Bump LLVM to llvm/llvm-project@bb180856ec (#2895)

Includes some minor first for `AffineMap::inferFromExprList`

externals/llvm-project

@@ -1 +1 @@
-Subproject commit 70eb0e37a86747f9266e4c8380baa89746f5e23b
+Subproject commit bb180856ec28efe305dc77ca4bb3db12d8932edf

lib/Conversion/TorchToLinalg/IndirectDataMovement.cpp

@@ -498,7 +498,8 @@ public:
       resultExpr.push_back(rewriter.getAffineDimExpr(i));
     }
 
-    auto indexingMaps = AffineMap::inferFromExprList({indicesExpr, resultExpr});
+    auto indexingMaps = AffineMap::inferFromExprList({indicesExpr, resultExpr},
+                                                     rewriter.getContext());
 
     Value finalRes =
         rewriter

lib/Conversion/TorchToLinalg/Linear.cpp

@@ -512,8 +512,8 @@ public:
       resultShape.insert(resultShape.end(), {lhsDim0, rhsDim1});
       Value zeroTensor =
           createZeroInitTensor(rewriter, loc, resultShape, elementType);
-      auto indexingMaps =
-          AffineMap::inferFromExprList({lhsExpr, rhsExpr, outExpr});
+      auto indexingMaps = AffineMap::inferFromExprList(
+          {lhsExpr, rhsExpr, outExpr}, rewriter.getContext());
       iteratorTypes.insert(iteratorTypes.end(),
                            {utils::IteratorType::parallel,
                             utils::IteratorType::reduction,

lib/Conversion/TorchToLinalg/Pooling.cpp

@@ -442,8 +442,8 @@ public:
 
     // Here we have six dimensions, each corresponding to N, C, Hout, Wout, kH,
     // and kW, respectively, as described in the algorithm above.
-    SmallVector<AffineMap> indexingMaps =
-        AffineMap::inferFromExprList({inputExprs, kernelExprs, outputExprs});
+    SmallVector<AffineMap> indexingMaps = AffineMap::inferFromExprList(
+        {inputExprs, kernelExprs, outputExprs}, rewriter.getContext());
     SmallVector<utils::IteratorType> iteratorTypes(
         4, utils::IteratorType::parallel);
     iteratorTypes.push_back(utils::IteratorType::reduction);
@@ -724,7 +724,7 @@ public:
     kSizeTensorExprs.push_back(rewriter.getAffineDimExpr(2));
     kIterExprs.push_back(rewriter.getAffineDimExpr(3));
     SmallVector<AffineMap> indexingMaps = AffineMap::inferFromExprList(
-        {kIterExprs, outputExprs, kSizeTensorExprs});
+        {kIterExprs, outputExprs, kSizeTensorExprs}, rewriter.getContext());
     SmallVector<utils::IteratorType> iteratorTypes(
         3, utils::IteratorType::parallel);
     iteratorTypes.push_back(utils::IteratorType::reduction);
@@ -774,8 +774,8 @@ public:
 
     // make a linalg generic to divide each element by the corresponding
     // Kernel Width. This step is only necessary for avg pooling.
-    SmallVector<AffineMap> indexingMaps1 =
-        AffineMap::inferFromExprList({kSizeTensorExprs, outputExprs});
+    SmallVector<AffineMap> indexingMaps1 = AffineMap::inferFromExprList(
+        {kSizeTensorExprs, outputExprs}, rewriter.getContext());
     SmallVector<utils::IteratorType> iteratorTypes1(
         3, utils::IteratorType::parallel);
     auto output = rewriter.create<linalg::GenericOp>(
@@ -916,8 +916,8 @@ public:
     for (unsigned i = rank; i < 2 * rank - 2; i++) {
       kIterExprs.push_back(rewriter.getAffineDimExpr(i));
     }
-    SmallVector<AffineMap> indexingMaps =
-        AffineMap::inferFromExprList({kIterExprs, outputExprs, auxTensorExprs});
+    SmallVector<AffineMap> indexingMaps = AffineMap::inferFromExprList(
+        {kIterExprs, outputExprs, auxTensorExprs}, rewriter.getContext());
     SmallVector<utils::IteratorType> iteratorTypes(
         rank, utils::IteratorType::parallel);
     for (unsigned i = 0; i < rank - 2; i++) {

lib/Conversion/TorchToLinalg/Reduction.cpp

@@ -167,7 +167,8 @@ public:
         resultExprs.push_back(rewriter.getAffineDimExpr(size.index()));
       }
     }
-    auto maps = AffineMap::inferFromExprList({exprs, resultExprs, resultExprs});
+    auto maps = AffineMap::inferFromExprList({exprs, resultExprs, resultExprs},
+                                             rewriter.getContext());
     auto linalgOp = rewriter.create<linalg::GenericOp>(
         loc,
         ArrayRef<Type>({filledTensorVal.getType(), filledTensorIdx.getType()}),

lib/Conversion/TorchToLinalg/Utils.cpp

@@ -197,7 +197,8 @@ Value torch_to_linalg::createReductionLinalgGeneric(
     }
   }
 
-  auto indexingMaps = AffineMap::inferFromExprList({exprs, resultExprs});
+  auto indexingMaps =
+      AffineMap::inferFromExprList({exprs, resultExprs}, b.getContext());
   Value accumulator =
       createInitTensor(b, loc, resultShape, initElem.getType(), initElem);
 

lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp

@@ -1064,7 +1064,8 @@ public:
         rewriter.getAffineDimExpr(tensorOperandRank));
 
     SmallVector<AffineMap> indexingMaps = AffineMap::inferFromExprList(
-        {originalIndicesDimExprs, updatedIndicesDimExprs});
+        {originalIndicesDimExprs, updatedIndicesDimExprs},
+        rewriter.getContext());
     SmallVector<utils::IteratorType> iteratorTypes(
         tensorOperandRank + 1, utils::IteratorType::parallel);