clean operands using names provided by operandAdaptors (#56)

* clean operands using names provided by operandAdaptors

* reverted changes that were erronerous, from Tung's comment

* clang format issues

src/Conversion/ONNXToKrnl/Math/Gemm.cpp

@@ -24,10 +24,11 @@ struct ONNXGemmOpLowering : public ConversionPattern {
     bool hasBias = !op->getOperand(2).getType().isa<NoneType>();
 
     Value A, B, C;
-    A = operands[0];
+    ONNXGemmOpOperandAdaptor operandAdaptor(operands);
-    B = operands[1];
+    A = operandAdaptor.A();
+    B = operandAdaptor.B();
     if (hasBias)
-      C = operands[2];
+      C = operandAdaptor.C();
 
     auto memRefType = convertToMemRefType(*op->result_type_begin());
 

src/Conversion/ONNXToKrnl/Math/MatMul.cpp

@@ -21,8 +21,9 @@ struct ONNXMatMulOpLowering : public ConversionPattern {
                   ConversionPatternRewriter &rewriter) const final {
     auto loc = op->getLoc();
 
-    Value A = operands[0];
+    ONNXMatMulOpOperandAdaptor operandAdaptor(operands);
-    Value B = operands[1];
+    Value A = operandAdaptor.A();
+    Value B = operandAdaptor.B();
     auto AShape = A.getType().cast<MemRefType>().getShape();
     auto BShape = B.getType().cast<MemRefType>().getShape();
 

src/Conversion/ONNXToKrnl/Math/Softmax.cpp

@@ -15,8 +15,7 @@ using namespace mlir;
 struct ONNXSoftmaxOpLowering : public ConversionPattern {
   ONNXSoftmaxOpLowering(MLIRContext *ctx)
       : ConversionPattern(mlir::ONNXSoftmaxOp::getOperationName(), 1, ctx) {}
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
     // softmax(x) = let max_x = max(x) in
     //                let exp_x = exp(x - max_x) in
@@ -29,7 +28,8 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
     assert(axis >= -rank && axis <= rank - 1);
 
     auto loc = op->getLoc();
-
+    ONNXSoftmaxOpOperandAdaptor operandAdaptor(operands);
+    Value input = operandAdaptor.input();
     // Insert an allocation and deallocation for the result of this operation.
     auto elementType = memRefType.getElementType();
 
@@ -38,8 +38,8 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
     if (hasAllConstantDimensions(memRefType))
       alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
     else
-      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
+      alloc = insertAllocAndDealloc(
-                                    operands[0]);
+          memRefType, loc, rewriter, insertDealloc, input);
 
     // Shape of the result
     auto memRefShape = memRefType.getShape();
@@ -49,15 +49,14 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
     Value sumOp = insertAllocAndDealloc(scalarMemRefType, loc, rewriter, true);
     Value maxOp = insertAllocAndDealloc(scalarMemRefType, loc, rewriter, true);
     Value zero = emitConstantOp(rewriter, loc, elementType, 0);
-    Value negInfinity = rewriter.create<ConstantOp>(
+    Value negInfinity = rewriter.create<ConstantOp>(loc,
-        loc,
         FloatAttr::get(elementType, -std::numeric_limits<float>::infinity()));
 
     // Define loops.
     std::vector<Value> originalLoops;
     std::vector<Value> optimizedLoops;
-    Block *optimizationBlock = defineLoops(rewriter, loc, originalLoops,
+    Block *optimizationBlock =
-            optimizedLoops, rank);
+        defineLoops(rewriter, loc, originalLoops, optimizedLoops, rank);
 
     // Coerce the input into a 2-D tensor. `axis` will be the coercing point.
     // This coercing follows the softmax definition in ONNX:
@@ -75,7 +74,7 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
     }
     KrnlIterateOperandPack outerPack(rewriter, outerLoops, optimizedOuterLoops);
     for (int i = 0; i < axis; ++i)
-      addDimensionToPack(rewriter, loc, outerPack, operands[0], i);
+      addDimensionToPack(rewriter, loc, outerPack, input, i);
 
     // Define an inner loop with respect to axis.
     std::vector<Value> innerLoops, optimizedInnerLoops;
@@ -87,7 +86,7 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
     }
     KrnlIterateOperandPack innerPack(rewriter, innerLoops, optimizedInnerLoops);
     for (int i = axis; i < rank; ++i)
-      addDimensionToPack(rewriter, loc, innerPack, operands[0], i);
+      addDimensionToPack(rewriter, loc, innerPack, input, i);
 
     KrnlIterateOp outerIterateOp, maxIterateOp, sumIterateOp, softmaxIterateOp;
     SmallVector<Value, 4> outerLoopIVs;
@@ -144,7 +143,7 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
 
     // Compute the max value.
     Value max = rewriter.create<LoadOp>(loc, maxOp);
-    Value nextMax = rewriter.create<LoadOp>(loc, operands[0], maxLoopIVs);
+    Value nextMax = rewriter.create<LoadOp>(loc, input, maxLoopIVs);
     auto maxCond =
         rewriter.create<CmpFOp>(loc, CmpFPredicate::OGT, max, nextMax);
     max = rewriter.create<SelectOp>(loc, maxCond, max, nextMax);
@@ -167,7 +166,7 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
 
     // Sum up values.
     Value sum = rewriter.create<LoadOp>(loc, sumOp);
-    Value next = rewriter.create<LoadOp>(loc, operands[0], sumLoopIVs);
+    Value next = rewriter.create<LoadOp>(loc, input, sumLoopIVs);
     Value sub = rewriter.create<SubFOp>(loc, next, max);
     Value exp = rewriter.create<ExpOp>(loc, sub);
     sum = rewriter.create<AddFOp>(loc, sum, exp);

src/Conversion/ONNXToKrnl/NN/Conv.cpp

@@ -26,12 +26,6 @@ struct ONNXConvOpLowering : public ConversionPattern {
     bool insertDealloc = checkInsertDealloc(op);
     ONNXConvOp convOp = llvm::dyn_cast<ONNXConvOp>(op);
 
-    if (hasAllConstantDimensions(memRefType))
-      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
-    else
-      alloc = insertAllocAndDealloc(
-          memRefType, loc, rewriter, insertDealloc, {operands[0]});
-
     auto resultShape = memRefType.getShape();
     auto inputOperand = operandAdaptor.X();
     auto inputShape = inputOperand.getType().cast<MemRefType>().getShape();
@@ -40,6 +34,12 @@ struct ONNXConvOpLowering : public ConversionPattern {
     auto biasOperand = operandAdaptor.B();
     bool hasBias = !biasOperand.getType().isa<NoneType>();
 
+    if (hasAllConstantDimensions(memRefType))
+      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
+    else
+      alloc = insertAllocAndDealloc(
+          memRefType, loc, rewriter, insertDealloc, {inputOperand});
+
     // R = Conv(D, K)
     //
     // The input/output shapes will look like this:

src/Conversion/ONNXToKrnl/NN/Normalization.cpp

@@ -1,4 +1,4 @@
-//===----------- Normalization.cpp - Lowering Normalization Ops ------------===//
+//===----------- Normalization.cpp - Lowering Normalization Ops -----------===//
 //
 // Copyright 2019 The IBM Research Authors.
 //
@@ -18,24 +18,24 @@ struct ONNXBatchNormalizationTestModeOpLowering : public ConversionPattern {
             mlir::ONNXBatchNormalizationTestModeOp::getOperationName(), 1,
             ctx) {}
   PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-      ConversionPatternRewriter & rewriter) const final {
+      ConversionPatternRewriter &rewriter) const final {
     // batchnorm{epsilon}(x, scale, bias, mean, variance) =
     //      scale * (x - mean) / sqrt(variance + epsilon) + bias
+    ONNXBatchNormalizationTestModeOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
 
     auto memRefType = convertToMemRefType(*op->result_type_begin());
-    auto epsilonAttr =
+    auto epsilonAttr = FloatAttr::get(memRefType.getElementType(),
-        FloatAttr::get(memRefType.getElementType(),
         llvm::dyn_cast<ONNXBatchNormalizationTestModeOp>(op)
             .epsilon()
             .convertToFloat());
     auto epsilon = rewriter.create<ConstantOp>(loc, epsilonAttr);
 
-    auto operand = operands[0];
+    auto operand = operandAdaptor.X();
-    auto scale = operands[1];
+    auto scale = operandAdaptor.scale();
-    auto bias = operands[2];
+    auto bias = operandAdaptor.B();
-    auto mean = operands[3];
+    auto mean = operandAdaptor.mean();
-    auto variance = operands[4];
+    auto variance = operandAdaptor.var();
 
     // Insert an allocation and deallocation for the result of this operation.
     Value alloc;
@@ -44,8 +44,8 @@ struct ONNXBatchNormalizationTestModeOpLowering : public ConversionPattern {
     if (hasAllConstantDimensions(memRefType))
       alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
     else
-      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
+      alloc = insertAllocAndDealloc(
-                                    {operand});
+          memRefType, loc, rewriter, insertDealloc, {operand});
 
     // Operand's dimensions can be in the form of NxCxD1xD2x...xDn or N.
     // In case of N, C is assumed to be 1.
@@ -67,8 +67,8 @@ struct ONNXBatchNormalizationTestModeOpLowering : public ConversionPattern {
 
     SmallVector<Value, 1> loopCIVs;
     if (rank > 1) {
-      KrnlIterateOperandPack cPack(rewriter, originalLoops[1],
+      KrnlIterateOperandPack cPack(
-                                   optimizedLoops[1]);
+          rewriter, originalLoops[1], optimizedLoops[1]);
       addDimensionToPack(rewriter, loc, cPack, operand, 1);
       auto cIterateOp = rewriter.create<KrnlIterateOp>(loc, cPack);
       Block &cIterationBlock = cIterateOp.bodyRegion().front();

src/Conversion/ONNXToKrnl/NN/Pooling.cpp

@@ -38,6 +38,7 @@ struct ONNXMaxPoolSingleOutOpLowering : public ConversionPattern {
 
   PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
+    ONNXMaxPoolSingleOutOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
 
     // Match
@@ -71,7 +72,7 @@ struct ONNXMaxPoolSingleOutOpLowering : public ConversionPattern {
       dilations.emplace_back(dilation.cast<IntegerAttr>().getInt());
 
     // Type information about the input and result of this operation.
-    auto &inputOperand = operands[0];
+    auto inputOperand = operandAdaptor.X();
     auto inputShape = inputOperand.getType().cast<MemRefType>().getShape();
     auto memRefType = convertToMemRefType(*op->result_type_begin());
     auto resultShape = memRefType.getShape();

src/Conversion/ONNXToKrnl/Tensor/Identity.cpp

@@ -16,10 +16,10 @@ struct ONNXIdentityOpLowering : public ConversionPattern {
   ONNXIdentityOpLowering(MLIRContext *ctx)
       : ConversionPattern(mlir::ONNXIdentityOp::getOperationName(), 1, ctx) {}
 
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
-    rewriter.replaceOp(op, operands[0]);
+    ONNXIdentityOpOperandAdaptor operandAdaptor(operands);
+    rewriter.replaceOp(op, operandAdaptor.input());
     return matchSuccess();
   }
 };

src/Conversion/ONNXToKrnl/Tensor/PadConstantValuePad.cpp

@@ -14,13 +14,13 @@ using namespace mlir;
 
 struct ONNXPadConstantValuePadOpLowering : public ConversionPattern {
   ONNXPadConstantValuePadOpLowering(MLIRContext *ctx)
-      : ConversionPattern(mlir::ONNXPadConstantValuePadOp::getOperationName(),
+      : ConversionPattern(
-                          1, ctx) {}
+            mlir::ONNXPadConstantValuePadOp::getOperationName(), 1, ctx) {}
 
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
     auto tensorType = (*op->result_type_begin());
+    ONNXPadConstantValuePadOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
 
     // Only constant padding is supported now.
@@ -55,7 +55,7 @@ struct ONNXPadConstantValuePadOpLowering : public ConversionPattern {
     BuildKrnlLoop valueLoops(rewriter, loc, rank);
     valueLoops.createDefineAndOptimizeOp();
     for (int i = 0; i < rank; ++i)
-      valueLoops.pushBounds(0, operands[0], i);
+      valueLoops.pushBounds(0, operandAdaptor.data(), i);
     valueLoops.createIterateOp();
 
     // Copy the input data into the output.
@@ -67,7 +67,7 @@ struct ONNXPadConstantValuePadOpLowering : public ConversionPattern {
 
     auto pads = llvm::dyn_cast<ONNXPadConstantValuePadOp>(op).pads();
     SmallVector<int64_t, 4> pad_begin;
-    for (int i = 0; i < pads.size()/2; ++i) {
+    for (int i = 0; i < pads.size() / 2; ++i) {
       pad_begin.emplace_back(pads.getValue()[i].cast<IntegerAttr>().getInt());
     }
 
@@ -77,14 +77,14 @@ struct ONNXPadConstantValuePadOpLowering : public ConversionPattern {
       if (pad_begin[i] == 0) {
         outLoopIVs.emplace_back(valueLoops.getInductionVar(i));
       } else {
-        auto outIV = rewriter.create<AddIOp>(
+        auto outIV = rewriter.create<AddIOp>(loc,
-            loc, rewriter.create<ConstantIndexOp>(loc, pad_begin[i]),
+            rewriter.create<ConstantIndexOp>(loc, pad_begin[i]),
             valueLoops.getInductionVar(i));
         outLoopIVs.emplace_back(outIV);
       }
     }
 
-    auto inVal = rewriter.create<LoadOp>(loc, operands[0], inLoopIVs);
+    auto inVal = rewriter.create<LoadOp>(loc, operandAdaptor.data(), inLoopIVs);
     rewriter.create<StoreOp>(loc, inVal, alloc, outLoopIVs);
     rewriter.setInsertionPointToStart(padLoops.getIterateBlock());
 

src/Conversion/ONNXToKrnl/Tensor/Reshape.cpp

@@ -16,11 +16,12 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
   ONNXReshapeOpLowering(MLIRContext *ctx)
       : ConversionPattern(mlir::ONNXReshapeOp::getOperationName(), 1, ctx) {}
 
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
-    auto inputShape = operands[0].getType().cast<MemRefType>().getShape();
+    ONNXReshapeOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
+    Value data = operandAdaptor.data();
+    auto inputShape = data.getType().cast<MemRefType>().getShape();
 
     // Insert an allocation and deallocation for the result of this operation.
     auto memRefType = convertToMemRefType(*op->result_type_begin());
@@ -33,7 +34,7 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
     for (int i = 0; i < inputShape.size(); ++i) {
       Value dimVal;
       if (inputShape[i] < 0) {
-        Value dim = rewriter.create<DimOp>(loc, operands[0], i);
+        Value dim = rewriter.create<DimOp>(loc, data, i);
         dimVal =
             rewriter.create<IndexCastOp>(loc, dim, rewriter.getIntegerType(64));
       } else {
@@ -61,8 +62,7 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
           rewriter.getIntegerType(64), getMemRefEltSizeInBytes(memRefType));
       SmallVector<Value, 4> DimInfo;
       for (int i = 0; i < memRefShape.size(); ++i) {
-        Value index =
+        Value index = emitConstantOp(rewriter, loc, rewriter.getIndexType(), i);
-            emitConstantOp(rewriter, loc, rewriter.getIndexType(), i);
         // Load index from array of indices.
         Value loadedVal = rewriter.create<LoadOp>(loc, operands[1], index);
         // If a dimension is zero, the actual dimension value is taken from the
@@ -75,7 +75,7 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
           Value dimVal;
           auto loadedValType = loadedVal.getType().cast<IntegerType>();
           if (inputShape[i] < 0) {
-            Value dim = rewriter.create<DimOp>(loc, operands[0], i);
+            Value dim = rewriter.create<DimOp>(loc, data, i);
             dimVal = rewriter.create<IndexCastOp>(loc, dim, loadedValType);
           } else {
             dimVal =
@@ -136,7 +136,7 @@ struct ONNXReshapeOpLowering : public ConversionPattern {
       alloc = allocateMemref;
     }
 
-    rewriter.create<KrnlMemcpyOp>(loc, alloc, operands[0], tensorSize);
+    rewriter.create<KrnlMemcpyOp>(loc, alloc, data, tensorSize);
     rewriter.replaceOp(op, alloc);
 
     return matchSuccess();

src/Conversion/ONNXToKrnl/Tensor/Transpose.cpp

@@ -16,20 +16,21 @@ struct ONNXTransposeOpLowering : public ConversionPattern {
   ONNXTransposeOpLowering(MLIRContext *ctx)
       : ConversionPattern(mlir::ONNXTransposeOp::getOperationName(), 1, ctx) {}
 
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
+    ONNXTransposeOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
     // Insert an allocation and deallocation for the result of this operation.
     auto memRefType = convertToMemRefType(*op->result_type_begin());
     Value alloc;
     bool insertDealloc = checkInsertDealloc(op);
+    Value data = operandAdaptor.data();
 
     if (hasAllConstantDimensions(memRefType))
       alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
     else
-      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
+      alloc = insertAllocAndDealloc(
-                                    {operands[0]});
+          memRefType, loc, rewriter, insertDealloc, {data});
 
     // Number of loops
     auto memRefShape = memRefType.getShape();
@@ -38,13 +39,13 @@ struct ONNXTransposeOpLowering : public ConversionPattern {
     // Define loops.
     std::vector<Value> originalLoops;
     std::vector<Value> optimizedLoops;
-    Block *optimizationBlock = defineLoops(rewriter, loc, originalLoops,
+    Block *optimizationBlock =
-        optimizedLoops, rank);
+        defineLoops(rewriter, loc, originalLoops, optimizedLoops, rank);
 
     KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
     // Iterate over the loop nest using the input shape.
     for (int i = 0; i < rank; ++i)
-      addDimensionToPack(rewriter, loc, pack, operands[0], i);
+      addDimensionToPack(rewriter, loc, pack, data, i);
 
     auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
     Block &iterationBlock = iterateOp.bodyRegion().front();
@@ -75,7 +76,7 @@ struct ONNXTransposeOpLowering : public ConversionPattern {
       // the default case). This means that perm was added by shape
       // inference or another pass to contain the values corresponding
       // to the default behavior of Transpose.
-      for (int i = iterationBlock.getArguments().size()-1; i >= 0; i--)
+      for (int i = iterationBlock.getArguments().size() - 1; i >= 0; i--)
         perm.emplace_back(i);
     }
 
@@ -84,10 +85,10 @@ struct ONNXTransposeOpLowering : public ConversionPattern {
       inLoopIVs.emplace_back(arg);
 
     SmallVector<Value, 4> outLoopIVs;
-    for (int i=0; i<iterationBlock.getArguments().size(); ++i)
+    for (int i = 0; i < iterationBlock.getArguments().size(); ++i)
       outLoopIVs.emplace_back(iterationBlock.getArguments()[perm[i]]);
 
-    auto inVal = rewriter.create<LoadOp>(loc, operands[0], inLoopIVs);
+    auto inVal = rewriter.create<LoadOp>(loc, data, inLoopIVs);
     rewriter.create<StoreOp>(loc, inVal, alloc, outLoopIVs);
 
     rewriter.replaceOp(op, alloc);

src/Conversion/ONNXToKrnl/Tensor/Unsqueeze.cpp

@@ -16,12 +16,13 @@ struct ONNXUnsqueezeOpLowering : public ConversionPattern {
   ONNXUnsqueezeOpLowering(MLIRContext *ctx)
       : ConversionPattern(mlir::ONNXUnsqueezeOp::getOperationName(), 1, ctx) {}
 
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const final {
+    ONNXUnsqueezeOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
     auto memRefType = convertToMemRefType(*op->result_type_begin());
     int outRank = memRefType.getRank();
+    Value data = operandAdaptor.data();
 
     // Assume that `axes` has been validated by shape inference.
     // So, here we just get it.
@@ -55,7 +56,7 @@ struct ONNXUnsqueezeOpLowering : public ConversionPattern {
       for (int outIdx = 0, inIdx = 0; outIdx < memRefShape.size(); ++outIdx) {
         Value dimVal = nullptr;
         if (memRefShape[outIdx] < 0) {
-          Value index = rewriter.create<DimOp>(loc, operands[0], inIdx);
+          Value index = rewriter.create<DimOp>(loc, data, inIdx);
           dimVal = rewriter.create<IndexCastOp>(
               loc, index, rewriter.getIntegerType(64));
           allocOperands.emplace_back(index);
@@ -74,7 +75,7 @@ struct ONNXUnsqueezeOpLowering : public ConversionPattern {
         dealloc.getOperation()->moveBefore(&parentBlock->back());
       }
     }
-    rewriter.create<KrnlMemcpyOp>(loc, alloc, operands[0], tensorSize);
+    rewriter.create<KrnlMemcpyOp>(loc, alloc, data, tensorSize);
     rewriter.replaceOp(op, alloc);
     return matchSuccess();
   }

src/Transform/LowerToLLVM.cpp

@@ -28,9 +28,7 @@ using namespace mlir;
 namespace {
 
 static FlatSymbolRefAttr getOrInsertExternFunc(StringRef funcName,
-                                               ModuleOp module,
+    ModuleOp module, mlir::LLVM::LLVMType funcType, PatternRewriter &rewriter) {
-                                               mlir::LLVM::LLVMType funcType,
-                                               PatternRewriter &rewriter) {
   auto *context = module.getContext();
   if (module.lookupSymbol<LLVM::LLVMFuncOp>(funcName)) {
     auto symbolRef = SymbolRefAttr::get(funcName, context);
@@ -71,10 +69,10 @@ public:
   explicit KrnlMemcpyOpLowering(MLIRContext *context)
       : ConversionPattern(KrnlMemcpyOp::getOperationName(), 1, context) {}
 
-  PatternMatchResult
+  PatternMatchResult matchAndRewrite(Operation *op, ArrayRef<Value> operands,
-  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
       ConversionPatternRewriter &rewriter) const override {
     auto *context = op->getContext();
+    KrnlMemcpyOpOperandAdaptor operandAdaptor(operands);
     auto loc = op->getLoc();
     auto *llvmDialect =
         op->getContext()->getRegisteredDialect<LLVM::LLVMDialect>();
@@ -85,33 +83,37 @@ public:
     auto memcpyRef = getOrInsertMemcpy(rewriter, parentModule, llvmDialect);
 
     // First operand.
-    Type dstType =
+    Type dstType = operandAdaptor.dest()
-        operands[0].getType().cast<LLVM::LLVMType>().getStructElementType(1);
+                       .getType()
+                       .cast<LLVM::LLVMType>()
+                       .getStructElementType(1);
     Value alignedDstMemory = rewriter.create<LLVM::ExtractValueOp>(
-        loc, dstType, operands[0], rewriter.getI64ArrayAttr(1));
+        loc, dstType, operandAdaptor.dest(), rewriter.getI64ArrayAttr(1));
     Value alignedInt8PtrDstMemory = rewriter.create<LLVM::BitcastOp>(
         loc, LLVM::LLVMType::getInt8PtrTy(llvmDialect), alignedDstMemory);
 
     // Second operand.
-    Type srcType =
+    Type srcType = operandAdaptor.src()
-        operands[1].getType().cast<LLVM::LLVMType>().getStructElementType(1);
+                       .getType()
+                       .cast<LLVM::LLVMType>()
+                       .getStructElementType(1);
     Value alignedSrcMemory = rewriter.create<LLVM::ExtractValueOp>(
-        loc, srcType, operands[1], rewriter.getI64ArrayAttr(1));
+        loc, srcType, operandAdaptor.src(), rewriter.getI64ArrayAttr(1));
     Value alignedInt8PtrSrcMemory = rewriter.create<LLVM::BitcastOp>(
         loc, LLVM::LLVMType::getInt8PtrTy(llvmDialect), alignedSrcMemory);
 
     // Size.
     Value int64Size = rewriter.create<LLVM::SExtOp>(
-        loc, LLVM::LLVMType::getInt64Ty(llvmDialect), operands[2]);
+        loc, LLVM::LLVMType::getInt64Ty(llvmDialect), operandAdaptor.size());
 
     // Is volatile (set to false).
-    Value isVolatile = rewriter.create<LLVM::ConstantOp>(
+    Value isVolatile = rewriter.create<LLVM::ConstantOp>(loc,
-        loc, LLVM::LLVMType::getInt1Ty(llvmDialect),
+        LLVM::LLVMType::getInt1Ty(llvmDialect),
         rewriter.getIntegerAttr(rewriter.getIntegerType(1), 0));
 
     // Memcpy call
-    rewriter.create<CallOp>(
+    rewriter.create<CallOp>(loc, memcpyRef,
-        loc, memcpyRef, LLVM::LLVMType::getVoidTy(llvmDialect),
+        LLVM::LLVMType::getVoidTy(llvmDialect),
         ArrayRef<Value>({alignedInt8PtrDstMemory, alignedInt8PtrSrcMemory,
             int64Size, isVolatile}));
 
@@ -123,8 +125,7 @@ private:
   /// Return a symbol reference to the memcpy function, inserting it into the
   /// module if necessary.
   static FlatSymbolRefAttr getOrInsertMemcpy(PatternRewriter &rewriter,
-                                             ModuleOp module,
+      ModuleOp module, LLVM::LLVMDialect *llvmDialect) {
-                                             LLVM::LLVMDialect *llvmDialect) {
     auto *context = module.getContext();
     if (module.lookupSymbol<LLVM::LLVMFuncOp>("llvm.memcpy.p0i8.p0i8.i64"))
       return SymbolRefAttr::get("llvm.memcpy.p0i8.p0i8.i64", context);
@@ -134,8 +135,7 @@ private:
     auto llvmI8PtrTy = LLVM::LLVMType::getInt8PtrTy(llvmDialect);
     auto llvmI64Ty = LLVM::LLVMType::getInt64Ty(llvmDialect);
     auto llvmI1Ty = LLVM::LLVMType::getInt1Ty(llvmDialect);
-    auto llvmFnType = LLVM::LLVMType::getFunctionTy(
+    auto llvmFnType = LLVM::LLVMType::getFunctionTy(llvmVoidTy,
-        llvmVoidTy,
         ArrayRef<mlir::LLVM::LLVMType>(
             {llvmI8PtrTy, llvmI8PtrTy, llvmI64Ty, llvmI1Ty}),
         false);
@@ -143,8 +143,8 @@ private:
     // Insert the memcpy function into the body of the parent module.
     PatternRewriter::InsertionGuard insertGuard(rewriter);
     rewriter.setInsertionPointToStart(module.getBody());
-    rewriter.create<LLVM::LLVMFuncOp>(module.getLoc(),
+    rewriter.create<LLVM::LLVMFuncOp>(
-                                      "llvm.memcpy.p0i8.p0i8.i64", llvmFnType);
+        module.getLoc(), "llvm.memcpy.p0i8.p0i8.i64", llvmFnType);
     return SymbolRefAttr::get("llvm.memcpy.p0i8.p0i8.i64", context);
   }
 };
@@ -186,8 +186,8 @@ public:
     }
   };
 
-  PatternMatchResult matchAndRewrite(KrnlEntryPointOp op,
+  PatternMatchResult matchAndRewrite(
-                                     PatternRewriter &rewriter) const override {
+      KrnlEntryPointOp op, PatternRewriter &rewriter) const override {
 
     auto *llvmDialect =
         op.getContext()->getRegisteredDialect<LLVM::LLVMDialect>();
@@ -261,8 +261,8 @@ public:
 
       // Fill in the memref underlying ptrToMemRef with information extracted
       // from dynMemRef.
-      fillPtrToMemRefWithDynMemRef(dynMemRef, ptrToMemRef, rewriter, loc,
+      fillPtrToMemRefWithDynMemRef(
-                                   apiRegistry, llvmDialect);
+          dynMemRef, ptrToMemRef, rewriter, loc, apiRegistry, llvmDialect);
 
       // ptrToMemRef will be an input to main computation graph function.
       staticInputs.emplace_back(ptrToMemRef);
@@ -273,14 +273,14 @@ public:
     assert(numOutputs == 1 && "only support 1 output tensor now.");
 
     // Call static entry point with the memref ptrs created, and get output.
-    auto outputMemRefs = rewriter.create<LLVM::CallOp>(
+    auto outputMemRefs = rewriter.create<LLVM::CallOp>(loc,
-        loc, staticEntryPointTy.getFunctionResultType(),
+        staticEntryPointTy.getFunctionResultType(),
         rewriter.getSymbolRefAttr(wrappedStaticEntryPointFuncName),
         staticInputs);
 
     // Create wrapped output.
-    auto wrappedOutput = callApi(rewriter, loc, apiRegistry,
+    auto wrappedOutput = callApi(
-                                 API::CREATE_ORDERED_DYN_MEM_REF_DICT, {});
+        rewriter, loc, apiRegistry, API::CREATE_ORDERED_DYN_MEM_REF_DICT, {});
 
     // Get the first memref returned, convert to a dynamic memref and store
     // it in the wrapped Output.
@@ -291,16 +291,16 @@ public:
         loc, int32Ty, rewriter.getI32IntegerAttr(outMemRefRank));
     auto outDynMemRef = callApi(rewriter, loc, apiRegistry,
         API::CREATE_DYN_MEM_REF, {outMemRefRankVal});
-    fillDynMemRefWithMemRef(outMemRef, outDynMemRef, rewriter, loc, apiRegistry,
+    fillDynMemRefWithMemRef(
-                            llvmDialect);
+        outMemRef, outDynMemRef, rewriter, loc, apiRegistry, llvmDialect);
     auto zero = rewriter.create<LLVM::ConstantOp>(
         loc, int32Ty, rewriter.getI32IntegerAttr(0));
     callApi(rewriter, loc, apiRegistry, API::SET_DYN_MEM_REF,
         {wrappedOutput, zero, outDynMemRef});
 
     // Return wrapped output.
-    rewriter.create<LLVM::ReturnOp>(loc,
+    rewriter.create<LLVM::ReturnOp>(
-                                    SmallVector<Value, 1>({wrappedOutput}));
+        loc, SmallVector<Value, 1>({wrappedOutput}));
     return matchSuccess();
   }
 
@@ -335,8 +335,8 @@ private:
     // identities to a symbol reference to the API function.
     ApiRegistry registry;
     for (auto &apiSpec : apiSpecs) {
-      apiSpec.symbolRef = getOrInsertExternFunc(apiSpec.name, module,
+      apiSpec.symbolRef = getOrInsertExternFunc(
-                                                apiSpec.funcTy(), rewriter);
+          apiSpec.name, module, apiSpec.funcTy(), rewriter);
       registry.emplace(apiSpec.id, apiSpec);
     }
 
@@ -347,9 +347,9 @@ private:
   // returned, otherwise return nullptr.
   Value callApi(PatternRewriter &rewriter, Location loc, ApiRegistry registry,
       API apiId, ArrayRef<Value> params) const {
-    auto returnVals = rewriter.create<LLVM::CallOp>(
+    auto returnVals =
-        loc, registry.at(apiId).outputTy, registry.at(apiId).symbolRef,
+        rewriter.create<LLVM::CallOp>(loc, registry.at(apiId).outputTy,
-        ArrayRef<Value>(params));
+            registry.at(apiId).symbolRef, ArrayRef<Value>(params));
     if (returnVals.getNumResults() == 1)
       return returnVals.getResult(0);
     return nullptr;
@@ -370,8 +370,7 @@ private:
   }
 
   void fillPtrToMemRefWithDynMemRef(Value &dynMemRef, Value &ptrToMemRef,
-                                    PatternRewriter &rewriter,
+      PatternRewriter &rewriter, const Location &loc,
-                                    const Location &loc,
       const std::map<API, ApiSpec> &apiRegistry,
       LLVM::LLVMDialect *llvmDialect) const {
     auto memRefPtrTy = ptrToMemRef.getType().dyn_cast<LLVM::LLVMType>();
@@ -385,18 +384,15 @@ private:
         callApi(rewriter, loc, apiRegistry, API::GET_DATA, {dynMemRef});
     dataPtr = rewriter.create<LLVM::BitcastOp>(
         loc, memRefTy.getStructElementType(0), dataPtr);
-    memRef = rewriter.create<LLVM::InsertValueOp>(
+    memRef = rewriter.create<LLVM::InsertValueOp>(loc, memRefTy, memRef,
-        loc, memRefTy, memRef, dataPtr,
+        dataPtr, rewriter.getArrayAttr({rewriter.getI32IntegerAttr(0)}));
-        rewriter.getArrayAttr({rewriter.getI32IntegerAttr(0)}));
+    memRef = rewriter.create<LLVM::InsertValueOp>(loc, memRefTy, memRef,
-    memRef = rewriter.create<LLVM::InsertValueOp>(
+        dataPtr, rewriter.getArrayAttr({rewriter.getI32IntegerAttr(1)}));
-        loc, memRefTy, memRef, dataPtr,
-        rewriter.getArrayAttr({rewriter.getI32IntegerAttr(1)}));
 
     // Use zero offset now.
     auto zero = rewriter.create<LLVM::ConstantOp>(
         loc, int64Ty, rewriter.getI64IntegerAttr(0));
-    memRef = rewriter.create<LLVM::InsertValueOp>(
+    memRef = rewriter.create<LLVM::InsertValueOp>(loc, memRefTy, memRef, zero,
-        loc, memRefTy, memRef, zero,
         rewriter.getArrayAttr({rewriter.getI32IntegerAttr(2)}));
 
     // Get rank, sizes array ptr and strides array ptr.
@@ -411,24 +407,22 @@ private:
           loc, int64Ty, rewriter.getI64IntegerAttr(i));
 
       // Insert size of the dimension.
-      auto dimSizePtr = rewriter.create<LLVM::GEPOp>(
+      auto dimSizePtr = rewriter.create<LLVM::GEPOp>(loc,
-          loc, int64Ty.getPointerTo(), sizesArrayPtr,
+          int64Ty.getPointerTo(), sizesArrayPtr, ArrayRef<Value>({dimIdx}));
-          ArrayRef<Value>({dimIdx}));
+      auto dimSize = rewriter.create<LLVM::LoadOp>(
-      auto dimSize = rewriter.create<LLVM::LoadOp>(loc, int64Ty.getPointerTo(),
+          loc, int64Ty.getPointerTo(), dimSizePtr);
-                                                   dimSizePtr);
+      memRef = rewriter.create<LLVM::InsertValueOp>(loc, memRefTy, memRef,
-      memRef = rewriter.create<LLVM::InsertValueOp>(
+          dimSize,
-          loc, memRefTy, memRef, dimSize,
           rewriter.getArrayAttr(
               {rewriter.getI64IntegerAttr(3), rewriter.getI64IntegerAttr(i)}));
 
       // Insert stride of the dimension.
-      auto dimStridePtr = rewriter.create<LLVM::GEPOp>(
+      auto dimStridePtr = rewriter.create<LLVM::GEPOp>(loc,
-          loc, int64Ty.getPointerTo(), sizesArrayPtr,
+          int64Ty.getPointerTo(), sizesArrayPtr, ArrayRef<Value>({dimIdx}));
-          ArrayRef<Value>({dimIdx}));
       auto dimStride = rewriter.create<LLVM::LoadOp>(
           loc, int64Ty.getPointerTo(), dimStridePtr);
-      memRef = rewriter.create<LLVM::InsertValueOp>(
+      memRef = rewriter.create<LLVM::InsertValueOp>(loc, memRefTy, memRef,
-          loc, memRefTy, memRef, dimStride,
+          dimStride,
           rewriter.getArrayAttr(
               {rewriter.getI64IntegerAttr(4), rewriter.getI64IntegerAttr(i)}));
     }
@@ -444,8 +438,8 @@ private:
     auto int64Ty = LLVM::LLVMType::getInt64Ty(llvmDialect);
 
     // Extract the data pointer, and record it in dynamic mem ref created.
-    Value outMemRefDataPtr = rewriter.create<LLVM::ExtractValueOp>(
+    Value outMemRefDataPtr = rewriter.create<LLVM::ExtractValueOp>(loc,
-        loc, outMemRefTy.getStructElementType(0), outMemRef,
+        outMemRefTy.getStructElementType(0), outMemRef,
         rewriter.getArrayAttr({rewriter.getI64IntegerAttr(0)}));
     outMemRefDataPtr = rewriter.create<LLVM::BitcastOp>(
         loc, LLVM::LLVMType::getInt8PtrTy(llvmDialect), outMemRefDataPtr);
@@ -463,23 +457,21 @@ private:
           loc, int64Ty, rewriter.getI64IntegerAttr(i));
 
       // Transfer size of dimension from memref to dynamic memref.
-      auto dimSize = rewriter.create<LLVM::ExtractValueOp>(
+      auto dimSize = rewriter.create<LLVM::ExtractValueOp>(loc, int64Ty,
-          loc, int64Ty, outMemRef,
+          outMemRef,
           rewriter.getArrayAttr(
               {rewriter.getI64IntegerAttr(3), rewriter.getI64IntegerAttr(i)}));
-      auto dimSizePtr = rewriter.create<LLVM::GEPOp>(
+      auto dimSizePtr = rewriter.create<LLVM::GEPOp>(loc,
-          loc, int64Ty.getPointerTo(), sizesArrayPtr,
+          int64Ty.getPointerTo(), sizesArrayPtr, ArrayRef<Value>({dimIdx}));
-          ArrayRef<Value>({dimIdx}));
       rewriter.create<LLVM::StoreOp>(loc, dimSize, dimSizePtr);
 
       // Transfer stride of dimension from memref to dynamic memref.
-      auto dimStride = rewriter.create<LLVM::ExtractValueOp>(
+      auto dimStride = rewriter.create<LLVM::ExtractValueOp>(loc, int64Ty,
-          loc, int64Ty, outMemRef,
+          outMemRef,
           rewriter.getArrayAttr(
               {rewriter.getI64IntegerAttr(4), rewriter.getI64IntegerAttr(i)}));
-      auto dimStridePtr = rewriter.create<LLVM::GEPOp>(
+      auto dimStridePtr = rewriter.create<LLVM::GEPOp>(loc,
-          loc, int64Ty.getPointerTo(), stridesArrayPtr,
+          int64Ty.getPointerTo(), stridesArrayPtr, ArrayRef<Value>({dimIdx}));
-          ArrayRef<Value>({dimIdx}));
       rewriter.create<LLVM::StoreOp>(loc, dimStride, dimStridePtr);
     }
   }
@@ -530,5 +522,5 @@ std::unique_ptr<mlir::Pass> mlir::createKrnlLowerToLLVMPass() {
   return std::make_unique<KrnlToLLVMLoweringPass>();
 }
 
-static PassRegistration<KrnlToLLVMLoweringPass>
+static PassRegistration<KrnlToLLVMLoweringPass> pass(
-    pass("lower-all-llvm", "Lower the Krnl Affine and Std dialects to LLVM.");
+    "lower-all-llvm", "Lower the Krnl Affine and Std dialects to LLVM.");

src/Transform/ONNX/ONNXRewrite.cpp

@@ -22,7 +22,7 @@ namespace {
 bool hasNonZeroInArrayAttr(ArrayAttr attrs) {
   bool allZeros = true;
   if (attrs) {
-    for (auto attr: attrs.getValue()) {
+    for (auto attr : attrs.getValue()) {
       if (attr.cast<IntegerAttr>().getInt() > 0) {
         allZeros = false;
         break;
@@ -54,7 +54,7 @@ ArrayAttr createArrayAttrOfZeros(
 // This function is used for padding attribute in MaxPoolSingleOut.
 ArrayAttr insertZerosForNonPaddedDims(
     PatternRewriter &rewriter, ArrayAttr origAttrs, int extensionLength) {
-  int nDims = (int) origAttrs.getValue().size() / 2;
+  int nDims = (int)origAttrs.getValue().size() / 2;
   int nElements = (nDims + extensionLength) * 2;
   SmallVector<int64_t, 4> pads(nElements, 0);
   for (int i = 0; i < nDims; ++i) {