Fix longformer parity and perf regression (#6760) …
Adding fp16 support for Einsum Cuda kernel (#6775) …
Update DirectML 1.4.1 to 1.4.2 for ORT 1.7 (#6780) …
Fix regression in constant folding optimizer (#6795)
Update transformers benchmark for transformers 4.3.* and ORT 1.7 (#6796) …
Make keepdims to its default value when adding ReduceMin/ReduceMax fo (#6788)… …
fix issues caused by quantize/calibrate changes (#6802)
6735 and 6728 already in release branch
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com>
Co-authored-by: Ori Levari <orlevari@microsoft.com>
Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Co-authored-by: stevenlix <38092805+stevenlix@users.noreply.github.com>
* Add ReluGrad and ConvGrad ops for the dnnl provider
* the mnist sample is updated to add the --use_dnnl option that
will cause the sample to use the dnnl execution provider for
nodes that exist in dnnl provider.
* Added the ability to find forward ops. Dnnl backward gradient
ops require the forward primitive description and workspace
from the forward operation.
* Enable specifying the execution provider for Gradient Checker Tests
* Prevent memory leak when running dnnl_provider in training mode
Prevent creating a SubgraphPrimitivePool when the code is built with the
ENABLE_TRAINING build flag. Instead create a SubgraphPrimitive directly.
The SubgraphPrimitivePool was causing a pool of SubgraphPrimitives to be
stashed in a map for reuse. Due to the way the Training Loop uses threads
the pool of SubgraphPrimitives were not being reuse instead a new pool of
SubgraphPrimitives being created each run. The old pool was not instantly
freed. This behavior could be a language error when using thread_local
memory.
Signed-off-by: George Nash <george.nash@intel.com>
* Added fixes to maxpoolgrad and memory leak.
Maxpoolgrad will now pass all unit tests.
With the conv and convgrad disabled for dnnl, mnist is able to train till 95%
Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Fixed misc issues when testing training code with dnnl provider
* fix conv_grad dnnl tests with dilation to run dnnl execution provider
* update mnist training sample to accept convolution type models
convolution models require the input shape to be {1, 28, 28}
instead of the flat {728} image that is used for the gemm models
this will enable models that require the different shape by adding
`--model_type conv` to the command line when running the mnist sample.
(while testing a workaround was used see #4762)
* Disable weight caching in dnnl conv operator when using training
When training we can not use cached weights because the weight
will be updated each run. This re-enables dnnl Conv and ConvGrad Ops.
The weight caching was the source of the error from Conv when training.
* Fix issues found when building grad ops on Linux
* The dnnl_convgrad code was over using the scope operator
causing a compilation problem.
* The dnnl_maxpoolgrad code had a logic error that is was
comparing with the source description when it should have
been comparing with the destination despription.
* Update BUILD.md so it shows DNNL for training
* Updated the table of contents. Since the same providers
are listed twice. Once for Infrance and again for Training
an HTML anchor was added to distinguish the second header
from the first for the TOC.
* Fix build failure when not using --enable-training build option
* reorganize the gradient operators so they are grouped together
* Fix issues found when running onnx_backend_test_series.py
* Pooling code only supports 2 outputs when built with --enable-training
* Address code review feedback
* class member variables end in underscore_
* use dst instead of dist to match pattern use elsewhere in DNNL code.
* Remove workaround that was introduced to handle problems running
convolution based training models. See issue #4762
Signed-off-by: George Nash <george.nash@intel.com>
* Isolate training code and code cleanup
* Do not build if dnnl_gpu_runtime if enable_training is set training code
does not support dnnl_gpu_runtime yet.
* Isolated Training code inside ifdefs so that they wont affect
project if built without training enabled
* Inadvertant changes in whitespace were removed to make code review simpler
* Undid some code reordering that was not needed
* comments added to closing #endif statments to simplify reading complex ifdefs
* Modified the GetPrimitiveDesc functions to return shared_ptr instead of raw
pointer. This matches what was done in Pool code and is safer memory code.
Signed-off-by: George Nash <george.nash@intel.com>
* Address code review issues
- whitespace changes caused by running clang-format on the code
- Several spelling errors fixed
- Removed/changed some ifdefs to improve readability
- other misc. changes in responce to code review.
Signed-off-by: George Nash <george.nash@intel.com>
* Code changes to address code review
- Simplify iteration code using `auto` keyword
- remove C style cast that was not needed
- remove instance variable that was not needed [relugrad.h]
- added the execution providers to `ComputeGradientErrorInternal()`
and `ComputeTheoreticalJacobianTranspose()` instead of using
a pointer to an instance varaible [gradient_checker.h/.cc]
Signed-off-by: George Nash <george.nash@intel.com>
* Combined the default gradient ops test and dnnl gradient ops test for ConvGrad and MaxPoolGrad into one function with the help of a helper function.
This will reduce repeated code.
Signed-off-by: Palangotu Keshava, Chethan's avatarChethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
* Replaced the stack used by convgrad to vector so that the vector(used as stack) can be easily cleared everytime the graph is created.
This will prevent memory leak from convolution kernels being pushed constantly onto the stack.
Signed-off-by: chethan.palangotu.keshava@intel.com
* Code clean up and formating updates
- Removed empty else statment
- updated indentation of code that was causing double curly brackets to look unususal
- Changed check for NumDimensions to Size in Relu and ReluGrad error checking code.
- isolated training code
Signed-off-by: George Nash <george.nash@intel.com>
* Restore inadvertantly removed ConvGrad tests
When combining the DNNL and CPU version of the ConvGrad
tests two test were inadvertantly excluded. This adds
back the Conv3d and Conv3d with strides test cases.
Signed-off-by: George Nash <george.nash@intel.com>
* Add validation to ConvGrad
This validates the dimensions of the ConvGrad match the
passed in Convolution forward primitive description.
The current code for DNNL ConvGrad makes the assumption that the ConvGrad
nodes will be visited in the reverse order from the corresponding Conv nodes
The added validation will return an error if this assumption is not true.
Signed-off-by: George Nash <george.nash@intel.com>
* Do not create new execution providers in provider_test_utils
This removes the code that generated new execution providers in the
OpTester::Run function. This was added because the std::move was
leaving the `entry` value empty so subsequent calls would cause a
segfault.
Problem is this potentially changed the execution_provider because it
would create the default provider dropping any custom arguments.
When the now removed code was originally added the std::move was causing
crashes when the GradientChecker unit tests were run. However, it is no
longer causing problems even with the code removed.
Signed-off-by: George Nash <george.nash@intel.com>
* Change the forward conv stack to a forward conv map
This changes how the forward conv kernel is mapped to the bwd ConvGrad
kernel the problematic stack is no longer used.
The convolution stack made the assumption that the corresponding
ConvGrad operator would be visited in reverse order of the forward
Conv operators. This was always problematic and was unlikely to
work for inception models.
Important changes:
- The weight_name is added to the ConvGrad dnnl_node making it
possible to use the weight_name as a lookup key to find the
Conv forward Kernel
- the `std::vector fwd_conv_stack_` has been replaced with a
`std::map fwd_conv_kernel_map_`
- Although it is not needed lock_guards were added when writing
to and reading from the fwd_conv_kernel_map_ as well as the
fwd_kernel_map_. These should always be accessed by a single
thread when preparing the dnnl subgraphs so the guard should not
be needed but its added just in case.
- Updated the comments ConvGrad.h code to no longer mention the
stack. The error check is not removed. It will be good to verify
there are no errors as we continue to test against more models.
Signed-off-by: George Nash <george.nash@intel.com>
Co-authored-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com>
Co-authored-by: unknown <63478620+jeyblu@users.noreply.github.com>
* assert sequence tensor and remove skips
* update testdata json
* use ONNX 1.8 in cgmanifest.json
* use previous commit to workaround
* update ONNX commit ID in docker
* skip test_maxpool_2d_dilations test for now
* update function name
* Remove nGraph Execution Provider
Pursuant to nGraph deprecation notice: https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md#deprecation-notice
**Deprecation Notice**
| | |
| --- | --- |
| Deprecation Begins | June 1, 2020 |
| Removal Date | December 1, 2020 |
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features
previously available through nGraph have been merged into the OpenVINO™
toolkit. As a result, all the features previously available through
ONNX RT Execution Provider for nGraph have been merged with ONNX RT
Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated
starting June 1, 2020 and will be completely removed on December 1,
2020. Users are recommended to migrate to the ONNX RT Execution Provider
for OpenVINO™ toolkit as the unified solution for all AI inferencing on
Intel® hardware.
* Remove nGraph Licence info from ThirdPartyNotices.txt
* Use simple Test.Run() for tests without EP exclusions
To be consistent with rest of test code.
* Remove nGraph EP functions from Java code
Transitions from the ORT-only DML NuGet (hosted on the onnxruntime_public feed) to the new unified DirectML NuGet (Microsoft.AI.DirectML) on nuget.org. In addition, the Microsoft.AI.MachineLearning (WinML) and Microsoft.ML.OnnxRuntime.DirectML packages now take a dependency on the Microsoft.AI.DirectML package. This means we can remove the extra copy of DML binaries in these packages since they will be installed by the DML package.
* fix hash conflict
* Add verbose for engine deserialization and destroy old engine memory if new engine is generated
* update parser
* Update tensorrt_execution_provider.cc
* use a better hash algorithm
* Update tensorrt_execution_provider.cc
* Fix places where MinSizeRel wasn't having relevant flags added in the same way as Release and RelWithDebInfo
Enable LTO for minimal build. Cleanups onnx_minimal.cmake to remove some things handled when LTO is enabled in CMakeLists.txt
* Only enable LTO for MSVC in a minimal build
* Add minimal build option to build.py
Group some of the build settings so binary size reduction options are all together
Make some cmake variable naming more consistent
Replace usage of std::hash with murmurhash3 for kernel. std::hash is implementation dependent so can't be used.
Add initial doco and ONNX to ORT model conversion script
Misc cleanups of minimal build breaks.
* correct some errors in the flatbuffers schema, move flatbuffers submodule to cmake/external
* update the ort flatbuffers schema to use less namespace
* minor update
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
* update onnx to latest master
* implement per-channel for quantizelinear and dequantizelinear
* refine the unit test
* exclude sequence_insert tests
* refine onnx cmake
* add failure tests to broken_tests
* move qdq common code to a seperate function
* refine code
1. Publish the image ACR, instead of building it every time for every PR
2. Make USE_MKLML and USE_OPENMP be able to co-exist. Currently both of them are enabled in our Linux CI build but indeed only one of them is taking effect.
3. Split nuphar and DNNL to separated pipelines.
4. Fix two warnings in onnxruntime/core/optimizer/matmul_scale_fusion.cc and onnxruntime/test/tvm/tvm_basic_test.cc.
5. Update the manylinux2010_x86_64 image to the latest.
* bump onnx to support bfloat16
* sign test code
* fix ut failures
* add bfloat type in gradient schema
* add bfloat16 to gathernd
* add bfloat16 into grad op defs
* temp disable gpu fusing transformers
* bfloat16 support fix
* more fix to bfloat
* bug ifx
* add bfloat16 to transpose matmul
* fix sce loss
* fix cast opset13 and other missing part of bfloat16
* Revert "temp disable gpu fusing transformers"
This reverts commit b627bc9019.
* add SCEloss back
* fix build break
* fix gpu failure due to missing kernel in opset13
* add tile opset 13 kernel
* Revert "fix gpu failure due to missing kernel in opset13"
This reverts commit 661d63d0599029757f240d29afd64b197b76b880.
* fix comments in pr
* fix cuda break due to opset13
* fix missing msdomain
* add nll loss tests into android build's broken list; disable bfloat16 cast tests due to the wrong type saved in onnx test data, will fix it in onnx first
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* Add protobuf mutator library as a git submodule
* Added files and instructions to build the protobuf mutator library in CMake
* Added fuzzing flag to build system and added fuzzing dependency library. To run fuzzing test use the flags --fuzz_testing --build_shared_lib --use_full_protobuf --cmake_generator 'Visual Studio 16 2019'
* Added src files and build instructions for the main fuzzing engine
* Removed Random number generation test from inside the engine
* Added license header to files
* Removed all pep8 violations introduced by this change and other E501 violations
* Merged PR 4616739: Update QLinear Ops fix 1D support layout
Update QLinear Ops fix 1D support layout
Related work items: #26011523
* Merged PR 4617257: Gather operator DML EP fails with scalar indices and 1D inputs
Fix gather with scalar value.
The ONNX conformance test case is in another PR:
// 0D, axis 1, rank 0 indices tensor
{
"op_type": "Gather",
"axis": 0,
"data": [1,2,3],
"indices": 0,
"output": 1,
"T": "float32"
}
* Merged PR 4632178: Re-enable ORT onnx_test_runner test case (DirectML ConvTranspose validation needs to be loosened to comply with ONNX definition of output_padding)
Re-enable 1D convolution tests.
Related work items: #23499747
* Merged PR 4656672: Make DML EP use Direct queue
While a Compute queue has benefits, Direct is consistent with Winml.
Related work items: #26324112
* Update DML nuget version
* Merged PR 4662079: Update DmlDev branch again from github master
Include Sheil's changes to fix namespace and header file include paths. Without this, the ONNX conformance tests all fail with E_NOTIMPL.
* Increment DML nuget version
Co-authored-by: Nick Feeney <nickfe@microsoft.com>
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
