* Define krnl.permute op.
* Support krnl.permute operation.
* Properly remove loop references.
* Re-push, Github was down.
* Need to debug interpretOp error.
* Fix lowering bug by erasing ops after full krnl IR interpretation is done, and clean up & comment code.
* Introduce permute, unroll operations.
* More debug.
* Remove std::set.
* krnl.terminate fails to be converted.
* Pass all tests, need to add legal ops as well as part of the conversion target.
* Change test format to new permute spec.
* Bug fix for nested iterate op lowering.
* Simplify error reporting.
* Fix compilation error.
* Increase comments coverage.
* Remove unnecessary imports.
* Re-trigger Jenkins
* Add permute/unroll tests.
* Retrigger Jenkins
* transpose fusion and removal
* format
* fix comments
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Add shape inference and names
- Add shape inference for PRelu
- Fix shape inference for group conv
for ConvTranspose
- Add input and output names for
graphs (functions)
- Add support for (u)int8 tensor
attributes
* Fix format issues
* Revert formatting for gen_onnx_mlir.py
* Pads can have ArrayAttr and DenseElementsAttr so support both
* NumInputs is the number of graph inputs that don't have initializers
* Add test for 2D batchnorm
* Fix typo in define_loops in new 2d BN test
* Change 'name' to 'onnx_node_name'
* Fix Batchnorm for 2D I/O and add lowering test
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Rewriting rule
* Fix formulas
* Reuse op results
* Const propagation for Div and Sqrt
* Explicitly use ONNXConstantOp
* Minor revise
* Const propagation for unsqueeze
* Do const propagationnce all tensors have inferred shapes
* LIT tests for fusion
* Add LIT tests for constant propagation on Div, Sqrt, and Unsqueeze
* Missing dash
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* move scalerop to decompose
* change clang format
* change clang format
* add shape inference for scaler op
* fixing generated onnxop
* generate onnx.md
* redo get onnx.md and onnxop.td.inc using onnx 1.6
* Add shape inference for scaler op
* add benefit for scaler decompose and simplify scaler shape inference
* add scaler decompose benefit num and simplify shape inference
* add cast builder
* cast rewrite only for float
* add cast op same type rewrite rule
* working on cast lowering
* cast lowering working
* add cast lowering
* fix format
* Delete OpBuildTable.inc
* complete requested changes
Co-authored-by: chentong319 <chentong@us.ibm.com>
* Update LLVM commit ID to include to the new modeling of LLVM type in MLIR
* Fix commit id discrepancy
* Update README.md
* Update MLIR version
* Force rebuild prereq dockers and see what happens.
* Use LLVM commit ID that corresponds to MLIR News, 13th edition (8/7/2020)
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Add shape inference for Ops used by BERT
* Erf
* Pow
* ReduceMean
* Dropout
* Expand
https://github.com/onnx/onnx/blob/master/docs/Operators.md#expand
Deduce the value of the shape operand by looking at the producer
of the operand.
Currently supported producers are: onnx.Constant and onnx.Shape.
* Add corresponding tests for each op.
* Sort the list of ops with shape inference in gen_onnx_mlir.py
in alphabetic order for clarity.
* Restart CI
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* catch errors after build command
* remove spaces
* use %ERRORLEVEL% instead
* move modification to CI command only
* add extra nextline to prevent modification
* base implementation
* add example
* change table gen
* docs
* small change for review
Co-authored-by: Alexandre Eichenberger <alexe@us.ibm.com>
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* move scalerop to decompose
* change clang format
* change clang format
* add shape inference for scaler op
* fixing generated onnxop
* generate onnx.md
* add benefit for scaler decompose and simplify scaler shape inference
* cast rewrite only for float
* add cast op same type rewrite rule
* fix format
Co-authored-by: chentong319 <chentong@us.ibm.com>
* move scalerop to decompose
* change clang format
* change clang format
* add shape inference for scaler op
* fixing generated onnxop
* generate onnx.md
* Add shape inference for scaler op
* add benefit for scaler decompose and simplify scaler shape inference
* changes for mypipeline.onnx
* format
* rm MLOpBuildTable.inc
* copy string without free
* fix the memory issue
* restore change for STRING
* format
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Lower Squeeze op to Krnl dialect
* Emit tensor size as a single constant; add a lit test for unknown dimensions
* Code style
* Speical case where the input is only used by this squeeze op
* Remove squeeze-in-place optimization
* Update ConvertONNXToKrnl.cpp
Twek to re-run tests.
* Trigger buildbot re-run.
* Re-run CI
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* string type from tensorflow
* simplify type
* parser and print
* gen StringType for tablegen
* onnx to onnx-mlir type
* add namespace
* allow all integer type
* dialect document
* add test case
* format
* more precise type for ONNXOp
* format
* enable the failed test
* update comment
* update onnx.md
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* support map and seq in tablegen
* register MLONNX for testing
* format
* remove the unwanted test
* add a test
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* 1. Add shape inference for the following ops:
- Atan
- Tan
- Sin
- Cast
- ConvTranspose
- Flatten
- DynamicQuantizeLinear
- QuantizeLinear
- DequantizeLinear
- ConvInteger
2. Import attributes for generic nodes
3. Fixes for cases where .cast<> should be .isa<> (ONNXConcat::inferShapes)
* Fix foormatting issues
* Address comments:
- SmallVector<> * -> SmallVectorImpl<> &
- switch-case -> helper function
- Inside helper function, preserve signed-ness
- add TODOs
* Can't use signed integers yet in convertONNXTypeToMLIRType, add TODO
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* initial const prop attempt
* added support for broadcast ops
* adde all binary broadcast ops into custom builders with precise type
* added test example
* working
* format
* fixed suggestion by Tung, start woring on unary
* added subtraction and neg the right way, and added elementwise mul too
* formatting changes
* format
* format
* added instructions to add new optimizations
* Call llc, ld from within onnx-mlir.
* Rename EmitLLVMBC -> EmitLib., reorder header files
* Edit comment.
* Checkpoint, debug.py works.
* Automatically generate inputs in debug.py.
* Use float.
* initial support for rapidcheck tests.
* Convolution test case works.
* Format code.
* Link library with MainUtils.
* Fix CMake script error.
* Fast implementation of array assertion, more detailed error analysis.
* More utility for DynMemRef.
* Fix linking issue.
* Uncomment unit test.
* Refactor to separate C++/Python ExecutionSession, enable unit test.
* format code.
* Verbose build.
* Enable PIC option for ExecusionSession.
* Fix cmake error.
* Build all targets.
* Fix doc to build all targets.
* Clean up.
* Clean up, debug.
* Use type alias consistently.
* Move definitions to DynMemRef.cpp.
* include algorithm.
* pyruntime -> PyRuntime
* Format code.
* Free memory.
* Add comments.
* Copyright notice.
* Improve stylistic consistency.
* Add comment.
* Revert irrelevant changes.
* Disambiguate.
* Refator test case generator out from test case implementation, implement example exhaustive test driver.
* Add documentation for testing.
* Add type inference for CastOp
* Share type translation between op builder and onnx importer
* clang-format
* Format emitted code
* Remove unnecessary dependencies
* fix type inference for ConstantOp and MaxPoolSingleOut
* modify interface
* use OpInterface
* change name to OpInterface
* Builder dependence
* Update CMakeLists.txt
* Update CMakeLists.txt
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Call llc, ld from within onnx-mlir.
* Rename EmitLLVMBC -> EmitLib., reorder header files
* Checkpoint, debug.py works.
* Automatically generate inputs in debug.py.
* Use float.
* Fix merge conflict, remove RapidCheck from this patch.
* Remove submodule rapidcheck properly.
* Reformat code.
* More comments.
* Add documentation.
* Add documentation to navigation.
* Account for the fact that some initializers may also appear as input.
* Move to more recent LLVM ID (May 15)
* clang-format
* Bump cache version up
* Update readme
* Fix doc check
* Move to a newer commit id
* Update LoopToStandard -> SCFToStandard
* Change MLIRSideEffects to MLIRSideEffectInterfaces
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* enable promote attr for pad
* use optional arguments for pad
* shape infereance for pad
* Lowering Pad
* format file
* use DenseTensor for the attribute
* use Pad in ONNXRewrite
* fix the merge conflict
* fix the attr given to constantOp
* handle ONNXConstantOp in attribute promotion
* Fix bug when AttributePromotion is called more than once
* update ONNXOps.td.inc with correct version of onnx
* update onnx.md
* responses to review
* fix the build error
* change the implementation of Pad
* delete commented out code
* clang format
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Support dilations and enable e2e tests
* Fix allocating memory for dynamic shape
* Edit comments
* Do dilation by computing an offset from kernel index
* Correct dilation formula, add an example of out-of-bound, and add a test for dilation
* Import optional outputs as NoneType
* Shape inference for ONNXLSTM
* Edit ONNXLSTM::inferShape()
* Shape inference for ONNXLSTMOp
* Create a common function for inferring shape for RNN ops
* CheckInsertDeallocation for a specific result
* Allocate memory for LSTM
* First round of lowering
* Allocate memory for hidden and cell states
* Test with custom Tanh
* Fix an error in Ct's formula
* Add E2E tests
* Return outputs
* Refactor the code
* Enable E2E tests
* Support reverse and bidirectional directions
* Minor revision
* Return all intermediate hidden states
* Call existing activation functions
* Structs for activation functions
* Call existing activations in ONNX
* Minor revision
* Compare strings ignoring case
* Use memreftype of rank 0 for calling activation functions
* Fix getActivationPack()
* Revise the code
* Add one MLIR test
* Add MLIR tests for reverse and bidirectional modes
* Make the order of emiting instructions deterministic
* Use OperandAdaptor instead of directly use an operand index
* Use literal assignments
* Change some variable names
* Use literal assignments
* Use literal assignments
* Format the code
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Implement shape inference for SplitOp
* Change spitOpt to SplitAttribute and check the axis range before updating the axis attribute
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* add buildbot on Windows via Azure Pipeline
Use Py 3.7 instead of 3.6
* Add status badge for Azure Pipeline
* reuse original cmd files, couple build and test on Windows-CI
* Update Python version on .yml
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Make onnx-mlir work with latest mlir.
* Bump CircleCI cache version.
* Fix missing passes in onnx-mlir-opt.
* Fix backend test failure.
* Fix doc.
* Fix doc and exclude the generated _site directory from DocCheck.
* Remove debug code.
* Do not hard code target name, on Mac shared lib can end with .dylib.
* FunctionPass -> PassWrapper.
* Move to more recent LLVM commit ID
* Update LLVM cache version from V9 to V10
* Update to latest LLVM commit id from master, roll back conditions in util scripts
* Update circlci LLVM cache tag to ensure ci updates builds with latest LLVM commit id
* Update README.md to have matching LLVM commit id
* Update doc/Dialtects/onnx.md
* Enable onnx-mlir for VS builds on Windows
* Update README to include lit
* Update build command for Windows to include config
* Update build instructions, add cmd files for windows, enable single source of truth for MLIR commit-id (clone-mlir.sh)
* Add Visual Studio workload info
* Update ONNX op definitions
* Revert onnx submodule back to previous commit, disable warnings in CMakeLists to work around build issues with MSVC
* Update environment for path to PDcurses on Windows
* Fix directory strings to be compatible with Windows or Linux style slashes
* Fix install-mlir.sh so it works when sourced
* Ensure README and cmd files match and have correct paths
* Properly quote ONNX_MLIR_SRC_DIR
* Address PR feedback: Use llvm_unreachable to indicate failure to convert attribute proto to name/value pair
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Setup documentation server, move doc files from /doc to /docs as per Github Pages convention.
* Include deleted files in patch.
* /doc -> /docs
* /doc -> /docs
* Update documentation on importing ONNX spec into ONNX Dialect; provide documentation on how to add new documentation pages.
* Generate ONNX Dialect TableGen Inc files & operation importing inc files when necessary.
* Ensure TableGen inc file is generated before TableGen is invoked.
* Nit: capitalize builder -> Builder.
* Use file-same-as-stdout directive to ensure generated files are always up-to-date in our codebase.
* Use more up-to-date version of ONNXOps.td.inc.
* Do not automatically invoke gen_doc.py.
* Support dry run in gen_doc.py.
* Fix case.
* Remove debug code.
* Add test for new doc_check primitive.
* Add documentation for file-same-as-stdout.
* Provide more comments.
* Add DocCheck to DocCheck README.
* Nit: format CMake script.
* Update comments.
Co-authored-by: Alexandre Eichenberger <alexe@us.ibm.com>
* Move to more recent LLVM commit ID
* Update LLVM cache version from V9 to V10
* Update to latest LLVM commit id from master, roll back conditions in util scripts
* Update circlci LLVM cache tag to ensure ci updates builds with latest LLVM commit id
* Update README.md to have matching LLVM commit id
* Update doc/Dialtects/onnx.md
Tian Jin
Alexandre Eichenberger
Aman LaChapelle
Tung D. Le
Anh Leu
albertomagni-ms
Chun-Wei Chen
chentong319
Kevin O'Brien
Anh Leu
Aaron Smith
David Byrd
Byron Changuion