diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
index 4df2e0f88..b9b60e774 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
@@ -596,7 +596,6 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
         if (binder.tensorResultType(resultType))
           return failure();
         auto dtype = resultType.getDtype();
-        Value scalarValue;
 
         float floatValue;
         if (binder.op->hasAttr("torch.onnx.value_float") &&
diff --git a/lib/Conversion/TorchToLinalg/TensorConstructors.cpp b/lib/Conversion/TorchToLinalg/TensorConstructors.cpp
index 9429d1e8c..6afae47c1 100644
--- a/lib/Conversion/TorchToLinalg/TensorConstructors.cpp
+++ b/lib/Conversion/TorchToLinalg/TensorConstructors.cpp
@@ -98,11 +98,8 @@ namespace {
 
       Location loc = op->getLoc();
       Value input = adaptor.getSelf();
-      MLIRContext *context = rewriter.getContext();
       auto inputType = llvm::cast<RankedTensorType>(input.getType());
       int64_t inputRank = inputType.getRank();
-      auto outputType = llvm::cast<RankedTensorType>(
-            getTypeConverter()->convertType(op->getResult(0).getType()));
       unsigned numDims = inputType.getRank();
         assert(numDims >= 2 && "Not enough input dimensions");
 
@@ -171,16 +168,6 @@ namespace {
         }
       }
 
-      // Some generic helper functions to aid in constructing basic arithmetic.
-      auto createAdd = [&](Value x, Value y) {
-        return rewriter.create<arith::AddIOp>(loc, x, y);
-      };
-
-      auto createAdds = [&](std::initializer_list<Value> values) {
-        assert(values.size() >= 2);
-        return std::accumulate(values.begin() + 1, values.end(), data(values)[0],
-                             createAdd);
-      };
       auto createSub = [&](Value x, Value y) {
         return rewriter.create<arith::SubIOp>(loc, x, y);
       };
@@ -247,8 +234,6 @@ namespace {
         tensorsRes.push_back(leftPadTile);
       }
       if (hasTopPadding) {
-        Value topLeftValue = rewriter.create<tensor::ExtractOp>(
-            loc, input, ValueRange{zero, zero, zero, zero});
         Value topHcenterSlice = rewriter.create<tensor::ExtractSliceOp>(
             loc, input, extractOffsetsLT, extractShapeTB, allOneStrides);
         for (auto i = 0; i < padInts[2]; ++i) {