diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
@@ -143,6 +143,98 @@
   return success();
 }
 
+static LogicalResult reshapeLowerToHigher(PatternRewriter &rewriter,
+                                          Location loc,
+                                          RankedTensorType outputType,
+                                          Value input1, Value input2,
+                                          Value input3, Value &outInput1,
+                                          Value &outInput2, Value &outInput3) {
+  SmallVector<Value, 3> inputs;
+  inputs.push_back(input1);
+  inputs.push_back(input2);
+  inputs.push_back(input3);
+
+  SmallVector<std::reference_wrapper<Value>, 3> outInputs;
+  outInputs.push_back(outInput1);
+  outInputs.push_back(outInput2);
+  outInputs.push_back(outInput3);
+
+  // Create a vector of <tensor-rank, tensor-id> pair for sorting.
+  SmallVector<std::pair<int, int>, 3> ranks;
+  for (int i = 0; i < 3; ++i) {
+    auto inputTy = inputs[i].getType().dyn_cast<RankedTensorType>();
+    if (!inputTy)
+      return rewriter.notifyMatchFailure(loc, "input not a ranked tensor");
+
+    ranks.push_back(std::make_pair(inputTy.getRank(), i));
+  }
+
+  // After sorting, the last element of vector is the highest-rank tensor.
+  sort(ranks.begin(), ranks.end());
+
+  // Initialization.
+  outInput1 = input1;
+  outInput2 = input2;
+  outInput3 = input3;
+  int higherTensorRank = ranks[2].first;
+  int higherTensorIndex = ranks[2].second;
+  Value higherTensorValue = inputs[higherTensorIndex];
+
+  bool reshaped = false;
+  for (int i = 0; i < 2; ++i) {
+    int lowerTensorRank = ranks[i].first;
+
+    if (lowerTensorRank != higherTensorRank) {
+      int lowerTensorIndex = ranks[i].second;
+      Value lowerTensorValue = inputs[lowerTensorIndex];
+
+      ArrayRef<int64_t> higherRankShape =
+          higherTensorValue.getType().cast<RankedTensorType>().getShape();
+      (void)higherRankShape;
+      ArrayRef<int64_t> lowerRankShape =
+          lowerTensorValue.getType().cast<RankedTensorType>().getShape();
+
+      SmallVector<int64_t, 4> reshapeOutputShape;
+      if (computeReshapeOutput(higherRankShape, lowerRankShape,
+                               reshapeOutputShape)
+              .failed())
+        return rewriter.notifyMatchFailure(
+            loc,
+            "fail to compute a reshaped output for a lower rank input tensor");
+
+      auto reshapeInputType =
+          lowerTensorValue.getType().cast<RankedTensorType>();
+      auto reshapeOutputType =
+          RankedTensorType::get(ArrayRef<int64_t>(reshapeOutputShape),
+                                reshapeInputType.getElementType());
+
+      // Verify the rank agrees with the output type if the output type is
+      // ranked.
+      if (outputType) {
+        if (outputType.getShape().size() != reshapeOutputShape.size() ||
+            outputType.getShape().size() != higherRankShape.size())
+          return rewriter.notifyMatchFailure(
+              loc, "the reshaped rank doesn't agrees with the output type");
+      }
+
+      auto reshapeLower = rewriter.create<tosa::ReshapeOp>(
+          loc, reshapeOutputType, lowerTensorValue,
+          rewriter.getI64ArrayAttr(reshapeOutputShape));
+
+      outInputs[lowerTensorIndex].get() = reshapeLower.getResult();
+      reshaped = true;
+    }
+  }
+
+  if (!reshaped)
+    return rewriter.notifyMatchFailure(
+        loc, "cannot rewrite as the ranks of inputs are already correct");
+
+  outInputs[higherTensorIndex].get() = higherTensorValue;
+
+  return success();
+}
+
 namespace {
 template <typename OpTy>
 struct ConvertTosaOp : public OpRewritePattern<OpTy> {
@@ -233,6 +325,36 @@
     return success();
   }
 };
+
+template <>
+struct ConvertTosaOp<tosa::SelectOp> : public OpRewritePattern<tosa::SelectOp> {
+  using OpRewritePattern<tosa::SelectOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::SelectOp tosaOp,
+                                PatternRewriter &rewriter) const override {
+
+    Value input1 = tosaOp.getOnTrue();
+    Value input2 = tosaOp.getOnFalse();
+    Value pred = tosaOp.getPred();
+    Value output = tosaOp.getResult();
+
+    auto outputType = output.getType().dyn_cast<RankedTensorType>();
+    if (!outputType)
+      return rewriter.notifyMatchFailure(tosaOp, "output not a ranked tensor");
+
+    Value outInput1, outInput2, outPred;
+    if (reshapeLowerToHigher(rewriter, tosaOp.getLoc(), outputType, pred,
+                             input1, input2, outPred, outInput1, outInput2)
+            .failed())
+      return rewriter.notifyMatchFailure(
+          tosaOp, "fail to broadcast a tensor, check if the ranks are valid");
+
+    rewriter.replaceOpWithNewOp<tosa::SelectOp>(tosaOp, outputType, outPred,
+                                                outInput1, outInput2);
+
+    return success();
+  }
+};
 } // namespace
 
 namespace {
@@ -265,6 +387,7 @@
     patterns.add<ConvertTosaOp<tosa::LogicalOrOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::LogicalXorOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::PowOp>>(ctx);
+    patterns.add<ConvertTosaOp<tosa::SelectOp>>(ctx);
     (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
   }
 };
diff --git a/mlir/test/Dialect/Tosa/broadcast.mlir b/mlir/test/Dialect/Tosa/broadcast.mlir
--- a/mlir/test/Dialect/Tosa/broadcast.mlir
+++ b/mlir/test/Dialect/Tosa/broadcast.mlir
@@ -195,3 +195,91 @@
   %0 = "tosa.add"(%arg0, %arg1) : (tensor<i32>, tensor<17x16x15x14xi32>) -> tensor<17x16x15x14xi32>
   return %0 : tensor<17x16x15x14xi32>
 }
+
+// -----
+// CHECK-LABEL: broadcast_select_both_input
+func.func @test_broadcast_select_both_input(%arg0: tensor<1x16x16xi1>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<1x16x16xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 1]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%arg0, %[[VAL_0]], %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<1x16x16xi1>, tensor<f32>, tensor<f32>) -> tensor<1x16x16xf32>
+  return %0 : tensor<1x16x16xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_one_input
+func.func @test_broadcast_select_one_input(%arg0: tensor<17x16x15x14xi1>, %arg1: tensor<17x16x15x14xf32>, %arg2: tensor<f32>) -> tensor<17x16x15x14xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 1, 1]}
+  // CHECK: %[[VAL_1:.*]] = "tosa.select"(%arg0, %arg1, %[[VAL_0]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<17x16x15x14xi1>, tensor<17x16x15x14xf32>, tensor<f32>) -> tensor<17x16x15x14xf32>
+  return %0 : tensor<17x16x15x14xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_predicate
+func.func @test_broadcast_select_predicate(%arg0: tensor<i1>, %arg1: tensor<1x32x32x8xf32>, %arg2: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 1, 1]}
+  // CHECK: %[[VAL_1:.*]] = "tosa.select"(%[[VAL_0]], %arg1, %arg2)
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<1x32x32x8xf32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_abc
+func.func @test_broadcast_select_abc(%arg0: tensor<i1>, %arg1: tensor<32x8xf32>, %arg2: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 1, 1]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 32, 8]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %[[VAL_1]], %arg2)
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<32x8xf32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_acb
+func.func @test_broadcast_select_acb(%arg0: tensor<i1>, %arg1: tensor<1x32x32x8xf32>, %arg2: tensor<32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 1, 1]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 32, 8]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %arg1, %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<1x32x32x8xf32>, tensor<32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_bac
+func.func @test_broadcast_select_bac(%arg0: tensor<32x8xi1>, %arg1: tensor<f32>, %arg2: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 32, 8]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %[[VAL_1]], %arg2)
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<32x8xi1>, tensor<f32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_bca
+func.func @test_broadcast_select_bca(%arg0: tensor<32x8xi1>, %arg1: tensor<1x32x32x8xf32>, %arg2: tensor<i1>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 32, 8]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 1, 1]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%[[VAL_0]], %arg1, %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<32x8xi1>, tensor<1x32x32x8xf32>, tensor<i1>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_cab
+func.func @test_broadcast_select_cab(%arg0: tensor<1x32x32x8xi1>, %arg1: tensor<f32>, %arg2: tensor<32x8xf32>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 32, 8]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%arg0, %[[VAL_0]], %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<1x32x32x8xi1>, tensor<f32>, tensor<32x8xf32>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}
+
+// -----
+// CHECK-LABEL: broadcast_select_cba
+func.func @test_broadcast_select_cba(%arg0: tensor<1x32x32x8xi1>, %arg1: tensor<32x8xf32>, %arg2: tensor<i1>) -> tensor<1x32x32x8xf32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 32, 8]}
+  // CHECK-DAG: %[[VAL_1:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 1, 1]}
+  // CHECK: %[[VAL_2:.*]] = "tosa.select"(%arg0, %[[VAL_0]], %[[VAL_1]])
+  %0 = "tosa.select"(%arg0, %arg1, %arg2) : (tensor<1x32x32x8xi1>, tensor<32x8xf32>, tensor<i1>) -> tensor<1x32x32x8xf32>
+  return %0 : tensor<1x32x32x8xf32>
+}