Bump llvm-project to 6b65d79fbb4682468333cea42b62f15c2dffd8f3 (#2723)

Co-authored-by: hanhanW <hanhan0912@gmail.com>

externals/llvm-project

@@ -1 +1 @@
-Subproject commit 99045b60b57571079f9cb4aea57870692523fbe8
+Subproject commit 6b65d79fbb4682468333cea42b62f15c2dffd8f3

lib/Dialect/TMTensor/IR/TMTensorOps.cpp

@@ -166,7 +166,6 @@ static void matmul(OpBuilder &b, Location loc, Value lhs, ValueRange lhsSizes,
                  })
                 ->getResult(0);
         b.create<memref::StoreOp>(loc, sum, output, localIVs);
-        b.create<scf::YieldOp>(loc);
       });
 }
 
@@ -229,13 +228,15 @@ LogicalResult AttentionOp::generateScalarImplementation(OpBuilder &b,
            SmallVector<Value>(weightRank, one), init,
            [&](OpBuilder &b, Location loc, ValueRange localIVs,
                ValueRange accs) {
-             b.create<scf::ReduceOp>(
+             auto reduceOp = b.create<scf::ReduceOp>(loc, init);
-                 loc, init,
+             // Build reduce body.
-                 [&](OpBuilder &b, Location loc, Value elem, Value acc) {
+             Block &reductionBody = reduceOp.getReductions()[0].front();
-                   Value x = b.create<memref::LoadOp>(loc, weight, localIVs);
+             auto bodyBuilder = OpBuilder::atBlockEnd(&reductionBody);
-                   Value max = b.create<arith::MaximumFOp>(loc, x, acc);
+             Value acc = reductionBody.getArgument(0);
-                   b.create<scf::ReduceReturnOp>(loc, max);
+             Value x =
-                 });
+                 bodyBuilder.create<memref::LoadOp>(loc, weight, localIVs);
+             Value max = bodyBuilder.create<arith::MaximumFOp>(loc, x, acc);
+             bodyBuilder.create<scf::ReduceReturnOp>(loc, max);
            })
           .getResult(0);
   // weight = (weight - max(weight)) / math.sqrt(querySizes[-1])
@@ -247,7 +248,6 @@ LogicalResult AttentionOp::generateScalarImplementation(OpBuilder &b,
         x = b.create<arith::SubFOp>(loc, x, globalMax);
         x = b.create<arith::DivFOp>(loc, x, scaleFactor);
         b.create<memref::StoreOp>(loc, x, weight, localIVs);
-        b.create<scf::YieldOp>(loc);
       });
   // calculate exp(weight)
   SmallVector<Value> min(weightRank, zero),
@@ -258,7 +258,6 @@ LogicalResult AttentionOp::generateScalarImplementation(OpBuilder &b,
         Value x = b.create<memref::LoadOp>(loc, weight, localIVs);
         x = b.create<math::ExpOp>(loc, x);
         b.create<memref::StoreOp>(loc, x, weight, localIVs);
-        b.create<scf::YieldOp>(loc);
       });
   Value expWeightSum = b.create<memref::AllocOp>(
       loc,
@@ -290,7 +289,6 @@ LogicalResult AttentionOp::generateScalarImplementation(OpBuilder &b,
               Value y = b.create<memref::LoadOp>(loc, weight, coords);
               Value sum = b.create<arith::AddFOp>(loc, x, y);
               b.create<memref::StoreOp>(loc, sum, expWeightSum, outsideDims);
-              b.create<scf::YieldOp>(loc);
             });
       });
   // calculate exp(weight) / sum(exp(weight))
@@ -305,7 +303,6 @@ LogicalResult AttentionOp::generateScalarImplementation(OpBuilder &b,
         Value sum = b.create<memref::LoadOp>(loc, expWeightSum, sumIVs);
         x = b.create<arith::DivFOp>(loc, x, sum);
         b.create<memref::StoreOp>(loc, x, weight, localIVs);
-        b.create<scf::YieldOp>(loc);
       });
 
   // output = weight @ value

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -715,6 +715,8 @@ OpFoldResult AtenNeBoolOp::fold(FoldAdaptor adaptor) {
 //===----------------------------------------------------------------------===//
 
 OpFoldResult AtenSqueezeOp::fold(FoldAdaptor adaptor) {
+  if (getOperand().getType() != getResult().getType())
+    return nullptr;
   if (auto tensorType = getOperand().getType().dyn_cast<BaseTensorType>()) {
     if (tensorType.hasSizes() && tensorType.getSizes().size() == 0)
       return getOperand();
@@ -727,6 +729,8 @@ OpFoldResult AtenSqueezeOp::fold(FoldAdaptor adaptor) {
 //===----------------------------------------------------------------------===//
 
 OpFoldResult AtenSqueezeDimOp::fold(FoldAdaptor adaptor) {
+  if (getOperand(0).getType() != getResult().getType())
+    return nullptr;
   if (auto tensorType = getOperand(0).getType().dyn_cast<BaseTensorType>()) {
     if (tensorType.hasSizes() && tensorType.getSizes().size() == 0)
       return getOperand(0);
@@ -739,6 +743,8 @@ OpFoldResult AtenSqueezeDimOp::fold(FoldAdaptor adaptor) {
 //===----------------------------------------------------------------------===//
 
 OpFoldResult AtenRoundOp::fold(FoldAdaptor adaptor) {
+  if (getSelf().getType() != getResult().getType())
+    return nullptr;
   if (auto selfType = getSelf().getType().dyn_cast<BaseTensorType>()) {
     if (selfType.hasDtype() && selfType.getDtype().isa<mlir::IntegerType>())
       return getSelf();
@@ -911,6 +917,8 @@ OpFoldResult AtenViewOp::fold(FoldAdaptor adaptor) {
   auto resType = getType().dyn_cast<BaseTensorType>();
   if (!resType || !resType.hasSizes() || resType.getSizes().size() != 1)
     return nullptr;
+  if (inputType != resType)
+    return nullptr;
   // Fold when both the input tensor and result are unity rank tensors.
   return getOperand(0);
 }
@@ -2441,6 +2449,8 @@ OpFoldResult AtenCatOp::fold(FoldAdaptor adaptor) {
   auto list = getOperand(0).getDefiningOp<PrimListConstructOp>();
   if (!list || !list->hasOneUse() || list.getElements().size() != 1)
     return nullptr;
+  if (list.getElements()[0].getType() != getResult().getType())
+    return nullptr;
   return list.getElements()[0];
 }
 
@@ -2451,6 +2461,8 @@ OpFoldResult AtenCatOp::fold(FoldAdaptor adaptor) {
 OpFoldResult AtenBroadcastToOp::fold(FoldAdaptor adaptor) {
   auto inType = getOperand(0).getType().dyn_cast<BaseTensorType>();
   auto outType = getResult().getType().dyn_cast<BaseTensorType>();
+  if (inType != outType)
+    return nullptr;
   if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes())
     return nullptr;
   if (inType.getSizes().size() != outType.getSizes().size() ||
@@ -2480,6 +2492,8 @@ OpFoldResult AtenSliceTensorOp::fold(FoldAdaptor adaptor) {
 
   auto inType = getOperand(0).getType().dyn_cast<BaseTensorType>();
   auto outType = getResult().getType().dyn_cast<BaseTensorType>();
+  if (inType != outType)
+    return nullptr;
   if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes())
     return nullptr;
   if (inType.getSizes().size() != outType.getSizes().size() ||

lib/Dialect/Torch/Transforms/InlineGlobalSlots.cpp

@@ -95,7 +95,7 @@ static bool isUseTreatedWithValueSemantics(OpOperand &use) {
 class InlineGlobalSlotsAnalysisState : public AnalysisState {
 public:
   InlineGlobalSlotsAnalysisState(ProgramPoint point) : AnalysisState(point) {
-    setSafe();
+    (void)setSafe();
   }
 
   void print(raw_ostream &os) const override {