automatic code reformat

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(
-  Value lane, int64_t numOutCols, ConversionPatternRewriter& rewriter, Location loc) {
+static Value
+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
-extractDynamicGemmBColumn(Value matrix, Value column, RankedTensorType vectorType, ConversionPatternRewriter& rewriter, Location loc) {
+static Value extractDynamicGemmBColumn(
+  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 {
-      biasType.getDimSize(0) == 1 ? OpFoldResult(rewriter.getIndexAttr(0)) : OpFoldResult(column)};
+    SmallVector<OpFoldResult> offsets {biasType.getDimSize(0) == 1 ? OpFoldResult(rewriter.getIndexAttr(0))
+                                                                   : 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)
-                     .getResult();
-    SmallVector<ReassociationIndices> reassociation {ReassociationIndices {0, 1}};
+    Value vector =
+      tensor::ExtractSliceOp::create(rewriter, loc, vectorType, bias, offsets, sizes, unitStrides).getResult();
+    SmallVector<ReassociationIndices> reassociation {
+      ReassociationIndices {0, 1}
+    };
     return tensor::ExpandShapeOp::create(rewriter, loc, scalarType, vector, reassociation).getResult();
   }
 
@@ -407,16 +419,21 @@ 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
-                        ? extractTransposedBRow(args.inputs[1], column, vectorType, rewriter, loc)
-                        : extractDynamicGemmBColumn(args.inputs[1], column, vectorType, rewriter, loc);
+      Value bVector = bAlreadyTransposed ? 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();
 
       SmallVector<OpFoldResult> outputOffsets {args.lane, rewriter.getIndexAttr(0)};
@@ -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 =
-        onnx_mlir::multiplyIndexByConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), hSlice,
-                                           crossbarSize.getValue());
+      Value hOffset = onnx_mlir::multiplyIndexByConstant(
+        rewriter, rewriter.getInsertionBlock()->getParentOp(), hSlice, 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(
-      a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
+    auto batchOp =
+      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());