diff --git a/src/opr/impl/search_policy/algo_chooser.cpp b/src/opr/impl/search_policy/algo_chooser.cpp
index 9f712c4040913b84fe0af16e3f4786356dd91972..a77a2af7474599251ace649eb48a043f2272ebe8 100644
--- a/src/opr/impl/search_policy/algo_chooser.cpp
+++ b/src/opr/impl/search_policy/algo_chooser.cpp
@@ -384,8 +384,15 @@ AlgoChooser<Opr>::choose_by_profile(ExeContext& ctx,
     MIDOUT_B(Opr, midout_iv(MGB_HASH_STR("AlgoChooser::choose_by_profile")))
     if (ctx.owner_graph()->options().no_profiling_on_shape_change) {
         auto policy = ctx.megdnn_opr()->execution_policy();
-        if (policy.algo.valid())
+        if (policy.algo.valid()){
             return policy;
+        }
+        if (!algo_usable_on_shape_change<Opr>()) {
+            mgb_log_warn(
+                    "choose algo by heuristic, which may cause performance "
+                    "regression.");
+            return ctx.choose_by_heuristic(selected_strategy);
+        }
     }
 
     if (enable_update) {
diff --git a/src/opr/include/megbrain/opr/search_policy/profiler.h b/src/opr/include/megbrain/opr/search_policy/profiler.h
index da91abab8bd1d86cbfd65532f86fab57e3fdb9b3..486563cbe55b705d060e77b4c731ba1233560720 100644
--- a/src/opr/include/megbrain/opr/search_policy/profiler.h
+++ b/src/opr/include/megbrain/opr/search_policy/profiler.h
@@ -89,6 +89,13 @@ constexpr bool opr_contain_bias() {
     return std::is_same<Opr, megdnn::ConvBias>::value;
 }
 
+//! matmul and batchedMatrixMul may not be usable once shape changed
+template <typename Opr>
+constexpr bool algo_usable_on_shape_change() {
+    return !(std::is_same<Opr, megdnn::MatrixMul>::value ||
+             std::is_same<Opr, megdnn::BatchedMatrixMul>::value);
+}
+
 template <typename Opr, bool has_prep>
 struct PreprocessFilterImpl {
     using T = union {};
diff --git a/src/opr/test/blas.cpp b/src/opr/test/blas.cpp
index e02f2b97ead8cd6139d02b575b0b760c6e6fead1..c019804272c004a1a1c9173d5a04a5d686758bd9 100644
--- a/src/opr/test/blas.cpp
+++ b/src/opr/test/blas.cpp
@@ -885,5 +885,44 @@ TEST(TestOprBlas, SingularValueDecompositionZeroGrad) {
     run_svd_empty_grad_test<1, 1, 1>();
 }
 
+#if MGB_ENABLE_FASTRUN
+TEST(TestOprBlas, MatrixMulExePolicy) {
+    using Param = opr::MatrixMul::Param;
+    Param param;
+    using Policy = opr::MatrixMul::ExecutionPolicy;
+    using S = Policy::Strategy;
+    Policy policy;
+    policy.strategy = S::PROFILE;
+
+    auto cn = CompNode::load("cpux");
+
+    int nr_get = 0;
+    auto on_get = [&nr_get](const std::string&, const void*, size_t,
+                            const void*, size_t) { ++nr_get; };
+    PersistentCacheHook cache_hook{on_get};
+
+    auto graph = ComputingGraph::make();
+    HostTensorGenerator<> gen;
+
+    auto mkvar = [&](const char* name, const TensorShape& shp) {
+        return opr::Host2DeviceCopy::make(*graph, gen(shp), cn).rename(name);
+    };
+
+    auto a = mkvar("a", {20, 50});
+    auto b = mkvar("b", {50, 40});
+    auto matmul = opr::MatrixMul::make(a, b, param, policy, {});
+
+    HostTensorND host_y;
+    graph->options().no_profiling_on_shape_change = true;
+    auto func = graph->compile({make_callback_copy(matmul, host_y)});
+    func->execute();
+    ASSERT_EQ(nr_get, 0);
+    graph->options().no_profiling_on_shape_change = false;
+    func = graph->compile({make_callback_copy(matmul, host_y)});
+    func->execute();
+    ASSERT_GT(nr_get, 0);
+}
+#endif
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}
 //