and during a memory fuzzing test, fill an allocated buffer with garbage before returning to the caller?
2. Prepacking is used more widely than we know. For instance, Cudnn rnn kernels also cache their weights. They mix several weight tensors together into a single buffer, and never touch the original weight tensor anymore. This is the same idea with pre-pack, but they didn't override the virtual function, and they never tried to release those weight tensors, leading to memory waste. It also seems to me that there are some other kernels have similar behavior. Wonder how much memory we can save if we try to cleanup those too.
3. Turning off memory pattern planning does increase memory fragmentation, leading to out of memory error in some training test cases. Perhaps we can revisit the idea of pushing kernels-creation stage earlier, and then during initializer deserialization, we only avoid tracing those that will be prepacked.
* Enable type reduction for Range, ReverseSequence, ScatterND, Split, and Unique CPU kernels. (#6963)
* add CI
* fix test in ci
* fix flags for nsync in wasm build
* add copyright banner
* fix wasm source glob
* add missing exports
* resolve comments
* Perf gain by make packb wide to 4 from 16 on GEMM for WASM.
Remove no need direct conv in previous perf tuning.
* fix buildbreak introduced from latest master merge
* fix buildbreak in mlasi.h
* resolve all comments except MLAS
* rewrite packb related 3 functions for WASM_SCALAR seperately rather than using #ifdef in each.
and other changes according to PR feedback in mlas.
* More complete scalar path in sgemm from Tracy.
* Fix edge case handling in depthwise conv2d kernel 3x3. where:
*) support input W==1 and H==1
*) recalc in accurate pad_right and pad_bottom
*) support hidden pad_right == 2 or pad_bottom == 2 when W == 1 or H==1 and no pad left/top
* Add more test coverage for conv depthwise from Tracy.
Fix one typo according to PR.
* resolve comments
* replace typedef by using
* do not use throw in OrtRun()
* output error message
Co-authored-by: Sunghoon <35605090+hanbitmyths@users.noreply.github.com>
Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Tracy Sharpe <42477615+tracysh@users.noreply.github.com>
Co-authored-by: David Medine <david.eric.medine@gmail.com>
Co-authored-by: David Medine <david.medine@brainproducts.com>
Co-authored-by: Ori Levari <ori.levari@microsoft.com>
Co-authored-by: Ori Levari <orlevari@microsoft.com>
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
Co-authored-by: Chen Fu <chenfucs@gmail.com>
With this change, differentiating CUDA EP and ROCm EP is not needed in training script when mem_limit option needs to be set.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Enable type reduction for Scatter/ScatterElements CPU kernels. Some refactoring to reduce binary size.
Add MLTypeCallDispatcher methods.
Minor cleanup for Pad CPU kernel.
* Allow specific optimizers to be disabled.
- replace unused ability to specify just the optimizers to run
- never used so not needed
Allow the disabled list to be specified via the python bindings
- expected usage is internal, so using kwargs for that so as not to pollute the documentation with stuff no user is likely to need
Update the ORT format model conversion script to disable NCHWc transformer when level is 'all'
- currently there aren't any known use cases where we'd want the NCHWc transformations to run as they create a device specific model and aren't used on ARM
- the ORT format model is not expected to be generated on the target device (e.g. generate on Windows/Linux/macOS to deploy to Android/iOS so there's a good chance we'd generate a useless/invalid model
- default to 'all' as ARM and MLAS prefer NHWC and the NHWC transformer runs at that level
* Add matching changes to optimizer generation in training code
Changes include:
* Revert Event Pool changes
* Add copyright and revert unrelated changes
* Add DLPack as submodule and remove to_dlpack and from_dlpack from public API
* Update golden numbers for DHP Parallel tests
* Update ORTTrainer unit test numbers
* Rollback to DLPack v0.3
* Disable flaky test
* Update third party notices and CG manifest file
* Minor refactoring of ORTValue API
Add functionality to the Graph class to be dumped to protobuf using an external binary file for the float initializers.
This change is meant to avoid hitting the 2GB protobuf limit when dumping large graphs.
This limit was particularly easy to exceed when dumping graphs after auto-diff.
The use of the external file is limited to initializers larger than a user-specified threshold.
This gives the possibility to users to include in the onnx file shape constants used by Reshape and Transpose used by Shape Inference.
* Update EyeLike CPU kernel.
* Update Mod CPU kernel.
* Update Multinomial CPU kernel.
* Slight improvement to Pad CPU kernel binary size.
* Update RandomNormal[Like], RandomUniform[Like] CPU kernels.
* add config allow_spinning
* add config allow_spinning
* set true as default
* split configures for inter and intra ops
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
In the previous shared providers there aren't many OpKernel classes, and the existing Provider_OpKernel wrapper was fine. With the opposibility of making Cuda a shared provider, having this need to be changed per OpKernel adds a lot of complexity.
It was fairly straightforward to make OpKernel work with shared providers with minimal changes.
In this change, the ONNX_OPERATOR_* macros can also be shared with the shared providers.
