* 1. Combine variadicIn/Out with expectedNumOperands/Results to simplify import function arguments.
2. Generic improvements to code readability in gen_doc.py.
* Update ONNX Dialect doc.
* Remove redundant code in ImportNode.
* Prettify op_build_table.inc.
* 1. Remove irrelevant code in gen_doc.py
* Refactor code to be more readable.
* Further refactoring for readability improvements.
* Allow gemm to have an optional operand (bias term), and include an example of declarative optimization pattern targeting gemm with bias term ommitted.
* Make shape inference/lowering of gemm op compatible with optional operand declaration.
* Apply canonicalization again after lowering from onnx -> std dialects.
* Make hasBias compatible with the situation of GemmNoBias op.
* Update doc.
* Add a canonicalization test.
* Remove special handler for importing Gemm op, as it's redundant now.
* Add ONNXBatchNormalizationTestModeOp and its shape inference
* Lower batchnormalization test mode
* re-use scale, bias, mean, and variance
* Add MLIR tests
* Add e2e tests
* fix typos
* Fix a bug in MLIR tests
* Change type from int to int64_t for indices
* Uncomment e2e tests due to segmentation fault
* Uncomment e2e tests due to segmentation fault
* Revise the code
* [Tian] Fix segmentation fault in e2e tests
* Re-generate onnx.md to include BatchNormalizationTestModeOp
* Reverse an unintentional change
* Fix some typos in comments
* Use convertToMemRefType from the master branch
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Allocate memory for matmul's result
* Group cases
* Add support of N-D x N-D, N>=2
* Revise createIterateOperandPack
* Add 1-D x 1-D
* Add 1-D x N-D
* Add MLIR tests
* Change variable names
* Change type from int to int64_t for indices
* Change variable names
* Change int64_t back to int
* Change int64_t back to int
* Change int64_t back to int
* Use decltype
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* handle pad op which does not have the optional third argment
* rewrite PadConstantValue with constant pad into PadConstantValuePad
* add test for PadConstantValuePad
* update onnx.md
* Shape inference for reduction
* Lower ReduceSum
* Support list-like attributes
* Add ReduceMax, ReduceMin, ReduceProd
* Add tests
* Emit errors for unsupported types
* Typos
* Add backend test
* Fix axis computation
* Update the use of attributes
* Use SmallVector
* Address stylistic comments
* Change type from int to int64_t for indices
* Change type from int to int64_t for indices
* Ensure data shape is at least 4.
* First version of convolution.
* Simplify code for KRNL lowering.
* Add test without padding or strides.
* Refactor code for lowering frontend operations to KRNL dialect.
* Add test for conv with no bias and no padding.
* Add test with group greater than one.
* Address comment.
* Support lowering of SignOp
* Fixed test code for signop of integer input
* Inserted Sigh and Reciprocal in SharingWork.md (Reciprocal is for past commit 7e3f96e)
* Added test for Sign Op
* Fixed minus_one -> minusOne
* Fixed test for signop
* Rewrite ReduceSumSquare
* Edit gen_doc.py
* Revise the code
* Do shape inference after canonicalization so that there is no need to implement shape inference of rewritten ops
* Rewrite ReduceL2
* Add onnx_rewrite.cpp for all rewriting for ONNX ops
* Rewrite ReduceL1, ReduceLogSum, ReduceLogSumExp
* Edit comments
* Change the use of -> to .
* Checkout gen_doc.py from the master branch
* Use emplace_back instead of push_back
* Revise the code
* Edit comments
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* first steps for shape inference of maxpool
* setps forward
* ongoing
* working version
* first steps for shape inference of maxpool
* setps forward
* ongoing
* working version
* fix errors introduced by github merge
* changes suggested by Doru
* updates
* requested fixes
* reqested changes
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Initial implementation
* Support transposing inputs
* Revise unidirectional broadcasting and unknown dimensions
* Revise gemm
* Add testcase
* Rename some variables
* Update SharingWork.md
* Change from the use of Value* to Value
* Insert deallocation
* Initilize the output matrix and fix wrong computation
* Add end-to-end testcases
* Edit lowering tests
* Change attribute names
* Use emplace_push for SmallVector
* Use the new way of getting attributes
* Revise the use of attributes
* Check the bias's shape
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Infer shape for Unsqueeze
* Lower Unsqueeze
* Revise
* Turn off backend tests
* Compute tensorSize for static shape
* Compute tensorSize with unknown dims
* Edit tests
* Update the use of attributes
* Add e2e tests
* Use SmallVector
* Remove return
* Check whether the operand is ranked or not
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Initial lowering of KrnlSqrtOp
* Fix errors and add a testcase
* typos
* Add the MLIR example
* Restore doc/doc_check/CMakeLists.txt
* Clean the code
* Edit comments
* Remove redundant parts
* Chang the use of -> to .
* Add a test for f64
* Support ONNXSqrtOp
* Fix indentation
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* add attributes of Op into parameters
* fix rewrite rule for GemmOp with attributes
* use I64Attr instead of I32Attr and modify test cases for the changes in attributes
* add output name (prefixed with o_) to Op definition
* update shape inference for the new attributes
* Support Softplus and Softsign operations
* Add the default shape inference for the transposition operation.
* Fix conflict with master
* Fix conflict with master branch
* Add test for softplus and softsign in test/backend/test.py
* Re-enable Reciprocal tests.
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* change the read-in of attribute, using variant
* Use backported variant.
* Reduce code duplication.
* 1. Make array attribute parsing more clear.
2. int -> int64_t.
* 1. Fix how array attributes are imported.
* 1. Fix clang-tidy warnings.
* 1. Nit: fix clang-tidy warnings.
* Fix MaxPool node construction.
* Fix call to MaxPool.
* Comment out backend tests that fail.
* Add path to variant submodule to enable include file detection.
* Allow unused argument to avoid special casing generator.
* Address attribute related e2e test failures for Hard sigmoid,Elu,LeakyRelu,Selu,Softmax
Co-authored-by: chentong319 <chentong@us.ibm.com>
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Rebase
* Use max normalization
* Handle axis
* Add tests
* Update SharingWork.md
* Remove redundant spaces
* Format code
* Rebase
* Change from the use of Value* to Value
* Add end-to-end tests
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Add broadcasting support for elementwise operations
* Remove MLIRDialect from MLIRWholeArchiveLibs
* Rewrite getLoopIVsForBroadcasting
* Compute dimensions for allocating result memory
* Compute dimensions for allocating result memory (revised)
* Use static dimension for element-wise operation testcases
* Add a test for addition with broadcasting
* Missed Traits.h when merging
* Revise
* Update SharedWork.md
* Broadcasting for variadic operations
* Edit comments
* Update SharedWork.md
* Reorganize the code
* Add CHECK-LABEL for test_add_with_broadcasting
* Add reshape op handling.
* Lower reshape to KRNL dialect.
* Add comments.
* Propagate reshape to KRNL IR.
* Lower KRNL reshape to affine and standard ops level dialects.
* Add lowering of reshape operation to Krnl and LLVM Dialects.
* Add test for LLVM IR dialect output for reshape.
* Fix rebase.
* Fix test variable.
* Emit errors during reshape shape inference. Address other reviewer comments.
* Lower ONNXSumOp
* Add inferShapes() and test cases
* Load the first operand to the result
* Update SharingWork.md
* Update SharingWork.md
* Update SharingWork.md
* Add support for Max, Min
* Pass operation instead of location to mapToLowerScalarOp
* Add support for Elu, Selu, LeakyRelu, HardSigmoid
* Add test cases
* Update SharingWork.md
* Rewrite the part of lowering variadic ops and use it for binary ops
* Use two diffenrent templates for Unary and Variadic Ops
* Revise the code
* Canonicalization pattern for eliminating identity ops
* Add a test for the identity elimination rule
* Remove frontend from test
* Use CHECK-NEXT instead of CHECK
* generate op from onnx document
* Restore FullGemm
* update the op attribute for shape inference and canonicalizer
* Update onnx_canonicalization.mlir
* compartmentalize build script, temporarily remove dependency of onnf_opt on helper.cpp
* fix test includes
* fix op directory include
* compiler -> op
* compiler test depends on boost system
* fix function name
* specify libcompiler dependencies
* let cmake take care of transitive dependencies
* remove unnecessary includes
* use ONNF_SRC_ROOT and ONNF_BIN_ROOT
* allow whole-archive linked libraries to be appended
* [MLIR] Support filecheck (#371)
* support lit+FileCheck
* add lit into build script
* format MLIR.cmake
* format cmake
* [MLIR] Remove input/output ops (#372)
* remove input/output ops
* get output tensor type from symbol table
chentong
Alexandre Eichenberger
Gheorghe-Teodor Bercea
Tung D. Le
Tian Jin
Haruki Imai
Yasushi Negishi
Tian Jin