Fix logical or/and usage for MSVC compilation.

The `or`/`and` alternate spellings are not supported on all compilers without additinoal flags (https://learn.microsoft.com/en-us/cpp/cpp/logical-or-operator-pipe-pipe?view=msvc-170#operator-keyword-for-).
llvm · Jan 27, 2025 · d94e357 · d94e357
1 parent 4f0b79c
commit d94e357
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Showing 2 changed files with 6 additions and 6 deletions.
diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
@@ -2346,7 +2346,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
         ArrayRef<int64_t> inputShape = inputTensorType.getSizes();
         unsigned inputRank = inputShape.size();
         // only handle 2D, 3D and 5D pooling cases
-        if (inputRank > 5 or inputRank < 3) {
+        if (inputRank > 5 || inputRank < 3) {
           return failure();
         }
         if (!resultType || !resultType.hasSizes()) {
@@ -2454,7 +2454,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
                                              "Unimplemented: unranked tensor");
         unsigned rank = *maybeRank;
         // only 1D, 2D and 3D LpPool is supported.
-        if (rank > 5 or rank < 3) {
+        if (rank > 5 || rank < 3) {
           return failure();
         }
 

diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -9780,16 +9780,16 @@ class DecomposeAtenNllLossForwardOp
     auto targetSizes = targetType.getSizes();
     int64_t selfRank = selfSizes.size();
     int64_t targetRank = targetSizes.size();
-    if (selfRank <= 0 or selfRank > 2) {
+    if (selfRank <= 0 || selfRank > 2) {
       return rewriter.notifyMatchFailure(op, "input tensor should be 1D or 2D");
     }
     if (targetRank > 1) {
       return rewriter.notifyMatchFailure(op,
                                          "target tensor shoule be 0D or 1D!");
     }
 
-    if (selfRank != 1 or targetRank != 0) {
-      if (!(selfSizes[0] == kUnknownSize and targetSizes[0] == kUnknownSize) and
+    if (selfRank != 1 || targetRank != 0) {
+      if (!(selfSizes[0] == kUnknownSize && targetSizes[0] == kUnknownSize) &&
           selfSizes[0] != targetSizes[0]) {
         return rewriter.notifyMatchFailure(
             op,
@@ -9907,7 +9907,7 @@ class DecomposeAtenNllLossForwardOp
         zeroTensor);
 
     Value totalWeight;
-    if (reduction == 0 and selfRank > 1) {
+    if (reduction == 0 && selfRank > 1) {
       auto zeroFloat =
           rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(0.0));
       Value twSize = rewriter.create<PrimListConstructOp>(