diff --git a/lib/Conversion/TorchToLinalg/Linear.cpp b/lib/Conversion/TorchToLinalg/Linear.cpp
index 76bf0c13d..52765411b 100644
--- a/lib/Conversion/TorchToLinalg/Linear.cpp
+++ b/lib/Conversion/TorchToLinalg/Linear.cpp
@@ -1080,21 +1080,16 @@ public:
         return rewriter.notifyMatchFailure(op, "expect bias to be rank 1");
 
       auto resultRank = cast<RankedTensorType>(initTensor.getType()).getRank();
-      SmallVector<AffineMap> indexingMaps = {
-          // bias is used to initialize the channels - dimension 1 of output
-          AffineMap::get(/*dimCount=*/resultRank, /*symbolCount=*/0,
-                         rewriter.getAffineDimExpr(1), context),
-          rewriter.getMultiDimIdentityMap(resultRank)};
-      SmallVector<utils::IteratorType> iteratorTypes(
-          resultRank, utils::IteratorType::parallel);
+      SmallVector<int64_t, 4> addedDimensions;
+      // bias is used to initialize the channels - dimension 1 of
+      // output
+      for (int i = 0; i < resultRank; ++i)
+        if (i != 1)
+          addedDimensions.push_back(i);
       outputTensor = rewriter
-                         .create<linalg::GenericOp>(
-                             loc, initTensor.getType(), bias, initTensor,
-                             indexingMaps, iteratorTypes,
-                             [](OpBuilder &b, Location loc, ValueRange args) {
-                               b.create<linalg::YieldOp>(loc, args[0]);
-                             })
-                         .getResult(0);
+                         .create<linalg::BroadcastOp>(loc, bias, initTensor,
+                                                      addedDimensions)
+                         ->getResult(0);
     }
 
     auto stridesAttr = rewriter.getI64VectorAttr(strideInts);