automatic code reformat

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 =
-    createSpatCompute<1>(rewriter, loc, mlir::TypeRange {resultType}, {}, mlir::ValueRange {input}, [&](mlir::Value computeInput) {
+  auto computeOp = createSpatCompute<1>(
+    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
-multiplyIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Operation* anchorOp, mlir::Value value, int64_t multiplier);
+mlir::Value multiplyIndexByConstant(mlir::PatternRewriter& rewriter,
+                                    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,
-                              RankedTensorType resultType,
-                              ArrayRef<int64_t> permutation,
-                              PatternRewriter& rewriter,
-                              Location loc) {
+Value transposeMaybeInCompute(
+  Value value, RankedTensorType resultType, 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::Location loc,
-                             mlir::Value source,
-                             int64_t axis,
-                             int64_t offset,
-                             int64_t size);
+mlir::Value extractAxisSlice(
+  mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value source, int64_t axis, int64_t offset, int64_t size);
 
 mlir::Value insertStaticSlice(mlir::PatternRewriter& rewriter,
                               mlir::Location loc,