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onnx-mlir/SharingWork.md

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Create SharingWork.md (#394) Add tables to keep track of the work, initially with a section on ONNX to KRNL work. Add more sections as you see fit
2019-12-08 05:53:43 +08:00
# ONNX to KRNL work
Please add row in first table when adding a new ONNX operator, and move to second table when work is completed
ONNX operations for which some work is needed.
* M for multi-broadcast, U for unidir-broadcast
[MLIR] Add support for Max, Min, Sum, Elu, Selu, LeakyRelu, HardSigmoid (#395) * 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
2019-12-12 10:49:50 +08:00
| ONNX Oper | Person working on it | ONNX 2 KRNL | Basic functionality | Extended functionality (e.g. broadcast) |
| ---------- | --------------------- | -------------- | --------------------- | ---------------------------------------- |
[MLIR] Add broadcasting support for element wise operations (#398) * 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
2019-12-20 00:28:06 +08:00
| Add | Tung (updated) | v | v | M |
| And | Tung | v | v | M |
| Cosh | Tung | v | v | |
| Div | Tung | v | v | M |
[MLIR] Add support for Max, Min, Sum, Elu, Selu, LeakyRelu, HardSigmoid (#395) * 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
2019-12-12 10:49:50 +08:00
| Elu | Tung | v | v | |
| Exp | Tung | v | v | |
| FullGemm | | | | noU |
| Gemm | | | | noU |
| HardSigmoid | Tung | v | v | |
| LeakyRelu | Tung | v | v | |
| MatMul | | | | noM |
[MLIR] Add broadcasting support for element wise operations (#398) * 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
2019-12-20 00:28:06 +08:00
| Max | Tung | v | v | M |
| Min | Tung | v | v | M |
| Mul | Tung | v | v | M |
| Or | Tung | v | v | M |
[MLIR] Add support for Max, Min, Sum, Elu, Selu, LeakyRelu, HardSigmoid (#395) * 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
2019-12-12 10:49:50 +08:00
| Relu | Tung | v | v | |
| Selu | Tung | v | v | |
| Sigmoid | Tung | v | v | |
| Sinh | Tung | v | v | |
Lowering softmax (#14) * 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>
2020-01-21 10:57:32 +08:00
| Softmax | Tung | v | v | |
[MLIR] Add broadcasting support for element wise operations (#398) * 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
2019-12-20 00:28:06 +08:00
| Sub | Tung | v | v | M |
| Sum | Tung | v | v | M |
[MLIR] Add support for Max, Min, Sum, Elu, Selu, LeakyRelu, HardSigmoid (#395) * 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
2019-12-12 10:49:50 +08:00
| Tanh | Tung | v | v | |
[MLIR] Add broadcasting support for element wise operations (#398) * 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
2019-12-20 00:28:06 +08:00
| Xor | Tung | v | v | M |
Create SharingWork.md (#394) Add tables to keep track of the work, initially with a section on ONNX to KRNL work. Add more sections as you see fit
2019-12-08 05:53:43 +08:00
ONNX operations for which the work is completed (full functionality) and tested
[MLIR] Add support for Max, Min, Sum, Elu, Selu, LeakyRelu, HardSigmoid (#395) * 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
2019-12-12 10:49:50 +08:00
| ONNX Oper | Person working on it | Initial work | Basic functionality | Extended functionality (e.g. broadcast) |
| ---------- | ---------------------- | -------------- | --------------------- | ---------------------------------------- |