automatic code reformat

src/PIM/Common/IR/ConstantUtils.hpp

@@ -10,15 +10,11 @@ namespace onnx_mlir {
 
 mlir::Block* getConstantInsertionBlock(mlir::Operation* anchorOp);
 
-mlir::Value getOrCreateConstant(mlir::OperationFolder& folder,
+mlir::Value
-                                mlir::Operation* anchorOp,
+getOrCreateConstant(mlir::OperationFolder& folder, mlir::Operation* anchorOp, mlir::Attribute value, mlir::Type type);
-                                mlir::Attribute value,
-                                mlir::Type type);
 
-mlir::Value getOrCreateConstant(mlir::RewriterBase& rewriter,
+mlir::Value
-                                mlir::Operation* anchorOp,
+getOrCreateConstant(mlir::RewriterBase& rewriter, mlir::Operation* anchorOp, mlir::Attribute value, mlir::Type type);
-                                mlir::Attribute value,
-                                mlir::Type type);
 
 mlir::Value getOrCreateConstantLike(mlir::OperationFolder& folder, mlir::arith::ConstantOp constantOp);
 

src/PIM/Common/IR/WeightUtils.cpp

@@ -1,5 +1,5 @@
-#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/BuiltinTypes.h"
 

src/PIM/Conversion/ONNXToSpatial/Common/AttributeUtils.cpp

@@ -1,7 +1,7 @@
-#include "AttributeUtils.hpp"
-
 #include "mlir/IR/BuiltinAttributes.h"
 
+#include "AttributeUtils.hpp"
+
 using namespace mlir;
 
 namespace onnx_mlir {

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

@@ -1,5 +1,6 @@
 #pragma once
 
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/Block.h"
 #include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/ValueRange.h"
@@ -11,8 +12,6 @@
 #include <type_traits>
 #include <utility>
 
-#include "mlir/Dialect/Tensor/IR/Tensor.h"
-
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
@@ -209,8 +208,7 @@ auto createSpatComputeBatch(RewriterT& rewriter,
     block->getArgument(0),
     mlir::ValueRange(block->getArguments()).slice(1, weights.size()),
     mlir::ValueRange(block->getArguments()).slice(1 + weights.size(), inputs.size()),
-    mlir::ValueRange(block->getArguments()).drop_front(1 + weights.size() + inputs.size())
+    mlir::ValueRange(block->getArguments()).drop_front(1 + weights.size() + inputs.size())};
-  };
 
   using BodyResult = std::invoke_result_t<BodyFn, detail::SpatComputeBatchBodyArgs>;
   if constexpr (std::is_same_v<BodyResult, void>) {
@@ -252,8 +250,8 @@ mlir::Value materializeOrComputeUnary(mlir::Value input,
   if (isCompileTimeComputable(input))
     return buildFn(input);
 
-  auto computeOp =
+  auto computeOp = createSpatCompute<1>(
-    createSpatCompute<1>(rewriter, loc, mlir::TypeRange {resultType}, {}, mlir::ValueRange {input}, [&](mlir::Value computeInput) {
+    rewriter, loc, mlir::TypeRange {resultType}, {}, mlir::ValueRange {input}, [&](mlir::Value computeInput) {
       mlir::Value result = buildFn(computeInput);
       spatial::SpatYieldOp::create(rewriter, loc, result);
     });

src/PIM/Conversion/ONNXToSpatial/Common/IndexingUtils.cpp

@@ -1,11 +1,10 @@
-#include "IndexingUtils.hpp"
-
 #include "mlir/Dialect/Arith/IR/Arith.h"
 
 #include "llvm/ADT/APInt.h"
 
 #include <algorithm>
 
+#include "IndexingUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 
 using namespace mlir;
@@ -85,7 +84,8 @@ Value floorDivIndexByConstant(PatternRewriter& rewriter, Location loc, Value val
 
 Value getOrMaterializeIndexValue(PatternRewriter& rewriter, OpFoldResult value) {
   if (auto attr = dyn_cast<Attribute>(value))
-    return getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), cast<IntegerAttr>(attr).getInt());
+    return getOrCreateIndexConstant(
+      rewriter, rewriter.getInsertionBlock()->getParentOp(), cast<IntegerAttr>(attr).getInt());
   return cast<Value>(value);
 }
 

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

@@ -26,8 +26,10 @@ mlir::Value createAffineApplyOrFoldedConstant(mlir::PatternRewriter& rewriter,
                                               mlir::AffineExpr expr,
                                               mlir::ValueRange operands);
 
-mlir::Value
+mlir::Value multiplyIndexByConstant(mlir::PatternRewriter& rewriter,
-multiplyIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Operation* anchorOp, mlir::Value value, int64_t multiplier);
+                                    mlir::Operation* anchorOp,
+                                    mlir::Value value,
+                                    int64_t multiplier);
 
 mlir::Value modIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value value, int64_t divisor);
 

src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.cpp

@@ -8,8 +8,8 @@
 #include <algorithm>
 #include <functional>
 
-#include "ShapeTilingUtils.hpp"
 #include "IndexingUtils.hpp"
+#include "ShapeTilingUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -53,7 +53,9 @@ bool hasStaticPositiveShape(ArrayRef<int64_t> shape) {
   return llvm::all_of(shape, [](int64_t dim) { return dim > 0; });
 }
 
-bool hasStaticPositiveShape(RankedTensorType type) { return type.hasStaticShape() && hasStaticPositiveShape(type.getShape()); }
+bool hasStaticPositiveShape(RankedTensorType type) {
+  return type.hasStaticShape() && hasStaticPositiveShape(type.getShape());
+}
 
 int64_t getStaticShapeElementCount(ArrayRef<int64_t> shape) {
   return std::accumulate(shape.begin(), shape.end(), int64_t {1}, std::multiplies<int64_t> {});
@@ -98,11 +100,8 @@ FailureOr<SmallVector<int64_t>> getTransposePermutationChecked(std::optional<Arr
   return permutation;
 }
 
-Value transposeMaybeInCompute(Value value,
+Value transposeMaybeInCompute(
-                              RankedTensorType resultType,
+  Value value, RankedTensorType resultType, ArrayRef<int64_t> permutation, PatternRewriter& rewriter, Location loc) {
-                              ArrayRef<int64_t> permutation,
-                              PatternRewriter& rewriter,
-                              Location loc) {
   auto buildTranspose = [&](Value input) -> Value {
     return ONNXTransposeOp::create(rewriter, loc, resultType, input, rewriter.getI64ArrayAttr(permutation)).getResult();
   };
@@ -127,7 +126,8 @@ SmallVector<OpFoldResult> getStaticSizes(PatternRewriter& rewriter, ArrayRef<int
 
 static bool isContiguousTensorSlice(Value source, RankedTensorType resultType, ArrayRef<OpFoldResult> strides) {
   auto sourceType = dyn_cast<RankedTensorType>(source.getType());
-  if (!sourceType || !sourceType.hasStaticShape() || !resultType.hasStaticShape() || sourceType.getRank() != resultType.getRank())
+  if (!sourceType || !sourceType.hasStaticShape() || !resultType.hasStaticShape()
+      || sourceType.getRank() != resultType.getRank())
     return false;
 
   for (OpFoldResult stride : strides) {
@@ -290,7 +290,8 @@ Value materializeContiguousTensorSlice(Value source,
     }
 
     Value lower = zeroIndices[dim];
-    Value upper = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), resultType.getDimSize(dim));
+    Value upper =
+      getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), resultType.getDimSize(dim));
     Value step = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
     auto loop = scf::ForOp::create(rewriter, loc, lower, upper, step, ValueRange {accumulator});
     rewriter.setInsertionPointToStart(loop.getBody());
@@ -316,7 +317,8 @@ Value extractAxisSlice(
   SmallVector<OpFoldResult> sizes = getStaticSizes(rewriter, sourceType.getShape());
   offsets[axis] = rewriter.getIndexAttr(offset);
   sizes[axis] = rewriter.getIndexAttr(size);
-  return tensor::ExtractSliceOp::create(rewriter, loc, resultType, source, offsets, sizes, getUnitStrides(rewriter, sourceType.getRank()))
+  return tensor::ExtractSliceOp::create(
+           rewriter, loc, resultType, source, offsets, sizes, getUnitStrides(rewriter, sourceType.getRank()))
     .getResult();
 }
 

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

@@ -115,12 +115,8 @@ mlir::Value materializeContiguousTensorSlice(mlir::Value source,
                                              mlir::ConversionPatternRewriter& rewriter,
                                              mlir::Location loc);
 
-mlir::Value extractAxisSlice(mlir::PatternRewriter& rewriter,
+mlir::Value extractAxisSlice(
-                             mlir::Location loc,
+  mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value source, int64_t axis, int64_t offset, int64_t size);
-                             mlir::Value source,
-                             int64_t axis,
-                             int64_t offset,
-                             int64_t size);
 
 mlir::Value insertStaticSlice(mlir::PatternRewriter& rewriter,
                               mlir::Location loc,

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -15,8 +15,8 @@
 #include "Common/Common.hpp"
 #include "Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialVerifier.cpp

@@ -69,9 +69,9 @@ LogicalResult verifyComputeLikeInputs(Operation* computeLikeOp,
       continue;
 
     diagnostics.report(computeLikeOp, [&](Operation* illegalOp) {
-      InFlightDiagnostic diagnostic = illegalOp->emitOpError() << kind << " input #" << currentInputIndex
+      InFlightDiagnostic diagnostic = illegalOp->emitOpError()
-                                                               << (allowChannelReceiveInputs
+                                   << kind << " input #" << currentInputIndex
-                                                                     ? " must come from the host or an explicit "
+                                   << (allowChannelReceiveInputs ? " must come from the host or an explicit "
                                                                    "spat.channel_receive"
                                                                  : " must come from the host");
       if (definingOp)

src/PIM/Conversion/ONNXToSpatial/Patterns.cpp

@@ -5,9 +5,7 @@ using namespace mlir;
 
 namespace onnx_mlir {
 
-void populatePrePatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
+void populatePrePatterns(RewritePatternSet& patterns, MLIRContext* ctx) { populateGeneratedPrePatterns(patterns, ctx); }
-  populateGeneratedPrePatterns(patterns, ctx);
-}
 
 void populateConversionPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
   populateGeneratedConversionPatterns(patterns, ctx);

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

@@ -51,8 +51,8 @@ static Value createPaddedRows(Value tensorValue,
   if (tensorType.getDimSize(0) == paddedRows)
     return tensorValue;
 
-  auto paddedType =
+  auto paddedType = RankedTensorType::get(
-    RankedTensorType::get({paddedRows, tensorType.getDimSize(1)}, tensorType.getElementType(), tensorType.getEncoding());
+    {paddedRows, tensorType.getDimSize(1)}, tensorType.getElementType(), tensorType.getEncoding());
   SmallVector<OpFoldResult> lowPads = {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
   SmallVector<OpFoldResult> highPads = {rewriter.getIndexAttr(paddedRows - tensorType.getDimSize(0)),
                                         rewriter.getIndexAttr(0)};
@@ -62,20 +62,15 @@ static Value createPaddedRows(Value tensorValue,
     padBlock->addArgument(rewriter.getIndexType(), loc);
   padOp.getRegion().push_back(padBlock);
   rewriter.setInsertionPointToStart(padBlock);
-  auto zero = getOrCreateConstant(rewriter,
+  auto zero = getOrCreateConstant(
-                                  padOp.getOperation(),
+    rewriter, padOp.getOperation(), rewriter.getZeroAttr(tensorType.getElementType()), tensorType.getElementType());
-                                  rewriter.getZeroAttr(tensorType.getElementType()),
-                                  tensorType.getElementType());
   tensor::YieldOp::create(rewriter, loc, zero);
   rewriter.setInsertionPointAfter(padOp);
   return padOp.getResult();
 }
 
-static Value packRowsForParallelGemm(Value rows,
+static Value packRowsForParallelGemm(
-                                     RankedTensorType rowsType,
+  Value rows, RankedTensorType rowsType, int64_t packFactor, ConversionPatternRewriter& rewriter, Location loc) {
-                                     int64_t packFactor,
-                                     ConversionPatternRewriter& rewriter,
-                                     Location loc) {
   if (packFactor == 1)
     return rows;
 
@@ -118,10 +113,8 @@ static Value unpackRowsFromParallelGemm(Value packedRows,
 
   const int64_t packedNumRows = packedRowsType.getDimSize(0);
   const int64_t paddedNumRows = packedNumRows * packFactor;
-  auto expandedType =
+  auto expandedType = RankedTensorType::get(
-    RankedTensorType::get({packedNumRows, packFactor, rowWidth},
+    {packedNumRows, packFactor, rowWidth}, packedRowsType.getElementType(), packedRowsType.getEncoding());
-                          packedRowsType.getElementType(),
-                          packedRowsType.getEncoding());
   auto paddedType =
     RankedTensorType::get({paddedNumRows, rowWidth}, packedRowsType.getElementType(), packedRowsType.getEncoding());
   auto unpackedType =
@@ -193,11 +186,8 @@ static Value buildPackedWeight(DenseElementsAttr wDenseAttr,
   return getOrCreateConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), packedAttr, packedWeightType);
 }
 
-static Value createConvWeightMatrix(Value w,
+static Value createConvWeightMatrix(
-                                    RankedTensorType wFlatType,
+  Value w, RankedTensorType wFlatType, RankedTensorType wTransType, ConversionPatternRewriter& rewriter, Location loc) {
-                                    RankedTensorType wTransType,
-                                    ConversionPatternRewriter& rewriter,
-                                    Location loc) {
   auto buildWeightMatrix = [&](Value weight) -> Value {
     Value wFlat = tensor::CollapseShapeOp::create(rewriter,
                                                   loc,
@@ -360,9 +350,8 @@ static Value createIm2colRowComputes(Value x,
       Value im2col = im2colLoop.getResult(0);
 
       Value gemmInputRows = im2col;
-      if (packFactor != 1) {
+      if (packFactor != 1)
         gemmInputRows = packRowsForParallelGemm(im2col, im2colType, packFactor, rewriter, loc);
-      }
 
       spatial::SpatYieldOp::create(rewriter, loc, gemmInputRows);
     });
@@ -387,8 +376,13 @@ static Value createCollectedConvOutput(ValueRange gemmRows,
     }
     else {
       Value packedOutput = createSpatConcat(rewriter, loc, /*axis=*/0, gemmRowArgs);
-      gemmOut = unpackRowsFromParallelGemm(
+      gemmOut = unpackRowsFromParallelGemm(packedOutput,
-        packedOutput, cast<RankedTensorType>(packedOutput.getType()), numPatches, numChannelsOut, packFactor, rewriter, loc);
+                                           cast<RankedTensorType>(packedOutput.getType()),
+                                           numPatches,
+                                           numChannelsOut,
+                                           packFactor,
+                                           rewriter,
+                                           loc);
     }
 
     // Restore to NCHW layout:

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

@@ -252,7 +252,13 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
                                                 Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
   auto batchOp = createSpatComputeBatch(
-    rewriter, loc, TypeRange {partialPiecesType}, laneCount, ValueRange {b}, ValueRange {a}, [&](detail::SpatComputeBatchBodyArgs args) {
+    rewriter,
+    loc,
+    TypeRange {partialPiecesType},
+    laneCount,
+    ValueRange {b},
+    ValueRange {a},
+    [&](detail::SpatComputeBatchBodyArgs args) {
       Value row = onnx_mlir::modIndexByConstant(rewriter, loc, args.lane, numOutRows);
       Value kOffset = createGemmBatchKOffset(args.lane, numOutRows, numKSlices, rewriter, loc);
       Value hOffset = createGemmBatchHOffset(args.lane, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
@@ -284,8 +290,8 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
   return *batchOp;
 }
 
-static Value createDynamicGemmBatchRow(
+static Value
-  Value lane, int64_t numOutCols, ConversionPatternRewriter& rewriter, Location loc) {
+createDynamicGemmBatchRow(Value lane, int64_t numOutCols, ConversionPatternRewriter& rewriter, Location loc) {
   if (numOutCols == 1)
     return lane;
 
@@ -294,17 +300,21 @@ static Value createDynamicGemmBatchRow(
   return createAffineApplyOrFoldedConstant(rewriter, loc, d0.floorDiv(numOutCols), ValueRange {lane});
 }
 
-static Value
+static Value extractDynamicGemmBColumn(
-extractDynamicGemmBColumn(Value matrix, Value column, RankedTensorType vectorType, ConversionPatternRewriter& rewriter, Location loc) {
+  Value matrix, Value column, RankedTensorType vectorType, ConversionPatternRewriter& rewriter, Location loc) {
   SmallVector<OpFoldResult> offsets {rewriter.getIndexAttr(0), column};
   SmallVector<OpFoldResult> strides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
   auto columnSliceType = RankedTensorType::get({vectorType.getDimSize(1), 1}, vectorType.getElementType());
   Value columnSlice = materializeContiguousTensorSlice(matrix, columnSliceType, offsets, strides, rewriter, loc);
-  SmallVector<ReassociationIndices> collapseReassociation {ReassociationIndices {0, 1}};
+  SmallVector<ReassociationIndices> collapseReassociation {
+    ReassociationIndices {0, 1}
+  };
   auto collapsedType = RankedTensorType::get({vectorType.getDimSize(1)}, vectorType.getElementType());
   Value collapsed =
     tensor::CollapseShapeOp::create(rewriter, loc, collapsedType, columnSlice, collapseReassociation).getResult();
-  SmallVector<ReassociationIndices> expandReassociation {ReassociationIndices {0, 1}};
+  SmallVector<ReassociationIndices> expandReassociation {
+    ReassociationIndices {0, 1}
+  };
   return tensor::ExpandShapeOp::create(rewriter, loc, vectorType, collapsed, expandReassociation).getResult();
 }
 
@@ -371,13 +381,15 @@ static Value createBroadcastedBiasScalar(Value bias,
                                          Location loc) {
   SmallVector<OpFoldResult> unitStrides(biasType.getRank(), rewriter.getIndexAttr(1));
   if (biasType.getRank() == 1) {
-    SmallVector<OpFoldResult> offsets {
+    SmallVector<OpFoldResult> offsets {biasType.getDimSize(0) == 1 ? OpFoldResult(rewriter.getIndexAttr(0))
-      biasType.getDimSize(0) == 1 ? OpFoldResult(rewriter.getIndexAttr(0)) : OpFoldResult(column)};
+                                                                   : OpFoldResult(column)};
     SmallVector<OpFoldResult> sizes {rewriter.getIndexAttr(1)};
     auto vectorType = RankedTensorType::get({1}, scalarType.getElementType());
-    Value vector = tensor::ExtractSliceOp::create(rewriter, loc, vectorType, bias, offsets, sizes, unitStrides)
+    Value vector =
-                     .getResult();
+      tensor::ExtractSliceOp::create(rewriter, loc, vectorType, bias, offsets, sizes, unitStrides).getResult();
-    SmallVector<ReassociationIndices> reassociation {ReassociationIndices {0, 1}};
+    SmallVector<ReassociationIndices> reassociation {
+      ReassociationIndices {0, 1}
+    };
     return tensor::ExpandShapeOp::create(rewriter, loc, scalarType, vector, reassociation).getResult();
   }
 
@@ -407,15 +419,20 @@ static spatial::SpatComputeBatch createVvdmulBatch(Value a,
   const int64_t reductionSize = aType.getDimSize(1);
   const int64_t laneCount = numOutRows * numOutCols;
   auto batchOp = createSpatComputeBatch(
-    rewriter, loc, TypeRange {scalarPiecesType}, laneCount, ValueRange {}, ValueRange {a, b}, [&](detail::SpatComputeBatchBodyArgs args) {
+    rewriter,
+    loc,
+    TypeRange {scalarPiecesType},
+    laneCount,
+    ValueRange {},
+    ValueRange {a, b},
+    [&](detail::SpatComputeBatchBodyArgs args) {
       Value row = createDynamicGemmBatchRow(args.lane, numOutCols, rewriter, loc);
       Value column = onnx_mlir::modIndexByConstant(rewriter, loc, args.lane, numOutCols);
 
       auto vectorType = RankedTensorType::get({1, reductionSize}, aType.getElementType());
       auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
       Value aVector = extractDynamicGemmRowVector(args.inputs[0], row, vectorType, rewriter, loc);
-      Value bVector = bAlreadyTransposed
+      Value bVector = bAlreadyTransposed ? extractTransposedBRow(args.inputs[1], column, vectorType, rewriter, loc)
-                        ? extractTransposedBRow(args.inputs[1], column, vectorType, rewriter, loc)
                                          : extractDynamicGemmBColumn(args.inputs[1], column, vectorType, rewriter, loc);
       Value scalar = spatial::SpatVVDMulOp::create(rewriter, loc, scalarType, aVector, bVector).getResult();
 
@@ -578,9 +595,8 @@ static spatial::SpatCompute createReductionCompute(Value partialPieces,
     auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
       Value reduced =
         reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
-      Value hOffset =
+      Value hOffset = onnx_mlir::multiplyIndexByConstant(
-        onnx_mlir::multiplyIndexByConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), hSlice,
+        rewriter, rewriter.getInsertionBlock()->getParentOp(), hSlice, crossbarSize.getValue());
-                                           crossbarSize.getValue());
       if (biasArg) {
         SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
         Value biasSlice =
@@ -721,8 +737,8 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     }
 
     auto scalarPiecesType = RankedTensorType::get({laneCount64, 1}, outType.getElementType());
-    auto batchOp = createVvdmulBatch(
+    auto batchOp =
-      a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
+      createVvdmulBatch(a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
     auto outputCompute = createDynamicGemmOutputCompute(
       batchOp.getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
     rewriter.replaceOp(gemmOp, outputCompute.getResults());

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

@@ -70,11 +70,8 @@ static SmallVector<int64_t> getKeptAxes(ArrayRef<bool> reducedAxes) {
   return keptAxes;
 }
 
-static Value computeLaneIndex(Value lane,
+static Value
-                              int64_t stride,
+computeLaneIndex(Value lane, int64_t stride, int64_t dimSize, ConversionPatternRewriter& rewriter, Location loc) {
-                              int64_t dimSize,
-                              ConversionPatternRewriter& rewriter,
-                              Location loc) {
   if (dimSize == 1)
     return getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
 
@@ -119,8 +116,14 @@ static FailureOr<Value> buildReduceMeanKeepdimsBatch(Value input,
   sliceSizes.reserve(inputType.getRank());
   insertOffsets.reserve(inputType.getRank());
 
-  auto batchOp = createSpatComputeBatch(
+  auto batchOp =
-    rewriter, loc, TypeRange {batchType}, laneCount, {}, ValueRange {input}, [&](detail::SpatComputeBatchBodyArgs args) {
+    createSpatComputeBatch(rewriter,
+                           loc,
+                           TypeRange {batchType},
+                           laneCount,
+                           {},
+                           ValueRange {input},
+                           [&](detail::SpatComputeBatchBodyArgs args) {
                              size_t keptAxisIndex = 0;
                              sliceOffsets.clear();
                              sliceSizes.clear();
@@ -132,8 +135,8 @@ static FailureOr<Value> buildReduceMeanKeepdimsBatch(Value input,
                                  continue;
                                }
 
-        Value axisIndex =
+                               Value axisIndex = computeLaneIndex(
-          computeLaneIndex(args.lane, keptAxisStrides[keptAxisIndex], inputType.getDimSize(axis), rewriter, loc);
+                                 args.lane, keptAxisStrides[keptAxisIndex], inputType.getDimSize(axis), rewriter, loc);
                                ++keptAxisIndex;
                                sliceOffsets.push_back(axisIndex);
                                sliceSizes.push_back(rewriter.getIndexAttr(1));
@@ -142,8 +145,8 @@ static FailureOr<Value> buildReduceMeanKeepdimsBatch(Value input,
                              insertOffsets.push_back(args.lane);
                              insertOffsets.append(inputType.getRank() - 1, rewriter.getIndexAttr(0));
 
-      Value slice =
+                             Value slice = tensor::ExtractSliceOp::create(
-        tensor::ExtractSliceOp::create(rewriter, loc, sliceType, args.inputs.front(), sliceOffsets, sliceSizes, unitStrides);
+                               rewriter, loc, sliceType, args.inputs.front(), sliceOffsets, sliceSizes, unitStrides);
                              Value reduced = spatial::SpatVAvgOp::create(rewriter, loc, leafType, slice).getResult();
                              createParallelInsertSliceIntoBatchOutput(
                                rewriter, loc, reduced, args.outputs.front(), insertOffsets, insertSizes, unitStrides);
@@ -193,15 +196,15 @@ static Value buildKeepdimsFromLanePackedBatch(Value batchValue,
 
   auto reshapeCompute =
     createSpatCompute<1>(rewriter, loc, TypeRange {keepdimsType}, {}, ValueRange {batchValue}, [&](Value input) {
-      auto flatType = RankedTensorType::get({batchType.getDimSize(0)}, batchType.getElementType(), batchType.getEncoding());
+      auto flatType =
+        RankedTensorType::get({batchType.getDimSize(0)}, batchType.getElementType(), batchType.getEncoding());
       Value flat = tensor::CollapseShapeOp::create(rewriter, loc, flatType, input, collapseToFlat);
       Value compact = flat;
       if (compactKeptType != flatType)
         compact = tensor::ExpandShapeOp::create(rewriter, loc, compactKeptType, flat, expandFlatToCompact);
       Value keepdims = compact;
       if (keepdimsType != compactKeptType)
-        keepdims =
+        keepdims = tensor::ExpandShapeOp::create(rewriter, loc, keepdimsType, compact, expandCompactToKeepdims);
-          tensor::ExpandShapeOp::create(rewriter, loc, keepdimsType, compact, expandCompactToKeepdims);
       spatial::SpatYieldOp::create(rewriter, loc, keepdims);
     });
   return reshapeCompute.getResult(0);

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

@@ -121,11 +121,9 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
 
       auto transposedType = RankedTensorType::get(
         permuteShape(inputType.getShape(), permutation), inputType.getElementType(), inputType.getEncoding());
-      Value transposedInput =
+      Value transposedInput = transposeMaybeInCompute(input, transposedType, permutation, rewriter, softmaxOp.getLoc());
-        transposeMaybeInCompute(input, transposedType, permutation, rewriter, softmaxOp.getLoc());
       Value transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
-      result = transposeMaybeInCompute(
+      result = transposeMaybeInCompute(transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
-        transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
     }
 
     rewriter.replaceOp(softmaxOp, result);

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

@@ -127,8 +127,8 @@ struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCom
     Block& oldBlock = compute.getBody().front();
 
     rewriter.setInsertionPointAfter(compute);
-    auto newCompute =
+    auto newCompute = spatial::SpatCompute::create(
-      spatial::SpatCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), promoted->newWeights, promoted->newInputs);
+      rewriter, compute.getLoc(), compute.getResultTypes(), promoted->newWeights, promoted->newInputs);
     SmallVector<Type> newBlockArgTypes;
     SmallVector<Location> newBlockArgLocs;
     for (Value weight : promoted->newWeights) {
@@ -155,7 +155,12 @@ struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCom
       mapper.map(*oldWeightArg, *newWeightArg);
     }
     if (failed(mapPromotedInputArguments(
-          compute, *promoted, bodyRewriter, mapper, [&](size_t index) { return newCompute.getInputArgument(index); }, rewriter)))
+          compute,
+          *promoted,
+          bodyRewriter,
+          mapper,
+          [&](size_t index) { return newCompute.getInputArgument(index); },
+          rewriter)))
       return failure();
 
     for (Operation& op : oldBlock.without_terminator())
@@ -199,7 +204,8 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
       return rewriter.notifyMatchFailure(compute, "missing compute_batch lane block argument");
     SmallVector<Type> newBlockArgTypes;
     SmallVector<Location> newBlockArgLocs;
-    newBlockArgTypes.reserve(1 + promoted->newWeights.size() + promoted->newInputTypes.size() + compute.getNumResults());
+    newBlockArgTypes.reserve(1 + promoted->newWeights.size() + promoted->newInputTypes.size()
+                             + compute.getNumResults());
     newBlockArgLocs.reserve(1 + promoted->newWeights.size() + promoted->newInputLocs.size() + compute.getNumResults());
     newBlockArgTypes.push_back(laneArg->getType());
     newBlockArgLocs.push_back(laneArg->getLoc());
@@ -239,7 +245,12 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
       mapper.map(*oldWeightArg, *newWeightArg);
     }
     if (failed(mapPromotedInputArguments(
-          compute, *promoted, bodyRewriter, mapper, [&](size_t index) { return newCompute.getInputArgument(index); }, rewriter)))
+          compute,
+          *promoted,
+          bodyRewriter,
+          mapper,
+          [&](size_t index) { return newCompute.getInputArgument(index); },
+          rewriter)))
       return failure();
     for (auto resultIndex : llvm::seq<size_t>(0, compute.getNumResults())) {
       auto outputArg = compute.getOutputArgument(resultIndex);

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

@@ -111,7 +111,8 @@ struct Reshape : OpConversionPattern<ONNXReshapeOp> {
     }
 
     auto replaceWithReshape = [&](auto buildReshape) -> LogicalResult {
-      Value reshaped = materializeOrComputeUnary(adaptor.getData(), resultType, rewriter, reshapeOp.getLoc(), buildReshape);
+      Value reshaped =
+        materializeOrComputeUnary(adaptor.getData(), resultType, rewriter, reshapeOp.getLoc(), buildReshape);
       rewriter.replaceOp(reshapeOp, reshaped);
       return success();
     };

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

@@ -44,8 +44,7 @@ struct Split : OpConversionPattern<ONNXSplitOp> {
 
     if (isCompileTimeComputable(adaptor.getInput())) {
       for (int64_t sliceSize : sliceSizes) {
-        outputs.push_back(
+        outputs.push_back(extractAxisSlice(rewriter, splitOp.getLoc(), adaptor.getInput(), *axis, offset, sliceSize));
-          extractAxisSlice(rewriter, splitOp.getLoc(), adaptor.getInput(), *axis, offset, sliceSize));
         offset += sliceSize;
       }
       rewriter.replaceOp(splitOp, outputs);

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

@@ -1,5 +1,5 @@
-#include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Transforms/DialectConversion.h"
 
@@ -104,8 +104,7 @@ struct TransposeToLinalgTranspose : OpConversionPattern<ONNXTransposeOp> {
     }
     Value init = createTransposeInit(adaptor.getData(), resultType, *permutation, rewriter, transposeOp.getLoc());
     Value transposed =
-      linalg::TransposeOp::create(rewriter, transposeOp.getLoc(), adaptor.getData(), init, *permutation)
+      linalg::TransposeOp::create(rewriter, transposeOp.getLoc(), adaptor.getData(), init, *permutation).getResult()[0];
-        .getResult()[0];
     rewriter.replaceOp(transposeOp, transposed);
     return success();
   }

src/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.cpp

@@ -7,8 +7,8 @@
 #include "mlir/IR/Matchers.h"
 
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
+#include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"

src/PIM/Conversion/SpatialToPim/Patterns.cpp

@@ -1,5 +1,5 @@
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/Patterns.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/SpatialToPim/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace mlir;

src/PIM/Conversion/SpatialToPim/Patterns/ChannelLowering.cpp

@@ -1,7 +1,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/Patterns.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/SpatialToPim/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -334,8 +334,8 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
       loc,
       tensorType,
       getOrCreateIndexConstant(constantFolder, deviceTensor.getOperation(), 0),
-      getOrCreateIndexConstant(constantFolder,
+      getOrCreateIndexConstant(
-        deviceTensor.getOperation(), static_cast<int64_t>(elementsOffset * elementByteSize) ),
+        constantFolder, deviceTensor.getOperation(), static_cast<int64_t>(elementsOffset * elementByteSize)),
       deviceTensor,
       inputTensor,
       rewriter.getI32IntegerAttr(static_cast<int32_t>(tensorType.getNumElements() * elementByteSize)));

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MaterializeMergeSchedule.cpp

@@ -1482,7 +1482,8 @@ void appendScalarSendLoop(MaterializerState& state,
   state.rewriter.setInsertionPoint(sourceClass.body->getTerminator());
 
   Value lowerBound = getOrCreateIndexConstant(state.constantFolder, sourceClass.op, 0);
-  Value upperBound = getOrCreateIndexConstant(state.constantFolder, sourceClass.op, static_cast<int64_t>(channelIds.size()));
+  Value upperBound =
+    getOrCreateIndexConstant(state.constantFolder, sourceClass.op, static_cast<int64_t>(channelIds.size()));
   Value step = getOrCreateIndexConstant(state.constantFolder, sourceClass.op, 1);
 
   auto loop = scf::ForOp::create(state.rewriter, loc, lowerBound, upperBound, step, ValueRange {});
@@ -1577,7 +1578,8 @@ void appendProjectedScalarSendLoop(MaterializerState& state,
   }
 
   Value lowerBound = getOrCreateIndexConstant(state.constantFolder, sourceClass.op, 0);
-  Value upperBound = getOrCreateIndexConstant(state.constantFolder, sourceClass.op, static_cast<int64_t>(channelIds.size()));
+  Value upperBound =
+    getOrCreateIndexConstant(state.constantFolder, sourceClass.op, static_cast<int64_t>(channelIds.size()));
   Value step = getOrCreateIndexConstant(state.constantFolder, sourceClass.op, 1);
 
   auto loop = scf::ForOp::create(state.rewriter, loc, lowerBound, upperBound, step, ValueRange {});
@@ -2342,7 +2344,8 @@ FailureOr<Value> insertPackedScalarRunIntoWholeBatch(MaterializerState& state,
   }
 
   Value lowerBound = getOrCreateIndexConstant(state.constantFolder, targetClass.op, 0);
-  Value upperBound = getOrCreateIndexConstant(state.constantFolder, targetClass.op, static_cast<int64_t>(run.slots.size()));
+  Value upperBound =
+    getOrCreateIndexConstant(state.constantFolder, targetClass.op, static_cast<int64_t>(run.slots.size()));
   Value step = getOrCreateIndexConstant(state.constantFolder, targetClass.op, 1);
 
   state.rewriter.setInsertionPoint(targetClass.body->getTerminator());

src/PIM/Pass/PimCodegen/MaterializeHostConstantsPass.cpp

@@ -99,8 +99,7 @@ static void materializeHostConstantsInCore(CoreOpTy coreOp,
                         .getOutput();
       }
       else {
-        copiedValue =
+        copiedValue = pim::PimMemCopyHostToDevOp::create(
-          pim::PimMemCopyHostToDevOp::create(
                         rewriter,
                         op->getLoc(),
                         originalType,