Fix orttraining-linux-ci-pipeline - Symbolic shape infer #11965
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garymm
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Jun 23, 2022
RandySheriffH
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Jul 6, 2022
fix symbolic shape error due to upgraded numpy + legacy sympy
RandySheriffH
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Jul 7, 2022
* Update ONNX to 1.12 (#11924) Follow-ups that need to happen after this and before the next ORT release: * Support SequenceMap with #11731 * Support signal ops with #11778 Follow-ups that need to happen after this but don't necessarily need to happen before the release: * Implement LayerNormalization kernel for opset version 17: #11916 Fixes #11640 * Dll version fix ovep4.1 (#11953) * Setting default version values for ovep dlls as well * Update backend_manager.cc Co-authored-by: mayavijx <mayax.vijayan@intel.com> Co-authored-by: mohsin <mohsinx.mohammad@intel.com> * Optimize t5 encoder in beam search (#11926) * ooptimize t5 encoder * update * update * update * refactor expand impl * cuda tests passed * update * alignment * more alignments * review comments * Allow saving on CPU usage for infrequent inference requests by reducing thread spinning (#11841) Introduce Start/Stop threadpool spinning switch Add a session config option to force spinning stop at the end of the Run() * Restructure function inliner (#11731) * Add nested function call tests * Add overload for Specialize * Pass symboltable to onnx shape inference * Avoid renaming empty names * Enable sequence_map tests which failed before this change * Deprecate APIs returning raw ptrs and provide replacements (#11922) Provider better documentation * register signal ops for opset 17 (#11778) * Register signal ops for op set 17 Note code is mostly being moved, not added. These ops were previously only registered as Microsoft contrib ops and only built if `BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx standard op set in version 17. Main components of this change: * Move the kernels from the conrib_ops directory to the core directory. * Add function bodies for ms experimental ops. This will allow old models that use the contrib ops to continue to function. All the function bodies consist of a single op (the new standard op), so performance overhead should be minimal. Minor clean-up also in this change: * De-duplicate get_scalar_value_from_tensor: put it in a new utils.h. * Fix some bugs that caused compilation errors with the experimental ops. Tested with `build.sh --ms_experimental` * Fix some spelling errors and lint violations. * Replace a couple of switch statements with `MLTypeCallDispatcher`. * Use `InlineVector` instead of `std::vector`. Unblocks #11640 * Include opset 15 in Conv+BatchNormalization fusion (#11960) * Fix WinML Tests are still targetting deprecated (deleted) experimental signal op definitions (#12006) * fix winml tests * remove legacy test * switch idft -> dft+inverse attr * upgrade opset 13->17 for signal ops tests * [C# Tests] Add support for double tensor output in TestPreTrainedModels. (#12008) Add support for double tensor output in TestPreTrainedModels. * DML EP ResNet50 opset 15 fails in ONNX checker for FusedBatchNormalization lacking training_mode attribute (#12010) FusedBatchNormalization include training_mode attribute * Generalize native op creation (#11539) * 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 * [DML EP] Pad operator: Handle negative pad counts (#11974) * Pad fallback to CPU * Added queryPad in operatorRegistration.cpp * Acknowledged PR comments * Used any_of * used none_of instead of any_of Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com> * Add warning about future computation change for ConvTranspose with auto_pad (#11984) * Add warning about future computation change for Convtranspose with auto_pad * improve msg * update TODO to make lint happy * update more contents for warning and add if * valid was not infected * move it into kernel registration * parse auto_pad myself * try to use conv_transpose_attrs_.auto_pad directly * update roialign cuda impl to onnx opset16 (#12036) * roialign opset16 * fix * fix * Fix windows eager build break by pinning to torch version 1.11.0 (#12033) Fix windows and linux eager build to torch 1.11.0. * Skip Constant Folding for ops producing an optional type output (#11839) * Disable sequence-type tests since C# infra doesn't support well (#12037) * Extend lifetime of KernelDef when creating a standalone op (#12057) place tmp kernel def as local variable to cover the lifetime of kernel creation * Add targets files for new .net6 frameworks (#12016) * Add net6 targets. Remove maccatalyst as we don't have a native build targetting that. * Set platform in macos targets * Add targetFramework entries * Move NativeLib.DllName definition and set using preprocessor values for simplicity. Couldn't get it to build with the preprocessor based setup when it was in a separate file. Update the nuspec generation to set platform version for .net6 targets. TODO: Validate versions. I copied them from the managed nuget package the packaging pipeline generated prior to adding targets. Possibly w could/should lower some of the versions. Hopefully the need to specify a version goes away when the release version of VS2022 supports .net6. * Try android 31.1 as https://github.com/actions/virtual-environments/blob/main/images/win/Windows2022-Readme.md suggests that should be available on the CI machines * Fix patch version mismatch Add some extra debug info in case it helps * Debug nuget location in CI * Add workspace entry back in * Add steps * One more attempt with hardcoded nuget.exe path and original android31.0 version * Better fix - found explicit nuget download and updated version there. * flake8 fixes * Fix black complaints. * Exit Microsoft_ML_OnnxRuntime_CheckPrerequisites for net6 iOS. * Removed outdated comment * Fix DML custom operators which set descriptor heap to command list (#12059) * Make C# runtest.sh automatically set latest opset (#12039) * Update C# runtest.sh for opset 17 Should have been part of #11924 * get appropriate opset version from onnx doc * use absolute rather than relative path * fix typo in var name * Disable DML command list reuse for Xbox (#12063) disable cl reuse for xbox * Add data type check in ConvAddRelu fusion (#12058) * Add undocumented attribute to disable generation of Java bindings from the Android AAR. (#12075) The generated bindings causes C# build errors that require workaround code. Disabling generation should avoid the need for any workarounds. As the user has the C# ORT package with the C# to C bindings there's no need for binding generation that calls the ORT Java API (which is C# -> Java ->C). * enable the extensions custom build for java and android (#11823) * generate quantization parameter for outputs (#12089) * DML EP Update to DML 1.9 (#12090) * Update to DML 1.9 * Appease obnoxious Python formatting tool * Fix orttraining-linux-ci-pipeline - Symbolic shape infer (#11965) fix symbolic shape error due to upgraded numpy + legacy sympy * check consumers of dq node before swap dq and transpose (#12099) * check consumers of dq node before swap dq and transpose * add unit test Co-authored-by: Gary Miguel <garymiguel@microsoft.com> Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com> Co-authored-by: mayavijx <mayax.vijayan@intel.com> Co-authored-by: mohsin <mohsinx.mohammad@intel.com> Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: G. Ramalingam <grama@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com> Co-authored-by: Sheil Kumar <smk2007@gmail.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: sumitsays <sumitagarwal330@gmail.com> Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com> Co-authored-by: Chun-Wei Chen <jacky82226@gmail.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: Wil Brady <25513670+WilBrady@users.noreply.github.com> Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Wei-Sheng Chin <wschin@outlook.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Jeff Bloomfield <38966965+jeffbloo@users.noreply.github.com> Co-authored-by: Justin Stoecker <justoeck@microsoft.com> Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: pengwa <pengwa@microsoft.com>
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Description: Describe your changes.
fix symbolic shape error due to upgraded numpy + legacy sympy
AttributeError: module 'numpy' has no attribute 'asscalar'
June 23, numpy upgraded on PYPI. The sympy version we used is old, which used a deprecated numpy function.
Collecting sympy==1.1.1
Downloading sympy-1.1.1.tar.gz (4.6 MB)
Collecting numpy>=1.16.6
Downloading numpy-1.23.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.1/17.1 MB 137.9 MB/s eta 0:00:00
The version controlled sympy for orttraining-linux-ci-pipeline is:
But I fixed other two places for parity. Let me know if you think they are not required.
Motivation and Context