diff --git a/lib/Conversion/TorchToLinalg/Random.cpp b/lib/Conversion/TorchToLinalg/Random.cpp
index 3b18844df..6519a2723 100644
--- a/lib/Conversion/TorchToLinalg/Random.cpp
+++ b/lib/Conversion/TorchToLinalg/Random.cpp
@@ -129,6 +129,7 @@ public:
     Value generator = adaptor.getGenerator();
     RankedTensorType resultType = self.getType().cast<RankedTensorType>();
     Type elemTy = resultType.getElementType();
+    Type f64Ty = rewriter.getF64Type();
 
     if (!isa<mlir::FloatType>(elemTy))
       return rewriter.notifyMatchFailure(op, "This op only support float type");
@@ -139,8 +140,8 @@ public:
               "generator is supported");
     // Get key, min and max used by `linalg.generic` compute payload.
     Value key = rewriter.create<TorchConversion::GetNextSeedOp>(loc);
-    Value min = convertScalarToDtype(rewriter, loc, from, elemTy);
-    Value max = convertScalarToDtype(rewriter, loc, to, elemTy);
+    Value min = convertScalarToDtype(rewriter, loc, from, f64Ty);
+    Value max = convertScalarToDtype(rewriter, loc, to, f64Ty);
 
     // Construct the `linalg.generic` op.
     auto resultRank = resultType.getRank();
@@ -179,11 +180,14 @@ public:
 
                   // res = cast(F64, tempN) * scale + min
                   Value updateFloat =
-                      b.create<arith::UIToFPOp>(loc, elemTy, randomVal);
+                      b.create<arith::UIToFPOp>(loc, f64Ty, randomVal);
                   Value updateScaled =
                       b.create<arith::MulFOp>(loc, updateFloat, scale);
                   Value res = b.create<arith::AddFOp>(loc, updateScaled, min);
-                  b.create<linalg::YieldOp>(loc, res);
+                  Value truncRes = res;
+                  if (elemTy.isa<Float16Type, Float32Type>())
+                    truncRes = b.create<arith::TruncFOp>(loc, elemTy, res);
+                  b.create<linalg::YieldOp>(loc, truncRes);
                 })
             .getResult(0);