diff --git a/tensorflow/compiler/mlir/xla/tests/legalize-tf.mlir b/tensorflow/compiler/mlir/xla/tests/legalize-tf.mlir
index f30bd961fca..d8a1a156b0c 100644
--- a/tensorflow/compiler/mlir/xla/tests/legalize-tf.mlir
+++ b/tensorflow/compiler/mlir/xla/tests/legalize-tf.mlir
@@ -3755,3 +3755,40 @@ func @batchmatmulv2_dynamic(%arg0: tensor<?x4x2xf32>, %arg1: tensor<?x2x4xf32>)
   return %0 : tensor<?x4x4xf32>
 }
 
+// CHECK-LABEL: func @batchmatmulv2_adj_real
+func @batchmatmulv2_adj_real(%arg0: tensor<5x2xf32>, %arg1: tensor<2x4xf32>) -> tensor<5x4xf32> {
+  // CHECK:         [[BLHS:%.+]] = "xla_hlo.broadcast_in_dim"(%arg0) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<5x2xf32>) -> tensor<5x2xf32>
+  // CHECK:         [[BRHS:%.+]] = "xla_hlo.broadcast_in_dim"(%arg1) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<2x4xf32>) -> tensor<2x4xf32>
+  // CHECK:         [[BDST:%.+]] = "xla_hlo.dot_general"([[BLHS]], [[BRHS]]) {dot_dimension_numbers = {
+  // CHECK-SAME:      lhs_batching_dimensions = dense<[]> : tensor<0xi64>,
+  // CHECK-SAME:      lhs_contracting_dimensions = dense<0> : tensor<1xi64>,
+  // CHECK-SAME:      rhs_batching_dimensions = dense<[]> : tensor<0xi64>,
+  // CHECK-SAME:      rhs_contracting_dimensions = dense<1> : tensor<1xi64>
+  // CHECK-SAME:    }} : (tensor<5x2xf32>, tensor<2x4xf32>) -> tensor<5x4xf32>
+  // CHECK:         return [[BDST]] : tensor<5x4xf32>
+  %0 = "tf.BatchMatMulV2"(%arg0, %arg1) {adj_x = true, adj_y = true, device = ""} : (tensor<5x2xf32>, tensor<2x4xf32>) -> tensor<5x4xf32>
+  return %0 : tensor<5x4xf32>
+}
+
+// CHECK-LABEL: func @batchmatmulv2_adj_complex
+func @batchmatmulv2_adj_complex(%arg0: tensor<5x2xcomplex<f32>>, %arg1: tensor<2x4xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
+  // CHECK:         [[LHSRE:%.+]] = "xla_hlo.real"(%arg0) : (tensor<5x2xcomplex<f32>>) -> tensor<5x2xf32>
+  // CHECK:         [[LHSIM:%.+]] = "xla_hlo.imag"(%arg0) : (tensor<5x2xcomplex<f32>>) -> tensor<5x2xf32>
+  // CHECK:         [[LHSIMNEG:%.+]] = "xla_hlo.neg"([[LHSIM]]) : (tensor<5x2xf32>) -> tensor<5x2xf32>
+  // CHECK:         [[LHSCONJ:%.+]] = "xla_hlo.complex"([[LHSRE]], [[LHSIMNEG]]) : (tensor<5x2xf32>, tensor<5x2xf32>) -> tensor<5x2xcomplex<f32>>
+  // CHECK:         [[RHSRE:%.+]] = "xla_hlo.real"(%arg1) : (tensor<2x4xcomplex<f32>>) -> tensor<2x4xf32>
+  // CHECK:         [[RHSIM:%.+]] = "xla_hlo.imag"(%arg1) : (tensor<2x4xcomplex<f32>>) -> tensor<2x4xf32>
+  // CHECK:         [[RHSIMNEG:%.+]] = "xla_hlo.neg"([[RHSIM]]) : (tensor<2x4xf32>) -> tensor<2x4xf32>
+  // CHECK:         [[RHSCONJ:%.+]] = "xla_hlo.complex"([[RHSRE]], [[RHSIMNEG]]) : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xcomplex<f32>>
+  // CHECK:         [[BLHS:%.+]] = "xla_hlo.broadcast_in_dim"([[LHSCONJ]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<5x2xcomplex<f32>>) -> tensor<5x2xcomplex<f32>>
+  // CHECK:         [[BRHS:%.+]] = "xla_hlo.broadcast_in_dim"([[RHSCONJ]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<2x4xcomplex<f32>>) -> tensor<2x4xcomplex<f32>>
+  // CHECK:         [[BDST:%.+]] = "xla_hlo.dot_general"([[BLHS]], [[BRHS]]) {dot_dimension_numbers = {
+  // CHECK-SAME:      lhs_batching_dimensions = dense<[]> : tensor<0xi64>,
+  // CHECK-SAME:      lhs_contracting_dimensions = dense<0> : tensor<1xi64>,
+  // CHECK-SAME:      rhs_batching_dimensions = dense<[]> : tensor<0xi64>,
+  // CHECK-SAME:      rhs_contracting_dimensions = dense<1> : tensor<1xi64>
+  // CHECK-SAME:    }} : (tensor<5x2xcomplex<f32>>, tensor<2x4xcomplex<f32>>) -> tensor<5x4xcomplex<f32>>
+  // CHECK:         return [[BDST]] : tensor<5x4xcomplex<f32>>
+  %0 = "tf.BatchMatMulV2"(%arg0, %arg1) {adj_x = true, adj_y = true, device = ""} : (tensor<5x2xcomplex<f32>>, tensor<2x4xcomplex<f32>>) -> tensor<5x4xcomplex<f32>>
+  return %0 : tensor<5x4xcomplex<f32>>
+}
diff --git a/tensorflow/compiler/mlir/xla/transforms/legalize_tf.cc b/tensorflow/compiler/mlir/xla/transforms/legalize_tf.cc
index 817dfb55ec9..65704ca8dec 100644
--- a/tensorflow/compiler/mlir/xla/transforms/legalize_tf.cc
+++ b/tensorflow/compiler/mlir/xla/transforms/legalize_tf.cc
@@ -1629,17 +1629,17 @@ class ConvertBatchMatMulV2Op : public OpRewritePattern<TF::BatchMatMulV2Op> {
 
   LogicalResult matchAndRewrite(TF::BatchMatMulV2Op op,
                                 PatternRewriter &rewriter) const override {
-    // TODO(silvasean): Handle adj_x/adj_y
-    // Should be able to just set the contracting_dimensions attribute
-    // appropriately.
-    // For complex types, need to do a complex conjugation.
-    if (op.adj_x() || op.adj_y()) return failure();
-
     Value lhs = op.x();
     Value rhs = op.y();
     auto lhs_type = lhs.getType().dyn_cast<RankedTensorType>();
     auto rhs_type = rhs.getType().dyn_cast<RankedTensorType>();
     if (!lhs_type || !rhs_type) return failure();
+    if (lhs_type.getElementType().isa<ComplexType>() && op.adj_x()) {
+      lhs = rewriter.create<TF::ConjOp>(op.getLoc(), lhs_type, lhs);
+    }
+    if (rhs_type.getElementType().isa<ComplexType>() && op.adj_y()) {
+      rhs = rewriter.create<TF::ConjOp>(op.getLoc(), rhs_type, rhs);
+    }
     // TODO(silvasean): Support dynamic shapes.
     if (!lhs_type.hasStaticShape() || !rhs_type.hasStaticShape()) {
       return failure();
@@ -1654,10 +1654,10 @@ class ConvertBatchMatMulV2Op : public OpRewritePattern<TF::BatchMatMulV2Op> {
     int64_t rank = lhs_type.getRank();
     auto batch_dimensions = GetI64ElementsAttr(
         llvm::to_vector<4>(llvm::seq<int64_t>(0, rank - 2)), &rewriter);
-    auto lhs_contracting_dimensions =
-        GetI64ElementsAttr(llvm::makeArrayRef({rank - 1}), &rewriter);
-    auto rhs_contracting_dimensions =
-        GetI64ElementsAttr(llvm::makeArrayRef({rank - 2}), &rewriter);
+    auto lhs_contracting_dimensions = GetI64ElementsAttr(
+        llvm::makeArrayRef({op.adj_x() ? rank - 2 : rank - 1}), &rewriter);
+    auto rhs_contracting_dimensions = GetI64ElementsAttr(
+        llvm::makeArrayRef({op.adj_y() ? rank - 1 : rank - 2}), &rewriter);
     auto dimension_numbers = DotDimensionNumbers::get(
         /*lhs_batching_dimensions=*/batch_dimensions,
         /*rhs_batching_dimensions=*/batch_dimensions,