SharingWork.md

Please keep up to date.

• As you start working on a new operation, move it from the lower table to the top table,
• write your name under 'person working on it,'
• once you have pushed the functionality in the master branch, mark it as complete.

ONNX to KRNL work.

Update as you push code to the master branch.

ONNX Operation	Person working on it	ONNX Def	Shape Inference	ONNX to KRNL	Optimized	Extended Functionality
Abs		v	v	v
Add		v	v	v		M
And		v	v	v		M
AveragePool		v	v	v		P
BatchNorm		v	v	v
Constant		v	v	v
Conv		v	v	v		nP
Concat		v	v	v		V
Cos		v	v	v
Cosh		v	v	v
Div		v	v	v		M
Dropout		v	v
Elu		v	v	v
Erf		v	v
Exp		v	v	v
Expand		v	v
Gemm		v	v	v		U
GlobalAvgPool		v	v
GlobalLpPool		v	v
GlobalMaxPool		v	v
HardSigmoid		v	v	v
Identity		v	v	v
LeakyRelu		v	v	v
Log		v	v	v
MatMul		v	v	v		M
Max		v	v	v		M V
MaxPool		v	v	v		P
Min		v	v	v		M V
Mul		v	v	v		M
Or		v	v	v		M
Pad		v	V	v		const only
Pow		v	v			M
Reciprocal		v	v	v
ReduceMax		v	v	v
ReduceL1		v				be decomposed into ReduceSum and Abs
ReduceL2		v				be decomposed into ReduceSumSquare and Sqrt
ReduceLogSum		v				be decomposed into ReduceSum and Log
ReduceLogSumExp		v				be decomposed into ReduceLogSum and Exp
ReduceMean		v	v
ReduceMin		v	v	v
ReduceProd		v	v	v
ReduceSum		v	v	v
ReduceSumSquare		v				be decomposed into ReduceSum and Mul
Relu		v	v	v
Reshape		v	v	v		const only
Selu		v	v	v
Sigmoid		v	v	v
Sign		v	v	v
Sinh		v	v	v
Softmax		v	v	v
Softplus		v	v	v
Softsign		v	v	v
Sqrt		v	v	v
Sub		v	v	v		M
Sum		v	v	v		M V
Tanh		v	v	v
Transpose		v	v	v
Unsqueeze		v	v	v
Xor		v	v	v		M

Extended functionality info (noXXX: additional work is needed).

• M / noM: multi-broadcast imlemented / not implemented
• U / noU: unidir-broadcast implemented / not implemented
• P / noP: padding implemented / not implemented
• V / noV: variadic input implemented / non implemented

Operation not supported yet.

Please pick new operations to work on, and make sure you utilize current code to minimize additional work needed to support new functionality.

Typical sequence of work.

1. Ensure the operation is properly parsed from ONNX and MLIR input.
2. Perform shape inference.
3. Lower to KRNL dialect.

And add literal tests at each step, and end to end tests once completed.

ONNX Operation	Person working on it	ONNX Def	Shape Inference	ONNX to KRNL	Basic Functionality	Extended functionality
ACos
ACosh
ArgMax
ArgMin
ASin
ASinh
ATan
ATanh
BitShift						M
Ceil
Celu
Clip
Compress
ConcatFromSeq
ConstOfShape
ConvInt						P
ConvTrans						P
CumSum
DepthToSpace
DequantizeLin
Det
DynQuantizeLin
Einsum						V
Equal						M
EyeLike
Flatten
Floor
GRU
GatherElements
GatherND
Greater						M
GreaterOrEq						M
HardMax
If						V
InstanceNorm
Inverse
IsInf
IsNaN
Less						M
LessOrEq						M
LSTM
LSTM
LSTM
LogSoftmax
Loop						V
LpNorm
LpPool
MatMulInteger						U
MaxRoiPool
MaxUnpool
Mean						M V
MeanSquareDist						M
MeanVarNorm
Mod						M
Multinomial
Neg
NegLogLikeLoss
NonMaxSupress
NonZero
Not
OneHot
PRelu						U
QLinearConv						P
QLinearMatMul						M
QuantizeLinear
RNN
RandomNormal
RandNormalLike
RandUniform
RandUniformLike
Range
Resize
ReverseSequence
RoiAlign
Round
Scan						V
Scatter						deprecated for Scatter Element
ScatterElem
ScatterND
SequenceAt
SeqConstruct						V
SequenceErase
SequenceInsert
SequenceLength
Shape
Shrink
Slice
SoftMaxCross
SpaceToDepth
Split						V
SplitToSequence
Squeeze
StringNorm
Tan
TfldfVect
ThresholdRelu
Tile
TopK
UnfoldToDepth
Unique
Upsample						deprecated
Where						M