* 1.Break down CMake scripts to smaller libraries per-directory.
2. Move some transformations and interfaces to the right folder.
3. Fix minor merge failure of the patch renaming files to use LLVM convention.
* Link OMBuilder with OMONNXOps.
* 1. Update the src location of generated ONNX dialect definition.
2. Link OMONNXRewrite with OMONNXOps.
* Fix path to tablegen for add_onnx_mlir_dialect_doc.
* Update build script for onnx_mlir_transform.
* 1. Remove comment code.
2. onnx_mlir_attribute_promotion -> OMAttributePromotion.
* Name tablegen generated files with LLVM convention.
* Nit: reorder libraries to link against.
* Nit: Link against MLIR first.
* Support attribute promotion.
* Simplify op interface name.
* 1. Add more comments to Attribute Promotion Pass.
2. Move Promotable Const Operand Interface to src/interface, and link against it.
* Complete NFC change onnx -> onnx-mlir.
* Move attribute_promotion pass to src/transform.
* Nit: reword comment.
* Support Attribute Promotion in gen_doc.py.
* Add test.
* Update ONNX doc.
* Add negative test.
* Rename onnxop.inc -> onnx_ops.td.inc.
* Include onnx_ops.td.inc.
* Nit: better comments.
* Prettify CMake.
* Remove original attribute_promotion code, improve comments.
* Append '_op_interface' to op interface decl/defs.
* Namespace cmake targets using onnx_mlir_ prefix.
* Use updated header name.
* Use new body file name.
* Fix dependency.
* Use new CMake target name.
* Make attribute promotion self-contained by removing redundant constant operaions inside the pass execution.
* Remove canonicalization pass.
* Increase comments.
* Use stricter checks.
* Add one more test case.
* Remove %arg1 as it's never used.
* 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
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Fix reshape when a dynamic shape is given.
* Fix default attributes for ConvNoBias.
* Fix comment.
* Resolve comment.
* Improve checks.
* Handle zero dim case.
* Add helper to fetch constants. Add test for dynamic reshape.
* Add test for zero.
* Use shortcut method for size.
* Support Pads for MaxPoolSingleOut
* Regenerate onnx.md to include the new op
* Edit comments
* Undo redundant parts that were unintentionally changed
* Move declarative rewriting rules into canonicalize to avoid creating a new op
* Reformat the rewriting rule pattern of MaxPoolSingleOut
* Put ONNXPadConstantValuePadOp's build method into a .cpp file instead of a tablegen file
* Use the same helper function as the one in inferShape for the ONNXPadConstantValuePadOp's build method
* Change function names and fix padding for the spatial dimensions
* Call shape-inference again after canonicalization to infer shape for newly added ops during canonicalization.
* Fix typos
* emitConstantOp with a given type
* Helper functions to create infinity constants
* Use new constant helper functions for MaxPoolSingleOut
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Lower MaxPoolSingleOutOp to Krnl dialect
* Edit comments
* Update changes according to the new folder structure
* Add MLIR tests
* Support ceil_mode
* Merge the first two krnl loops into one krnl loop; remove attribute checks
* Dynamically allocate memory for the result if the result has unknown dimensions
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Remove rank constraints in gemm fusion
* Add an MLIR test
Co-authored-by: Tian Jin <tjingrant@gmail.com>
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* helper to gen krnl code, applied to conv
* suggested changes, name, removed set insertion point
* format
* suggested changes
* added comments and made a small name change
* 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 result type inference to op definition
* Edit MLIR tests
* Fix result type for Mul
* Format comments
* Return UnrankedTensorType as result type
* Just for testing -split-input-file
* Undo: Just for testing -split-input-file
* Extract a function, get_operand_ins, that gets operand types; rewrite gen_attr_ins function
* Generate custom builders
* Call existing build methods
* Add comments
* Minor changes
* Generate build methods with attributes
* Add support of variadic type
* Do not generate custom build methods for ops having only attributes
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* 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>
* Get memreftype for result types
* Revise
* Replace convertToMemRefType
* Use convertToMemRefType in ONNXConvNoBiasOpLowering
* Merge with the master branch
* Reverse an unintentional change
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
* Create two categories: elementwise and tensor
* typos
* Create directories for categories
* Edit comments
* Extract a function that creates a KrnlIterateOp
* Add comments
* Extract some common parts
* Revise softmax
* Add reduction.inc
* Move lower-frontend to lib/conversion
* Move directory to directory
* Change file/directory names
* Comment format
* Add matmul.inc
Tung D. Le
Gheorghe-Teodor Bercea
Tian Jin
chentong319
Alexandre Eichenberger
Gheorghe-Teodor Bercea