add shared loop creation helpers

add shared checked arithmetic helpers
refactor pim passes into Pim/Transforms
more robust memory coalescing pass

src/PIM/Conversion/ONNXToSpatial/Common/ComputeRegionBuilder.hpp

@@ -12,6 +12,7 @@
 #include <type_traits>
 #include <utility>
 
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
@@ -180,8 +181,11 @@ auto createSpatComputeBatch(RewriterT& rewriter,
   if (laneCount <= 0 || laneCount > std::numeric_limits<int32_t>::max())
     return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
 
-  auto batchOp = spatial::SpatComputeBatch::create(
-    rewriter, loc, resultTypes, rewriter.getI32IntegerAttr(static_cast<int32_t>(laneCount)), weights, inputs);
+  auto laneCountAttr = pim::getCheckedI32Attr(rewriter, loc, laneCount, "spatial compute_batch lane count");
+  if (mlir::failed(laneCountAttr))
+    return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+
+  auto batchOp = spatial::SpatComputeBatch::create(rewriter, loc, resultTypes, *laneCountAttr, weights, inputs);
 
   mlir::SmallVector<mlir::Type> blockArgTypes {rewriter.getIndexType()};
   mlir::SmallVector<mlir::Location> blockArgLocs {loc};

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Conv.cpp

@@ -8,6 +8,7 @@
 
 #include <algorithm>
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -305,58 +306,67 @@ static Value createIm2colRowComputes(Value x,
       auto cStrideHeight = getOrCreateIndexConstant(rewriter, anchorOp, strideHeight);
       auto cStrideWidth = getOrCreateIndexConstant(rewriter, anchorOp, strideWidth);
 
-      auto im2colLoop = scf::ForOp::create(rewriter, loc, c0, cNumPatches, c1, ValueRange {im2colInit});
-      rewriter.setInsertionPointToStart(im2colLoop.getBody());
+      auto im2colLoop = buildNormalizedScfFor(
+        rewriter,
+        loc,
+        c0,
+        cNumPatches,
+        c1,
+        ValueRange {im2colInit},
+        [&](OpBuilder&, Location nestedLoc, Value patchIndex, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+          Value im2colAcc = iterArgs.front();
+          Value batchIndex = arith::DivUIOp::create(rewriter, nestedLoc, patchIndex, cNumPatchesPerBatch);
+          Value batchPatchIndex = arith::RemUIOp::create(rewriter, nestedLoc, patchIndex, cNumPatchesPerBatch);
+          Value outHeightIndex = arith::DivUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutWidth);
+          Value outWidthIndex = arith::RemUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutWidth);
+          Value inputHeightOffset = arith::MulIOp::create(rewriter, nestedLoc, outHeightIndex, cStrideHeight);
+          Value inputWidthOffset = arith::MulIOp::create(rewriter, nestedLoc, outWidthIndex, cStrideWidth);
 
-      Value patchIndex = im2colLoop.getInductionVar();
-      Value im2colAcc = im2colLoop.getRegionIterArgs().front();
+          SmallVector<OpFoldResult> offsets = {
+            batchIndex, rewriter.getIndexAttr(0), inputHeightOffset, inputWidthOffset};
+          SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                             rewriter.getIndexAttr(numChannelsIn),
+                                             rewriter.getIndexAttr(wHeight),
+                                             rewriter.getIndexAttr(wWidth)};
+          SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
+                                               rewriter.getIndexAttr(1),
+                                               rewriter.getIndexAttr(dilationHeight),
+                                               rewriter.getIndexAttr(dilationWidth)};
+          auto patchType = RankedTensorType::get({1, numChannelsIn, wHeight, wWidth}, elemType);
+          Value patch =
+            tensor::ExtractSliceOp::create(rewriter, nestedLoc, patchType, paddedInput, offsets, sizes, strides);
 
-      Value batchIndex = arith::DivUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
-      Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
-      Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
-      Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
-      Value inputHeightOffset = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
-      Value inputWidthOffset = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
+          Value row = tensor::CollapseShapeOp::create(rewriter,
+                                                      nestedLoc,
+                                                      im2colRowType,
+                                                      patch,
+                                                      SmallVector<ReassociationIndices> {
+                                                        {0},
+                                                        {1, 2, 3}
+          });
 
-      SmallVector<OpFoldResult> offsets = {batchIndex, rewriter.getIndexAttr(0), inputHeightOffset, inputWidthOffset};
-      SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
-                                         rewriter.getIndexAttr(numChannelsIn),
-                                         rewriter.getIndexAttr(wHeight),
-                                         rewriter.getIndexAttr(wWidth)};
-      SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
-                                           rewriter.getIndexAttr(1),
-                                           rewriter.getIndexAttr(dilationHeight),
-                                           rewriter.getIndexAttr(dilationWidth)};
-      auto patchType = RankedTensorType::get({1, numChannelsIn, wHeight, wWidth}, elemType);
-      Value patch = tensor::ExtractSliceOp::create(rewriter, loc, patchType, paddedInput, offsets, sizes, strides);
-
-      Value row = tensor::CollapseShapeOp::create(rewriter,
-                                                  loc,
-                                                  im2colRowType,
-                                                  patch,
-                                                  SmallVector<ReassociationIndices> {
-                                                    {0},
-                                                    {1, 2, 3}
-      });
-
-      SmallVector<OpFoldResult> rowOffsets = {patchIndex, rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> rowSizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(patchSize)};
-      SmallVector<OpFoldResult> rowStrides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      Value updatedIm2col =
-        tensor::InsertSliceOp::create(rewriter, loc, row, im2colAcc, rowOffsets, rowSizes, rowStrides);
-      scf::YieldOp::create(rewriter, loc, updatedIm2col);
-
-      rewriter.setInsertionPointAfter(im2colLoop);
-      Value im2col = im2colLoop.getResult(0);
+          SmallVector<OpFoldResult> rowOffsets = {patchIndex, rewriter.getIndexAttr(0)};
+          SmallVector<OpFoldResult> rowSizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(patchSize)};
+          SmallVector<OpFoldResult> rowStrides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+          Value updatedIm2col =
+            tensor::InsertSliceOp::create(rewriter, nestedLoc, row, im2colAcc, rowOffsets, rowSizes, rowStrides);
+          yielded.push_back(updatedIm2col);
+          return success();
+        });
+      if (failed(im2colLoop))
+        return failure();
+      Value im2col = im2colLoop->results.front();
 
       Value gemmInputRows = im2col;
       if (packFactor != 1)
         gemmInputRows = packRowsForParallelGemm(im2col, im2colType, packFactor, rewriter, loc);
 
       spatial::SpatYieldOp::create(rewriter, loc, gemmInputRows);
+      return success();
     });
 
-  return im2colComputeOp.getResult(0);
+  assert(succeeded(im2colComputeOp) && "Conv im2col compute construction must succeed");
+  return im2colComputeOp->getResult(0);
 }
 
 static Value createCollectedConvOutput(ValueRange gemmRows,

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Gemm.cpp

@@ -15,6 +15,7 @@
 
 #include "Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
@@ -247,16 +248,16 @@ static Value createPaddedInputCompute(Value input,
   return computeOp.getResult(0);
 }
 
-static spatial::SpatComputeBatch createVmmBatch(Value a,
-                                                Value b,
-                                                RankedTensorType aType,
-                                                RankedTensorType paddedBType,
-                                                RankedTensorType partialPiecesType,
-                                                int64_t numOutRows,
-                                                int64_t numKSlices,
-                                                int64_t numOutHSlices,
-                                                ConversionPatternRewriter& rewriter,
-                                                Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createVmmBatch(Value a,
+                                                           Value b,
+                                                           RankedTensorType aType,
+                                                           RankedTensorType paddedBType,
+                                                           RankedTensorType partialPiecesType,
+                                                           int64_t numOutRows,
+                                                           int64_t numKSlices,
+                                                           int64_t numOutHSlices,
+                                                           ConversionPatternRewriter& rewriter,
+                                                           Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
   auto batchOp = createSpatComputeBatch(
     rewriter,
@@ -294,7 +295,8 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, piece, args.outputs.front(), pieceOffsets, pieceSizes, unitStrides);
     });
-  assert(succeeded(batchOp) && "expected Gemm VMM batch construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
@@ -416,15 +418,15 @@ static Value createBroadcastedBiasScalar(Value bias,
   return tensor::SplatOp::create(rewriter, loc, scalarType, scalar).getResult();
 }
 
-static spatial::SpatComputeBatch createVvdmulBatch(Value a,
-                                                   Value b,
-                                                   RankedTensorType aType,
-                                                   RankedTensorType bType,
-                                                   RankedTensorType scalarPiecesType,
-                                                   RankedTensorType outType,
-                                                   bool bAlreadyTransposed,
-                                                   ConversionPatternRewriter& rewriter,
-                                                   Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createVvdmulBatch(Value a,
+                                                              Value b,
+                                                              RankedTensorType aType,
+                                                              RankedTensorType bType,
+                                                              RankedTensorType scalarPiecesType,
+                                                              RankedTensorType outType,
+                                                              bool bAlreadyTransposed,
+                                                              ConversionPatternRewriter& rewriter,
+                                                              Location loc) {
   const int64_t numOutRows = outType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   const int64_t reductionSize = aType.getDimSize(1);
@@ -454,26 +456,27 @@ static spatial::SpatComputeBatch createVvdmulBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, scalar, args.outputs.front(), outputOffsets, scalarSizes, unitStrides);
     });
-  assert(succeeded(batchOp) && "expected Gemm VVDMul batch construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
-static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
-                                                           Value bias,
-                                                           RankedTensorType scalarPiecesType,
-                                                           RankedTensorType biasType,
-                                                           RankedTensorType outType,
-                                                           float alpha,
-                                                           float beta,
-                                                           ConversionPatternRewriter& rewriter,
-                                                           Location loc) {
+static FailureOr<spatial::SpatCompute> createDynamicGemmOutputCompute(Value scalarPieces,
+                                                                      Value bias,
+                                                                      RankedTensorType scalarPiecesType,
+                                                                      RankedTensorType biasType,
+                                                                      RankedTensorType outType,
+                                                                      float alpha,
+                                                                      float beta,
+                                                                      ConversionPatternRewriter& rewriter,
+                                                                      Location loc) {
   const int64_t laneCount = scalarPiecesType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   SmallVector<Value> inputs {scalarPieces};
   if (bias)
     inputs.push_back(bias);
 
-  return createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) {
+  return createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) -> LogicalResult {
     Value pieces = blockArgs[0];
     Value biasArg = bias ? blockArgs[1] : Value();
     auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
@@ -481,40 +484,50 @@ static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
     Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
     Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
     Value cLaneCount = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), laneCount);
-    auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
-    rewriter.setInsertionPointToStart(loop.getBody());
+    auto loop = buildNormalizedScfFor(
+      rewriter,
+      loc,
+      c0,
+      cLaneCount,
+      c1,
+      ValueRange {outputInit},
+      [&](OpBuilder&, Location nestedLoc, Value lane, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+        Value outputAcc = iterArgs.front();
+        Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, nestedLoc);
+        Value column =
+          onnx_mlir::affineModConst(rewriter, nestedLoc, lane, numOutCols, rewriter.getInsertionBlock()->getParentOp());
+        SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
+        SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+        SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+        Value scalar = tensor::ExtractSliceOp::create(
+                         rewriter, nestedLoc, scalarType, pieces, scalarOffsets, scalarSizes, unitStrides)
+                         .getResult();
+        if (alpha != 1.0f) {
+          Value alphaTensor = createScalarTensorConstant(scalarType, alpha, rewriter, nestedLoc);
+          scalar = spatial::SpatVMulOp::create(rewriter, nestedLoc, scalarType, scalar, alphaTensor).getResult();
+        }
+        if (biasArg) {
+          Value biasScalar =
+            createBroadcastedBiasScalar(biasArg, biasType, row, column, scalarType, rewriter, nestedLoc);
+          if (beta != 1.0f) {
+            Value betaTensor = createScalarTensorConstant(scalarType, beta, rewriter, nestedLoc);
+            biasScalar =
+              spatial::SpatVMulOp::create(rewriter, nestedLoc, scalarType, biasScalar, betaTensor).getResult();
+          }
+          scalar = spatial::SpatVAddOp::create(rewriter, nestedLoc, scalarType, scalar, biasScalar).getResult();
+        }
+        SmallVector<OpFoldResult> outputOffsets {row, column};
+        Value outputNext =
+          tensor::InsertSliceOp::create(rewriter, nestedLoc, scalar, outputAcc, outputOffsets, scalarSizes, unitStrides)
+            .getResult();
+        yielded.push_back(outputNext);
+        return success();
+      });
+    if (failed(loop))
+      return failure();
 
-    Value lane = loop.getInductionVar();
-    Value outputAcc = loop.getRegionIterArgs().front();
-    Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, loc);
-    Value column =
-      onnx_mlir::affineModConst(rewriter, loc, lane, numOutCols, rewriter.getInsertionBlock()->getParentOp());
-    SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
-    SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    Value scalar =
-      tensor::ExtractSliceOp::create(rewriter, loc, scalarType, pieces, scalarOffsets, scalarSizes, unitStrides)
-        .getResult();
-    if (alpha != 1.0f) {
-      Value alphaTensor = createScalarTensorConstant(scalarType, alpha, rewriter, loc);
-      scalar = spatial::SpatVMulOp::create(rewriter, loc, scalarType, scalar, alphaTensor).getResult();
-    }
-    if (biasArg) {
-      Value biasScalar = createBroadcastedBiasScalar(biasArg, biasType, row, column, scalarType, rewriter, loc);
-      if (beta != 1.0f) {
-        Value betaTensor = createScalarTensorConstant(scalarType, beta, rewriter, loc);
-        biasScalar = spatial::SpatVMulOp::create(rewriter, loc, scalarType, biasScalar, betaTensor).getResult();
-      }
-      scalar = spatial::SpatVAddOp::create(rewriter, loc, scalarType, scalar, biasScalar).getResult();
-    }
-    SmallVector<OpFoldResult> outputOffsets {row, column};
-    Value outputNext =
-      tensor::InsertSliceOp::create(rewriter, loc, scalar, outputAcc, outputOffsets, scalarSizes, unitStrides)
-        .getResult();
-    scf::YieldOp::create(rewriter, loc, outputNext);
-
-    rewriter.setInsertionPointAfter(loop);
-    spatial::SpatYieldOp::create(rewriter, loc, loop.getResult(0));
+    spatial::SpatYieldOp::create(rewriter, loc, loop->results.front());
+    return success();
   });
 }
 
@@ -579,85 +592,92 @@ static Value reducePartialPiecesForHSlice(Value partialPiecesArg,
   return activePieces.front();
 }
 
-static spatial::SpatCompute createReductionCompute(Value partialPieces,
-                                                   Value bias,
-                                                   RankedTensorType partialPiecesType,
-                                                   RankedTensorType outType,
-                                                   RankedTensorType paddedOutType,
-                                                   int64_t numKSlices,
-                                                   ConversionPatternRewriter& rewriter,
-                                                   Location loc) {
+static FailureOr<spatial::SpatCompute> createReductionCompute(Value partialPieces,
+                                                              Value bias,
+                                                              RankedTensorType partialPiecesType,
+                                                              RankedTensorType outType,
+                                                              RankedTensorType paddedOutType,
+                                                              int64_t numKSlices,
+                                                              ConversionPatternRewriter& rewriter,
+                                                              Location loc) {
   SmallVector<Value> inputs {partialPieces};
   if (bias)
     inputs.push_back(bias);
 
-  auto computeOp = createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) {
-    Value partialPiecesArg = blockArgs[0];
-    Value biasArg = bias ? blockArgs[1] : Value();
-    if (biasArg && cast<RankedTensorType>(biasArg.getType()) != paddedOutType)
-      biasArg = createZeroPaddedTensor(biasArg, paddedOutType, rewriter, loc);
+  auto computeOp =
+    createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) -> LogicalResult {
+      Value partialPiecesArg = blockArgs[0];
+      Value biasArg = bias ? blockArgs[1] : Value();
+      if (biasArg && cast<RankedTensorType>(biasArg.getType()) != paddedOutType)
+        biasArg = createZeroPaddedTensor(biasArg, paddedOutType, rewriter, loc);
 
-    const int64_t numOutRows = outType.getDimSize(0);
-    const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(1), crossbarSize.getValue());
-    auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
-                                           partialPiecesType.getElementType());
+      const int64_t numOutRows = outType.getDimSize(0);
+      const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(1), crossbarSize.getValue());
+      auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
+                                             partialPiecesType.getElementType());
 
-    Value outputInit =
-      tensor::EmptyOp::create(rewriter, loc, paddedOutType.getShape(), paddedOutType.getElementType()).getResult();
-    SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(numOutRows),
-                                          rewriter.getIndexAttr(crossbarSize.getValue())};
+      Value outputInit =
+        tensor::EmptyOp::create(rewriter, loc, paddedOutType.getShape(), paddedOutType.getElementType()).getResult();
+      SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(numOutRows),
+                                            rewriter.getIndexAttr(crossbarSize.getValue())};
 
-    auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
-      Value reduced =
-        reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
-      Value hOffset = onnx_mlir::affineMulConst(
-        rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
-      if (biasArg) {
-        SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
-        Value biasSlice =
-          tensor::ExtractSliceOp::create(rewriter, loc, pieceType, biasArg, biasOffsets, pieceSizes, unitStrides)
-            .getResult();
-        reduced = spatial::SpatVAddOp::create(rewriter, loc, pieceType, reduced, biasSlice).getResult();
+      auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
+        Value reduced =
+          reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
+        Value hOffset = onnx_mlir::affineMulConst(
+          rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
+        if (biasArg) {
+          SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
+          Value biasSlice =
+            tensor::ExtractSliceOp::create(rewriter, loc, pieceType, biasArg, biasOffsets, pieceSizes, unitStrides)
+              .getResult();
+          reduced = spatial::SpatVAddOp::create(rewriter, loc, pieceType, reduced, biasSlice).getResult();
+        }
+
+        SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), hOffset};
+        return tensor::InsertSliceOp::create(rewriter, loc, reduced, outputAcc, outputOffsets, pieceSizes, unitStrides)
+          .getResult();
+      };
+
+      Value paddedOutput = outputInit;
+      if (numOutHSlices == 1) {
+        Value hSlice = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+        paddedOutput = buildOutputSlice(outputInit, hSlice);
+      }
+      else {
+        Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+        Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
+        Value cOutHSlices =
+          getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
+        auto hLoop = buildNormalizedScfFor(
+          rewriter,
+          loc,
+          c0,
+          cOutHSlices,
+          c1,
+          ValueRange {outputInit},
+          [&](OpBuilder&, Location, Value hSlice, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+            yielded.push_back(buildOutputSlice(iterArgs.front(), hSlice));
+            return success();
+          });
+        if (failed(hLoop))
+          return failure();
+        paddedOutput = hLoop->results.front();
       }
 
-      SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), hOffset};
-      return tensor::InsertSliceOp::create(rewriter, loc, reduced, outputAcc, outputOffsets, pieceSizes, unitStrides)
-        .getResult();
-    };
-
-    Value paddedOutput = outputInit;
-    if (numOutHSlices == 1) {
-      Value hSlice = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      paddedOutput = buildOutputSlice(outputInit, hSlice);
-    }
-    else {
-      Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
-      Value cOutHSlices =
-        getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
-      auto hLoop = scf::ForOp::create(rewriter, loc, c0, cOutHSlices, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(hLoop.getBody());
-
-      Value hSlice = hLoop.getInductionVar();
-      Value outputAcc = hLoop.getRegionIterArgs().front();
-      scf::YieldOp::create(rewriter, loc, buildOutputSlice(outputAcc, hSlice));
-
-      rewriter.setInsertionPointAfter(hLoop);
-      paddedOutput = hLoop.getResult(0);
-    }
-
-    Value result = paddedOutput;
-    if (paddedOutType != outType) {
-      SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(outType.getDimSize(0)),
-                                             rewriter.getIndexAttr(outType.getDimSize(1))};
-      result =
-        tensor::ExtractSliceOp::create(rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, unitStrides)
-          .getResult();
-    }
-    spatial::SpatYieldOp::create(rewriter, loc, result);
-  });
+      Value result = paddedOutput;
+      if (paddedOutType != outType) {
+        SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
+        SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(outType.getDimSize(0)),
+                                               rewriter.getIndexAttr(outType.getDimSize(1))};
+        result =
+          tensor::ExtractSliceOp::create(rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, unitStrides)
+            .getResult();
+      }
+      spatial::SpatYieldOp::create(rewriter, loc, result);
+      return success();
+    });
 
   return computeOp;
 }
@@ -755,9 +775,13 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     auto scalarPiecesType = RankedTensorType::get({laneCount64, 1}, outType.getElementType());
     auto batchOp =
       createVvdmulBatch(a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
+    if (failed(batchOp))
+      return failure();
     auto outputCompute = createDynamicGemmOutputCompute(
-      batchOp.getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
-    rewriter.replaceOp(gemmOp, outputCompute.getResults());
+      batchOp->getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
+    if (failed(outputCompute))
+      return failure();
+    rewriter.replaceOp(gemmOp, outputCompute->getResults());
     return success();
   }
 
@@ -832,10 +856,14 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     RankedTensorType::get({laneCount64, static_cast<int64_t>(crossbarSize.getValue())}, outType.getElementType());
   auto batchOp =
     createVmmBatch(a, b, aType, paddedBType, partialPiecesType, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
+  if (failed(batchOp))
+    return failure();
   auto reductionCompute = createReductionCompute(
-    batchOp.getResult(0), bias, partialPiecesType, outType, paddedOutType, numKSlices, rewriter, loc);
+    batchOp->getResult(0), bias, partialPiecesType, outType, paddedOutType, numKSlices, rewriter, loc);
+  if (failed(reductionCompute))
+    return failure();
 
-  rewriter.replaceOp(gemmOp, reductionCompute.getResults());
+  rewriter.replaceOp(gemmOp, reductionCompute->getResults());
   return success();
 }
 

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/MatMul.cpp

@@ -8,6 +8,7 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
@@ -281,18 +282,18 @@ static Value getBatchLaneIndex(
     rewriter, loc, lane, numOutRows * numKSlices * numOutHSlices, rewriter.getInsertionBlock()->getParentOp());
 }
 
-static spatial::SpatComputeBatch createBatchedVmmBatch(Value a,
-                                                       Value b,
-                                                       RankedTensorType aType,
-                                                       int64_t aBatchCount,
-                                                       RankedTensorType bType,
-                                                       int64_t bBatchCount,
-                                                       RankedTensorType partialPiecesType,
-                                                       int64_t numOutRows,
-                                                       int64_t numKSlices,
-                                                       int64_t numOutHSlices,
-                                                       PatternRewriter& rewriter,
-                                                       Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createBatchedVmmBatch(Value a,
+                                                                  Value b,
+                                                                  RankedTensorType aType,
+                                                                  int64_t aBatchCount,
+                                                                  RankedTensorType bType,
+                                                                  int64_t bBatchCount,
+                                                                  RankedTensorType partialPiecesType,
+                                                                  int64_t numOutRows,
+                                                                  int64_t numKSlices,
+                                                                  int64_t numOutHSlices,
+                                                                  PatternRewriter& rewriter,
+                                                                  Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
   auto batchOp = createSpatComputeBatch(
     rewriter,
@@ -331,7 +332,8 @@ static spatial::SpatComputeBatch createBatchedVmmBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, piece, args.outputs.front(), pieceOffsets, pieceSizes, getUnitStrides(rewriter, 2));
     });
-  assert(succeeded(batchOp) && "expected batched MatMul VMM construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
@@ -422,17 +424,17 @@ static Value extractDynamicBatchedRowVector(Value matrix,
   });
 }
 
-static spatial::SpatComputeBatch createBatchedVvdmulBatch(Value a,
-                                                          int64_t aBatchCount,
-                                                          Value b,
-                                                          int64_t bBatchCount,
-                                                          RankedTensorType aType,
-                                                          RankedTensorType bType,
-                                                          RankedTensorType scalarPiecesType,
-                                                          RankedTensorType outType,
-                                                          bool bAlreadyTransposed,
-                                                          PatternRewriter& rewriter,
-                                                          Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createBatchedVvdmulBatch(Value a,
+                                                                     int64_t aBatchCount,
+                                                                     Value b,
+                                                                     int64_t bBatchCount,
+                                                                     RankedTensorType aType,
+                                                                     RankedTensorType bType,
+                                                                     RankedTensorType scalarPiecesType,
+                                                                     RankedTensorType outType,
+                                                                     bool bAlreadyTransposed,
+                                                                     PatternRewriter& rewriter,
+                                                                     Location loc) {
   const int64_t numBatches = outType.getDimSize(0);
   const int64_t numOutRows = outType.getDimSize(1);
   const int64_t numOutCols = outType.getDimSize(2);
@@ -466,64 +468,73 @@ static spatial::SpatComputeBatch createBatchedVvdmulBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, scalar, args.outputs.front(), outputOffsets, scalarSizes, getUnitStrides(rewriter, 2));
     });
-  assert(succeeded(batchOp) && "expected batched MatMul VVDMul construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
-static Value createBatchedDynamicOutputCompute(Value scalarPieces,
-                                               RankedTensorType scalarPiecesType,
-                                               RankedTensorType outType,
-                                               PatternRewriter& rewriter,
-                                               Location loc) {
+static FailureOr<Value> createBatchedDynamicOutputCompute(Value scalarPieces,
+                                                          RankedTensorType scalarPiecesType,
+                                                          RankedTensorType outType,
+                                                          PatternRewriter& rewriter,
+                                                          Location loc) {
   const int64_t laneCount = scalarPiecesType.getDimSize(0);
   const int64_t numOutRows = outType.getDimSize(1);
   const int64_t numOutCols = outType.getDimSize(2);
   auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
   auto outputScalarType = RankedTensorType::get({1, 1, 1}, outType.getElementType());
 
-  auto computeOp =
-    createSpatCompute<1>(rewriter, loc, TypeRange {outType}, {}, ValueRange {scalarPieces}, [&](Value pieces) {
+  auto computeOp = createSpatCompute<1>(
+    rewriter, loc, TypeRange {outType}, {}, ValueRange {scalarPieces}, [&](Value pieces) -> LogicalResult {
       Value outputInit =
         tensor::EmptyOp::create(rewriter, loc, outType.getShape(), outType.getElementType()).getResult();
       Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
       Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
       Value cLaneCount = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), laneCount);
-      auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(loop.getBody());
-
-      Value lane = loop.getInductionVar();
-      Value outputAcc = loop.getRegionIterArgs().front();
-      Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
-      Value batch = affineFloorDivConst(rewriter, loc, lane, numOutRows * numOutCols, anchorOp);
-      Value batchLane = affineModConst(rewriter, loc, lane, numOutRows * numOutCols, anchorOp);
-      Value row = affineFloorDivConst(rewriter, loc, batchLane, numOutCols, anchorOp);
-      Value column = affineModConst(rewriter, loc, batchLane, numOutCols, anchorOp);
-      SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      Value scalar = tensor::ExtractSliceOp::create(
-        rewriter, loc, scalarType, pieces, scalarOffsets, scalarSizes, getUnitStrides(rewriter, 2));
-      Value expanded = tensor::ExpandShapeOp::create(rewriter,
-                                                     loc,
-                                                     outputScalarType,
-                                                     scalar,
-                                                     SmallVector<ReassociationIndices> {
-                                                       {0},
-                                                       {1, 2}
-      });
-      SmallVector<OpFoldResult> outputOffsets {batch, row, column};
-      SmallVector<OpFoldResult> outputSizes {
-        rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      scf::YieldOp::create(
+      auto loop = buildNormalizedScfFor(
         rewriter,
         loc,
-        tensor::InsertSliceOp::create(
-          rewriter, loc, expanded, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
-          .getResult());
-
-      rewriter.setInsertionPointAfter(loop);
-      spatial::SpatYieldOp::create(rewriter, loc, loop.getResult(0));
+        c0,
+        cLaneCount,
+        c1,
+        ValueRange {outputInit},
+        [&](OpBuilder&, Location nestedLoc, Value lane, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+          Value outputAcc = iterArgs.front();
+          Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
+          Value batch = affineFloorDivConst(rewriter, nestedLoc, lane, numOutRows * numOutCols, anchorOp);
+          Value batchLane = affineModConst(rewriter, nestedLoc, lane, numOutRows * numOutCols, anchorOp);
+          Value row = affineFloorDivConst(rewriter, nestedLoc, batchLane, numOutCols, anchorOp);
+          Value column = affineModConst(rewriter, nestedLoc, batchLane, numOutCols, anchorOp);
+          SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
+          SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+          Value scalar = tensor::ExtractSliceOp::create(
+            rewriter, nestedLoc, scalarType, pieces, scalarOffsets, scalarSizes, getUnitStrides(rewriter, 2));
+          Value expanded = tensor::ExpandShapeOp::create(rewriter,
+                                                         nestedLoc,
+                                                         outputScalarType,
+                                                         scalar,
+                                                         SmallVector<ReassociationIndices> {
+                                                           {0},
+                                                           {1, 2}
+          });
+          SmallVector<OpFoldResult> outputOffsets {batch, row, column};
+          SmallVector<OpFoldResult> outputSizes = {
+            rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+          Value next =
+            tensor::InsertSliceOp::create(
+              rewriter, nestedLoc, expanded, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
+              .getResult();
+          yielded.push_back(next);
+          return success();
+        });
+      if (failed(loop))
+        return failure();
+      spatial::SpatYieldOp::create(rewriter, loc, loop->results.front());
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 static Value transposeBatchedOutput(Value value, RankedTensorType outputType, PatternRewriter& rewriter, Location loc) {
@@ -587,16 +598,16 @@ static Value reduceBatchedPartialPiecesForHSlice(Value partialPiecesArg,
   return activePieces.front();
 }
 
-static Value createBatchedReductionCompute(Value partialPieces,
-                                           RankedTensorType partialPiecesType,
-                                           RankedTensorType outType,
-                                           RankedTensorType paddedOutType,
-                                           int64_t numBatches,
-                                           int64_t numKSlices,
-                                           PatternRewriter& rewriter,
-                                           Location loc) {
+static FailureOr<Value> createBatchedReductionCompute(Value partialPieces,
+                                                      RankedTensorType partialPiecesType,
+                                                      RankedTensorType outType,
+                                                      RankedTensorType paddedOutType,
+                                                      int64_t numBatches,
+                                                      int64_t numKSlices,
+                                                      PatternRewriter& rewriter,
+                                                      Location loc) {
   auto computeOp = createSpatCompute<1>(
-    rewriter, loc, TypeRange {outType}, {}, ValueRange {partialPieces}, [&](Value partialPiecesArg) {
+    rewriter, loc, TypeRange {outType}, {}, ValueRange {partialPieces}, [&](Value partialPiecesArg) -> LogicalResult {
       const int64_t numOutRows = outType.getDimSize(1);
       const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(2), crossbarSize.getValue());
       auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
@@ -612,43 +623,55 @@ static Value createBatchedReductionCompute(Value partialPieces,
       Value cNumOutHSlices =
         getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
 
-      auto batchLoop = scf::ForOp::create(rewriter, loc, c0, cNumBatches, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(batchLoop.getBody());
-      Value batch = batchLoop.getInductionVar();
-      Value batchAcc = batchLoop.getRegionIterArgs().front();
-
-      auto hLoop = scf::ForOp::create(rewriter, loc, c0, cNumOutHSlices, c1, ValueRange {batchAcc});
-      rewriter.setInsertionPointToStart(hLoop.getBody());
-      Value hSlice = hLoop.getInductionVar();
-      Value outputAcc = hLoop.getRegionIterArgs().front();
-
-      Value reduced = reduceBatchedPartialPiecesForHSlice(
-        partialPiecesArg, batch, hSlice, pieceType, numKSlices, numOutHSlices, numOutRows, rewriter, loc);
-      Value expandedReduced = tensor::ExpandShapeOp::create(rewriter,
-                                                            loc,
-                                                            outputSliceType,
-                                                            reduced,
-                                                            SmallVector<ReassociationIndices> {
-                                                              {0, 1},
-                                                              {2}
-      });
-      Value hOffset =
-        affineMulConst(rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
-      SmallVector<OpFoldResult> outputOffsets {batch, rewriter.getIndexAttr(0), hOffset};
-      SmallVector<OpFoldResult> outputSizes {
-        rewriter.getIndexAttr(1), rewriter.getIndexAttr(numOutRows), rewriter.getIndexAttr(crossbarSize.getValue())};
-      scf::YieldOp::create(
+      auto batchLoop = buildNormalizedScfFor(
         rewriter,
         loc,
-        tensor::InsertSliceOp::create(
-          rewriter, loc, expandedReduced, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
-          .getResult());
-
-      rewriter.setInsertionPointAfter(hLoop);
-      scf::YieldOp::create(rewriter, loc, hLoop.getResult(0));
-
-      rewriter.setInsertionPointAfter(batchLoop);
-      Value paddedOutput = batchLoop.getResult(0);
+        c0,
+        cNumBatches,
+        c1,
+        ValueRange {outputInit},
+        [&](
+          OpBuilder&, Location batchLoc, Value batch, ValueRange batchIterArgs, SmallVectorImpl<Value>& batchYielded) {
+          auto hLoop = buildNormalizedScfFor(
+            rewriter,
+            batchLoc,
+            c0,
+            cNumOutHSlices,
+            c1,
+            ValueRange {batchIterArgs.front()},
+            [&](OpBuilder&, Location hLoc, Value hSlice, ValueRange hIterArgs, SmallVectorImpl<Value>& hYielded) {
+              Value outputAcc = hIterArgs.front();
+              Value reduced = reduceBatchedPartialPiecesForHSlice(
+                partialPiecesArg, batch, hSlice, pieceType, numKSlices, numOutHSlices, numOutRows, rewriter, hLoc);
+              Value expandedReduced = tensor::ExpandShapeOp::create(rewriter,
+                                                                    hLoc,
+                                                                    outputSliceType,
+                                                                    reduced,
+                                                                    SmallVector<ReassociationIndices> {
+                                                                      {0, 1},
+                                                                      {2}
+              });
+              Value hOffset = affineMulConst(
+                rewriter, hLoc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
+              SmallVector<OpFoldResult> outputOffsets {batch, rewriter.getIndexAttr(0), hOffset};
+              SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(numOutRows),
+                                                     rewriter.getIndexAttr(crossbarSize.getValue())};
+              Value next =
+                tensor::InsertSliceOp::create(
+                  rewriter, hLoc, expandedReduced, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
+                  .getResult();
+              hYielded.push_back(next);
+              return success();
+            });
+          if (failed(hLoop))
+            return failure();
+          batchYielded.push_back(hLoop->results.front());
+          return success();
+        });
+      if (failed(batchLoop))
+        return failure();
+      Value paddedOutput = batchLoop->results.front();
       Value result = paddedOutput;
       if (paddedOutType != outType) {
         SmallVector<OpFoldResult> outputOffsets {
@@ -660,8 +683,11 @@ static Value createBatchedReductionCompute(Value partialPieces,
           rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, getUnitStrides(rewriter, 3));
       }
       spatial::SpatYieldOp::create(rewriter, loc, result);
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 struct MatMulShapeInfo {
@@ -841,22 +867,27 @@ struct MatMulBatchedToSpatialComputes : OpRewritePattern<ONNXMatMulOp> {
                                              numOutHSlices,
                                              rewriter,
                                              loc);
-        Value result = createBatchedReductionCompute(batchOp.getResult(0),
-                                                     partialPiecesType,
-                                                     directOutType,
-                                                     paddedOutType,
-                                                     shapeInfo->batch,
-                                                     numKSlices,
-                                                     rewriter,
-                                                     loc);
+        if (failed(batchOp))
+          return failure();
+        auto result = createBatchedReductionCompute(batchOp->getResult(0),
+                                                    partialPiecesType,
+                                                    directOutType,
+                                                    paddedOutType,
+                                                    shapeInfo->batch,
+                                                    numKSlices,
+                                                    rewriter,
+                                                    loc);
+        if (failed(result))
+          return failure();
+        Value finalResult = *result;
         if (useTransposedForm)
-          result = transposeBatchedOutput(
-            result,
+          finalResult = transposeBatchedOutput(
+            finalResult,
             RankedTensorType::get({shapeInfo->batch, shapeInfo->m, shapeInfo->n}, shapeInfo->outType.getElementType()),
             rewriter,
             loc);
-        result = expandBatchDims(result, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
-        rewriter.replaceOp(matmulOp, result);
+        finalResult = expandBatchDims(finalResult, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
+        rewriter.replaceOp(matmulOp, finalResult);
         return success();
       }
     }
@@ -873,16 +904,21 @@ struct MatMulBatchedToSpatialComputes : OpRewritePattern<ONNXMatMulOp> {
                                             false,
                                             rewriter,
                                             loc);
-    Value result =
-      createBatchedDynamicOutputCompute(batchOp.getResult(0), scalarPiecesType, directOutType, rewriter, loc);
+    if (failed(batchOp))
+      return failure();
+    auto result =
+      createBatchedDynamicOutputCompute(batchOp->getResult(0), scalarPiecesType, directOutType, rewriter, loc);
+    if (failed(result))
+      return failure();
+    Value finalResult = *result;
     if (useTransposedForm)
-      result = transposeBatchedOutput(
-        result,
+      finalResult = transposeBatchedOutput(
+        finalResult,
         RankedTensorType::get({shapeInfo->batch, shapeInfo->m, shapeInfo->n}, shapeInfo->outType.getElementType()),
         rewriter,
         loc);
-    result = expandBatchDims(result, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
-    rewriter.replaceOp(matmulOp, result);
+    finalResult = expandBatchDims(finalResult, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
+    rewriter.replaceOp(matmulOp, finalResult);
     return success();
   }
 };

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Pool.cpp

@@ -12,6 +12,7 @@
 #include <optional>
 #include <type_traits>
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -275,86 +276,102 @@ struct PoolToSpatialComputeBase : public OpConversionPattern<PoolOp> {
         Value cStrideHeight = getOrCreateIndexConstant(rewriter, anchorOp, strideHeight);
         Value cStrideWidth = getOrCreateIndexConstant(rewriter, anchorOp, strideWidth);
 
-        auto outputLoop = scf::ForOp::create(rewriter, loc, c0, cOutputPatchCount, c1, ValueRange {pooledOutputInit});
-        rewriter.setInsertionPointToStart(outputLoop.getBody());
+        auto outputLoop = buildNormalizedScfFor(
+          rewriter,
+          loc,
+          c0,
+          cOutputPatchCount,
+          c1,
+          ValueRange {pooledOutputInit},
+          [&](OpBuilder&,
+              Location nestedLoc,
+              Value outputPatchIndex,
+              ValueRange iterArgs,
+              SmallVectorImpl<Value>& yielded) {
+            Value pooledOutputAcc = iterArgs.front();
+            Value batchIndex = arith::DivUIOp::create(rewriter, nestedLoc, outputPatchIndex, cOutputPixelsPerBatch);
+            Value batchPatchIndex =
+              arith::RemUIOp::create(rewriter, nestedLoc, outputPatchIndex, cOutputPixelsPerBatch);
+            Value outHeightIndex = arith::DivUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutputWidth);
+            Value outWidthIndex = arith::RemUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutputWidth);
+            Value windowBaseH = arith::MulIOp::create(rewriter, nestedLoc, outHeightIndex, cStrideHeight);
+            Value windowBaseW = arith::MulIOp::create(rewriter, nestedLoc, outWidthIndex, cStrideWidth);
 
-        Value outputPatchIndex = outputLoop.getInductionVar();
-        Value pooledOutputAcc = outputLoop.getRegionIterArgs().front();
+            Value updatedOutput = pooledOutputAcc;
+            for (int64_t channelTile = 0; channelTile < channelTileCount; ++channelTile) {
+              const int64_t tileChannels = std::min<int64_t>(xbarSize, channels - channelTile * xbarSize);
+              auto tileType = RankedTensorType::get({1, tileChannels, 1, 1}, outType.getElementType());
+              Value reducedWindow =
+                createPoolFillTensor(rewriter, nestedLoc, tileType, std::is_same_v<PoolOp, ONNXMaxPoolSingleOutOp>);
 
-        Value batchIndex = arith::DivUIOp::create(rewriter, loc, outputPatchIndex, cOutputPixelsPerBatch);
-        Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, outputPatchIndex, cOutputPixelsPerBatch);
-        Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutputWidth);
-        Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutputWidth);
-        Value windowBaseH = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
-        Value windowBaseW = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
+              for (int64_t kernelH = 0; kernelH < kernelHeight; ++kernelH) {
+                Value paddedInH = windowBaseH;
+                if (kernelH * dilationHeight != 0) {
+                  Value kernelHOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelH * dilationHeight);
+                  paddedInH = arith::AddIOp::create(rewriter, nestedLoc, paddedInH, kernelHOffset);
+                }
 
-        Value updatedOutput = pooledOutputAcc;
-        for (int64_t channelTile = 0; channelTile < channelTileCount; ++channelTile) {
-          const int64_t tileChannels = std::min<int64_t>(xbarSize, channels - channelTile * xbarSize);
-          auto tileType = RankedTensorType::get({1, tileChannels, 1, 1}, outType.getElementType());
-          Value reducedWindow =
-            createPoolFillTensor(rewriter, loc, tileType, std::is_same_v<PoolOp, ONNXMaxPoolSingleOutOp>);
+                for (int64_t kernelW = 0; kernelW < kernelWidth; ++kernelW) {
+                  Value paddedInW = windowBaseW;
+                  if (kernelW * dilationWidth != 0) {
+                    Value kernelWOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelW * dilationWidth);
+                    paddedInW = arith::AddIOp::create(rewriter, nestedLoc, paddedInW, kernelWOffset);
+                  }
 
-          for (int64_t kernelH = 0; kernelH < kernelHeight; ++kernelH) {
-            Value paddedInH = windowBaseH;
-            if (kernelH * dilationHeight != 0) {
-              Value kernelHOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelH * dilationHeight);
-              paddedInH = arith::AddIOp::create(rewriter, loc, paddedInH, kernelHOffset);
-            }
-
-            for (int64_t kernelW = 0; kernelW < kernelWidth; ++kernelW) {
-              Value paddedInW = windowBaseW;
-              if (kernelW * dilationWidth != 0) {
-                Value kernelWOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelW * dilationWidth);
-                paddedInW = arith::AddIOp::create(rewriter, loc, paddedInW, kernelWOffset);
+                  SmallVector<OpFoldResult> offsets = {
+                    batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), paddedInH, paddedInW};
+                  SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(tileChannels),
+                                                     rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(1)};
+                  SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1)};
+                  Value windowValue =
+                    tensor::ExtractSliceOp::create(rewriter, nestedLoc, tileType, paddedInput, offsets, sizes, strides);
+                  windowValue = materializeTileTensor(rewriter, nestedLoc, windowValue);
+                  reducedWindow = ReduceOp::create(rewriter, nestedLoc, tileType, reducedWindow, windowValue);
+                }
               }
 
-              SmallVector<OpFoldResult> offsets = {
-                batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), paddedInH, paddedInW};
-              SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
-                                                 rewriter.getIndexAttr(tileChannels),
-                                                 rewriter.getIndexAttr(1),
-                                                 rewriter.getIndexAttr(1)};
-              SmallVector<OpFoldResult> strides = {
+              if constexpr (std::is_same_v<PoolOp, ONNXAveragePoolOp>) {
+                SmallVector<OpFoldResult> scaleOffsets = {rewriter.getIndexAttr(0),
+                                                          rewriter.getIndexAttr(channelTile * xbarSize),
+                                                          outHeightIndex,
+                                                          outWidthIndex};
+                SmallVector<OpFoldResult> scaleSizes = {rewriter.getIndexAttr(1),
+                                                        rewriter.getIndexAttr(tileChannels),
+                                                        rewriter.getIndexAttr(1),
+                                                        rewriter.getIndexAttr(1)};
+                SmallVector<OpFoldResult> scaleStrides = {rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1)};
+                Value scaleSlice = tensor::ExtractSliceOp::create(
+                  rewriter, nestedLoc, tileType, averageScaleTensor, scaleOffsets, scaleSizes, scaleStrides);
+                scaleSlice = materializeTileTensor(rewriter, nestedLoc, scaleSlice);
+                reducedWindow = spatial::SpatVMulOp::create(rewriter, nestedLoc, tileType, reducedWindow, scaleSlice);
+              }
+
+              SmallVector<OpFoldResult> outputOffsets = {
+                batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
+              SmallVector<OpFoldResult> outputSizes = {rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(tileChannels),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1)};
+              SmallVector<OpFoldResult> outputStrides = {
                 rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-              Value windowValue =
-                tensor::ExtractSliceOp::create(rewriter, loc, tileType, paddedInput, offsets, sizes, strides);
-              windowValue = materializeTileTensor(rewriter, loc, windowValue);
-              reducedWindow = ReduceOp::create(rewriter, loc, tileType, reducedWindow, windowValue);
+              updatedOutput = tensor::InsertSliceOp::create(
+                rewriter, nestedLoc, reducedWindow, updatedOutput, outputOffsets, outputSizes, outputStrides);
             }
-          }
+            yielded.push_back(updatedOutput);
+            return success();
+          });
+        if (failed(outputLoop))
+          return failure();
 
-          if constexpr (std::is_same_v<PoolOp, ONNXAveragePoolOp>) {
-            SmallVector<OpFoldResult> scaleOffsets = {
-              rewriter.getIndexAttr(0), rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
-            SmallVector<OpFoldResult> scaleSizes = {rewriter.getIndexAttr(1),
-                                                    rewriter.getIndexAttr(tileChannels),
-                                                    rewriter.getIndexAttr(1),
-                                                    rewriter.getIndexAttr(1)};
-            SmallVector<OpFoldResult> scaleStrides = {
-              rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-            Value scaleSlice = tensor::ExtractSliceOp::create(
-              rewriter, loc, tileType, averageScaleTensor, scaleOffsets, scaleSizes, scaleStrides);
-            scaleSlice = materializeTileTensor(rewriter, loc, scaleSlice);
-            reducedWindow = spatial::SpatVMulOp::create(rewriter, loc, tileType, reducedWindow, scaleSlice);
-          }
-
-          SmallVector<OpFoldResult> outputOffsets = {
-            batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
-          SmallVector<OpFoldResult> outputSizes = {rewriter.getIndexAttr(1),
-                                                   rewriter.getIndexAttr(tileChannels),
-                                                   rewriter.getIndexAttr(1),
-                                                   rewriter.getIndexAttr(1)};
-          SmallVector<OpFoldResult> outputStrides = {
-            rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-          updatedOutput = tensor::InsertSliceOp::create(
-            rewriter, loc, reducedWindow, updatedOutput, outputOffsets, outputSizes, outputStrides);
-        }
-
-        scf::YieldOp::create(rewriter, loc, updatedOutput);
-
-        rewriter.setInsertionPointAfter(outputLoop);
-        spatial::SpatYieldOp::create(rewriter, loc, outputLoop.getResult(0));
+        spatial::SpatYieldOp::create(rewriter, loc, outputLoop->results.front());
         return success();
       });
     if (failed(computeOp))

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Softmax.cpp

@@ -3,6 +3,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Transforms/DialectConversion.h"
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -42,13 +43,13 @@ static Value buildLoopSoftmaxSlice(Value input,
   return tensor::InsertSliceOp::create(rewriter, loc, softmaxSlice, accumulator, offsets, sizes, strides);
 }
 
-static Value buildLoopSoftmaxNest(Value input,
-                                  Value accumulator,
-                                  RankedTensorType inputType,
-                                  int64_t axis,
-                                  SmallVectorImpl<Value>& outerIndices,
-                                  ConversionPatternRewriter& rewriter,
-                                  Location loc) {
+static FailureOr<Value> buildLoopSoftmaxNest(Value input,
+                                             Value accumulator,
+                                             RankedTensorType inputType,
+                                             int64_t axis,
+                                             SmallVectorImpl<Value>& outerIndices,
+                                             ConversionPatternRewriter& rewriter,
+                                             Location loc) {
   if (axis == inputType.getRank() - 1)
     return buildLoopSoftmaxSlice(input, accumulator, inputType, outerIndices, rewriter, loc);
 
@@ -57,38 +58,50 @@ static Value buildLoopSoftmaxNest(Value input,
   Value c1 = getOrCreateIndexConstant(rewriter, anchorOp, 1);
   Value cUpper = getOrCreateIndexConstant(rewriter, anchorOp, inputType.getDimSize(axis));
 
-  auto loop = scf::ForOp::create(rewriter, loc, c0, cUpper, c1, ValueRange {accumulator});
-  rewriter.setInsertionPointToStart(loop.getBody());
-
-  Value loopIndex = loop.getInductionVar();
-  Value loopAccumulator = loop.getRegionIterArgs().front();
-  outerIndices.push_back(loopIndex);
-  Value updatedAccumulator =
-    buildLoopSoftmaxNest(input, loopAccumulator, inputType, axis + 1, outerIndices, rewriter, loc);
-  outerIndices.pop_back();
-
-  scf::YieldOp::create(rewriter, loc, updatedAccumulator);
-  rewriter.setInsertionPointAfter(loop);
-  return loop.getResult(0);
+  auto loop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cUpper,
+    c1,
+    ValueRange {accumulator},
+    [&](OpBuilder& builder, Location nestedLoc, Value loopIndex, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+      outerIndices.push_back(loopIndex);
+      auto updatedAccumulator =
+        buildLoopSoftmaxNest(input, iterArgs.front(), inputType, axis + 1, outerIndices, rewriter, nestedLoc);
+      outerIndices.pop_back();
+      if (failed(updatedAccumulator))
+        return failure();
+      yielded.push_back(*updatedAccumulator);
+      return success();
+    });
+  if (failed(loop))
+    return failure();
+  return loop->results.front();
 }
 
-static Value createLoopSoftmaxCompute(Value input, ConversionPatternRewriter& rewriter, Location loc) {
+static FailureOr<Value> createLoopSoftmaxCompute(Value input, ConversionPatternRewriter& rewriter, Location loc) {
   auto inputType = cast<RankedTensorType>(input.getType());
   constexpr size_t numInputs = 1;
-  auto computeOp =
-    createSpatCompute<numInputs>(rewriter, loc, TypeRange {inputType}, {}, ValueRange {input}, [&](Value x) {
+  auto computeOp = createSpatCompute<numInputs>(
+    rewriter, loc, TypeRange {inputType}, {}, ValueRange {input}, [&](Value x) -> LogicalResult {
       if (inputType.getRank() == 1) {
         Value softmax = spatial::SpatSoftmaxOp::create(rewriter, loc, inputType, x).getResult();
         spatial::SpatYieldOp::create(rewriter, loc, softmax);
-        return;
+        return success();
       }
 
       Value outputInit = tensor::EmptyOp::create(rewriter, loc, inputType.getShape(), inputType.getElementType());
       SmallVector<Value> outerIndices;
-      Value result = buildLoopSoftmaxNest(x, outputInit, inputType, /*axis=*/0, outerIndices, rewriter, loc);
-      spatial::SpatYieldOp::create(rewriter, loc, result);
+      auto result = buildLoopSoftmaxNest(x, outputInit, inputType, /*axis=*/0, outerIndices, rewriter, loc);
+      if (failed(result))
+        return failure();
+      spatial::SpatYieldOp::create(rewriter, loc, *result);
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
@@ -108,7 +121,10 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
     Value input = adaptor.getInput();
     Value result;
     if (*axis == inputType.getRank() - 1) {
-      result = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
+      auto computed = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
+      if (failed(computed))
+        return failure();
+      result = *computed;
     }
     else {
       SmallVector<int64_t> permutation;
@@ -122,8 +138,10 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
       auto transposedType = RankedTensorType::get(
         permuteShape(inputType.getShape(), permutation), inputType.getElementType(), inputType.getEncoding());
       Value transposedInput = transposeMaybeInCompute(input, transposedType, permutation, rewriter, softmaxOp.getLoc());
-      Value transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
-      result = transposeMaybeInCompute(transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
+      auto transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
+      if (failed(transposedResult))
+        return failure();
+      result = transposeMaybeInCompute(*transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
     }
 
     rewriter.replaceOp(softmaxOp, result);

src/PIM/Conversion/ONNXToSpatial/Patterns/Post.cpp

@@ -9,6 +9,7 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/WeightMaterialization.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -192,13 +193,12 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
 
     rewriter.setInsertionPointAfter(compute);
 
-    auto newCompute =
-      spatial::SpatComputeBatch::create(rewriter,
-                                        compute.getLoc(),
-                                        compute.getResultTypes(),
-                                        rewriter.getI32IntegerAttr(static_cast<int32_t>(compute.getLaneCount())),
-                                        promoted->newWeights,
-                                        promoted->newInputs);
+    auto laneCountAttr = pim::getCheckedI32Attr(
+      rewriter, compute, static_cast<uint64_t>(compute.getLaneCount()), "promoted compute_batch lane count");
+    if (failed(laneCountAttr))
+      return failure();
+    auto newCompute = spatial::SpatComputeBatch::create(
+      rewriter, compute.getLoc(), compute.getResultTypes(), *laneCountAttr, promoted->newWeights, promoted->newInputs);
     auto laneArg = compute.getLaneArgument();
     if (!laneArg)
       return rewriter.notifyMatchFailure(compute, "missing compute_batch lane block argument");

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Resize.cpp

@@ -5,6 +5,7 @@
 
 #include "llvm/ADT/STLExtras.h"
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -26,11 +27,11 @@ static Value buildNearestAsymmetricIndex(
   return arith::MinUIOp::create(rewriter, loc, inputIndex, cInputDimLast);
 }
 
-static Value buildNearestResizeLoop(Value input,
-                                    RankedTensorType inputType,
-                                    RankedTensorType resultType,
-                                    ConversionPatternRewriter& rewriter,
-                                    Location loc) {
+static FailureOr<Value> buildNearestResizeLoop(Value input,
+                                               RankedTensorType inputType,
+                                               RankedTensorType resultType,
+                                               ConversionPatternRewriter& rewriter,
+                                               Location loc) {
   auto elemType = resultType.getElementType();
   SmallVector<int64_t> unitShape(resultType.getRank(), 1);
   auto unitTensorType = RankedTensorType::get(unitShape, elemType);
@@ -48,54 +49,94 @@ static Value buildNearestResizeLoop(Value input,
 
   Value outputInit = tensor::EmptyOp::create(rewriter, loc, resultType.getShape(), elemType);
 
-  auto batchLoop = scf::ForOp::create(rewriter, loc, c0, cOutputN, c1, ValueRange {outputInit});
-  rewriter.setInsertionPointToStart(batchLoop.getBody());
+  auto batchLoop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cOutputN,
+    c1,
+    ValueRange {outputInit},
+    [&](OpBuilder&, Location nestedLoc, Value outputN, ValueRange batchIterArgs, SmallVectorImpl<Value>& batchYielded) {
+      Value outputBatchAcc = batchIterArgs.front();
+      Value inputN =
+        buildNearestAsymmetricIndex(outputN, inputType.getDimSize(0), resultType.getDimSize(0), rewriter, nestedLoc);
 
-  Value outputN = batchLoop.getInductionVar();
-  Value outputBatchAcc = batchLoop.getRegionIterArgs().front();
-  Value inputN = buildNearestAsymmetricIndex(outputN, inputType.getDimSize(0), resultType.getDimSize(0), rewriter, loc);
+      auto channelLoop = buildNormalizedScfFor(
+        rewriter,
+        nestedLoc,
+        c0,
+        cOutputC,
+        c1,
+        ValueRange {outputBatchAcc},
+        [&](OpBuilder&,
+            Location channelLoc,
+            Value outputC,
+            ValueRange channelIterArgs,
+            SmallVectorImpl<Value>& channelYielded) {
+          Value outputChannelAcc = channelIterArgs.front();
+          Value inputC = buildNearestAsymmetricIndex(
+            outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, channelLoc);
 
-  auto channelLoop = scf::ForOp::create(rewriter, loc, c0, cOutputC, c1, ValueRange {outputBatchAcc});
-  rewriter.setInsertionPointToStart(channelLoop.getBody());
+          auto heightLoop = buildNormalizedScfFor(
+            rewriter,
+            channelLoc,
+            c0,
+            cOutputH,
+            c1,
+            ValueRange {outputChannelAcc},
+            [&](OpBuilder&,
+                Location heightLoc,
+                Value outputH,
+                ValueRange heightIterArgs,
+                SmallVectorImpl<Value>& heightYielded) {
+              Value outputHeightAcc = heightIterArgs.front();
+              Value inputH = buildNearestAsymmetricIndex(
+                outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, heightLoc);
 
-  Value outputC = channelLoop.getInductionVar();
-  Value outputChannelAcc = channelLoop.getRegionIterArgs().front();
-  Value inputC = buildNearestAsymmetricIndex(outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, loc);
+              auto widthLoop = buildNormalizedScfFor(
+                rewriter,
+                heightLoc,
+                c0,
+                cOutputW,
+                c1,
+                ValueRange {outputHeightAcc},
+                [&](OpBuilder&,
+                    Location widthLoc,
+                    Value outputW,
+                    ValueRange widthIterArgs,
+                    SmallVectorImpl<Value>& widthYielded) {
+                  Value outputWidthAcc = widthIterArgs.front();
+                  Value inputW = buildNearestAsymmetricIndex(
+                    outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, widthLoc);
 
-  auto heightLoop = scf::ForOp::create(rewriter, loc, c0, cOutputH, c1, ValueRange {outputChannelAcc});
-  rewriter.setInsertionPointToStart(heightLoop.getBody());
+                  SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
+                  Value inputSlice = tensor::ExtractSliceOp::create(
+                    rewriter, widthLoc, unitTensorType, input, inputOffsets, unitSizes, unitStrides);
 
-  Value outputH = heightLoop.getInductionVar();
-  Value outputHeightAcc = heightLoop.getRegionIterArgs().front();
-  Value inputH = buildNearestAsymmetricIndex(outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, loc);
-
-  auto widthLoop = scf::ForOp::create(rewriter, loc, c0, cOutputW, c1, ValueRange {outputHeightAcc});
-  rewriter.setInsertionPointToStart(widthLoop.getBody());
-
-  Value outputW = widthLoop.getInductionVar();
-  Value outputWidthAcc = widthLoop.getRegionIterArgs().front();
-  Value inputW = buildNearestAsymmetricIndex(outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, loc);
-
-  SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
-  Value inputSlice =
-    tensor::ExtractSliceOp::create(rewriter, loc, unitTensorType, input, inputOffsets, unitSizes, unitStrides);
-
-  SmallVector<OpFoldResult> outputOffsets = {outputN, outputC, outputH, outputW};
-  Value updatedOutput =
-    tensor::InsertSliceOp::create(rewriter, loc, inputSlice, outputWidthAcc, outputOffsets, unitSizes, unitStrides);
-  scf::YieldOp::create(rewriter, loc, updatedOutput);
-
-  rewriter.setInsertionPointAfter(widthLoop);
-  scf::YieldOp::create(rewriter, loc, widthLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(heightLoop);
-  scf::YieldOp::create(rewriter, loc, heightLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(channelLoop);
-  scf::YieldOp::create(rewriter, loc, channelLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(batchLoop);
-  return batchLoop.getResult(0);
+                  SmallVector<OpFoldResult> outputOffsets = {outputN, outputC, outputH, outputW};
+                  Value updatedOutput = tensor::InsertSliceOp::create(
+                    rewriter, widthLoc, inputSlice, outputWidthAcc, outputOffsets, unitSizes, unitStrides);
+                  widthYielded.push_back(updatedOutput);
+                  return success();
+                });
+              if (failed(widthLoop))
+                return failure();
+              heightYielded.push_back(widthLoop->results.front());
+              return success();
+            });
+          if (failed(heightLoop))
+            return failure();
+          channelYielded.push_back(heightLoop->results.front());
+          return success();
+        });
+      if (failed(channelLoop))
+        return failure();
+      batchYielded.push_back(channelLoop->results.front());
+      return success();
+    });
+  if (failed(batchLoop))
+    return failure();
+  return batchLoop->results.front();
 }
 
 struct Resize : OpConversionPattern<ONNXResizeOp> {
@@ -120,12 +161,17 @@ struct Resize : OpConversionPattern<ONNXResizeOp> {
         || llvm::any_of(resultType.getShape(), [](int64_t dim) { return dim <= 0; }))
       return rewriter.notifyMatchFailure(resizeOp, "resize lowering requires positive static dimensions.");
 
-    auto computeOp =
-      createSpatCompute<1>(rewriter, resizeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getX(), [&](Value x) {
-        Value result = buildNearestResizeLoop(x, inputType, resultType, rewriter, resizeOp.getLoc());
-        spatial::SpatYieldOp::create(rewriter, resizeOp.getLoc(), result);
+    auto computeOp = createSpatCompute<1>(
+      rewriter, resizeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getX(), [&](Value x) -> LogicalResult {
+        auto result = buildNearestResizeLoop(x, inputType, resultType, rewriter, resizeOp.getLoc());
+        if (failed(result))
+          return failure();
+        spatial::SpatYieldOp::create(rewriter, resizeOp.getLoc(), *result);
+        return success();
       });
-    rewriter.replaceOp(resizeOp, computeOp.getResults());
+    if (failed(computeOp))
+      return failure();
+    rewriter.replaceOp(resizeOp, computeOp->getResults());
     return success();
   }
 };