diff --git a/paddle/fluid/operators/fusion_seq_concat_fc_op.cc b/paddle/fluid/operators/fusion_seq_concat_fc_op.cc
index 810df3c3ed838872d8d61c7db56707b896df598b..203ebaf3e28e7972a166d45b296e2e043213b41b 100644
--- a/paddle/fluid/operators/fusion_seq_concat_fc_op.cc
+++ b/paddle/fluid/operators/fusion_seq_concat_fc_op.cc
@@ -25,30 +25,15 @@ namespace operators {
 void FusionSeqConcatFCOp::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE(ctx->HasInput("X"),
                  "Input(X) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("C0"),
-                 "Input(C0) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("LSTMWeight"),
-                 "Input(LSTMWeight) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("LSTMBias"),
-                 "Input(LSTMBias) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(
-      ctx->HasInput("AttentionWeight"),
-      "Input(AttentionWeight) of FusionSeqConcatFC should not be null.");
-
-  PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
-                 "Output(Hidden) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("Cell"),
-                 "Output(Cell) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(
-      ctx->HasOutput("AttentionedX"),
-      "Output(AttentionedX) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(
-      ctx->HasOutput("AttentionFCOut"),
-      "Output(AttentionFCOut) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("LSTMX"),
-                 "Output(LSTMX) of FusionSeqConcatFC should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("LSTMOUT"),
-                 "Output(LSTMOUT) of FusionSeqConcatFC should not be null.");
+  PADDLE_ENFORCE(ctx->HasInput("FCWeight"),
+                 "Input(FCWeight) of FusionSeqConcatFC should not be null.");
+
+  PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                 "Output(Out) of FusionSeqConcatFC should not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput("FCOut"),
+                 "Output(FCOut) of FusionSeqConcatFC should not be null.");
+
+  // need check fc height = all inputs width sum
 
   auto x_dims = ctx->GetInputDim("X");
   const int M = x_dims[1];
@@ -120,6 +105,9 @@ void FusionSeqConcatFCOp::InferShape(framework::InferShapeContext* ctx) const {
   // AttentionFCOut should be reshape as (maxseqlen,1) in runtime
   ctx->ShareLoD("X", "Hidden");
   ctx->ShareLoD("X", "Cell");
+
+  ctx->SetOutputDim("Out", out_dims);
+  ctx->ShareLoD("X", /*->*/ "Out");
 }
 
 framework::OpKernelType FusionSeqConcatFCOp::GetExpectedKernelType(
@@ -131,95 +119,37 @@ framework::OpKernelType FusionSeqConcatFCOp::GetExpectedKernelType(
 
 void FusionSeqConcatFCOpMaker::Make() {
   AddInput("X",
-           "(LoDTensor) the input is a LodTensor, which support "
-           "variable-time length input sequence. The underlying tensor in "
-           "this LoDTensor is a matrix with shape (T X M), where T is the "
-           "total time steps in this mini-batch, M is the dim size of x.");
-  AddInput("C0",
-           "(Tensor) LSTM C0"
-           "This is a tensor with shape (N x D), where N is the batch size, D "
-           "is the gate size."
-           "C0 is necessary because of attention.");
-  AddInput("H0",
-           "(Tensor, optional) LSTM H0"
-           "This is a tensor with shape (N x D), where N is the "
-           "batch size and D is the gate size.")
-      .AsDispensable();
-  AddInput("AttentionWeight",
-           "(Tensor) the weights of attention fc. Always relu the fc result."
-           "The shape is ((M+D) x 1), where M is the dim size of x, D is the "
-           "gate size of LSTM.");
-  AddInput("AttentionBias",
-           "(Tensor, optional) the bias of attention fc."
-           "The shape is (1 x 1)")
-      .AsDispensable();
-  AddInput("AttentionScalar",
-           "(Tensor, optional) the scalar on the result of attentioned fc. "
-           "Always relu the Scalar."
-           "The shape is (1 x 1)")
-      .AsDispensable();
-  AddInput("AttentionScalarBias",
-           "(Tensor, optional) the scalar bias of attention fc."
-           "The shape is (1 x 1)")
-      .AsDispensable();
-  AddInput("LSTMWeight",
-           "(Tensor) the combined weight of LSTM"
-           " - The shape is ((D+M) x 4D), where D is the hidden gate size, M "
-           "is the dim size of x"
-           " - Weight = {W_forget, W_input, W_output, W_cell}");
-  AddInput("LSTMBias",
-           "(Tensor) the combined bias of LSTM, shape (1x4D)."
-           "Note: we should add the bias of hidden and context accorindg to "
-           "the same gate: "
-           "{B_forget, B_input, B_output, B_cell}");
-  AddOutput("Hidden",
-            "(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
-            "The shape is (T x D), and lod is the same with the `Input`.");
-  AddOutput("Cell",
-            "(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
-            "The shape is (T x D), and lod is the same with the `Input`.");
-  AddOutput("AttentionedX",
-            "(Tensor) shape is (T x 1), the result after X * AttentionWeight,"
-            " where T is the total time steps in this mini-batch,"
-            " D is the hidden size.")
-      .AsIntermediate();
-  AddOutput("AttentionFCOut",
-            "(Tensor) (max_seq_len, 1), compute at each step.")
-      .AsIntermediate();
-  AddOutput("LSTMX",
-            "(Tensor) the input X of LSTM for each step."
-            "Shape is (1 x M), where M is the x frame size")
-      .AsIntermediate();
+           "(LoDTensor) input LodDTensors, the first one must be have ref lod "
+           "for sequence expand, and the rest input should have same lod.")
+      .AsDuplicable();
+  AddInput("FCWeight", "(Tensor) the weights of fc.");
+  AddInput("FCBias", "(Tensor, optional) the bias of fc.").AsDispensable();
+  AddOutput("Out", "(LoDTensor) Output LodTensor.");
   AddOutput(
-      "LSTMOUT",
-      "(Tensor) the output of LSTM X(1*(D+M))* weight((D+M)*4D) for each step."
-      "Shape is (1 x 4D), where M is the x frame size")
+      "FCOut",
+      "(Tensor) the intermediate tensor to keep the result of fc."
+      "Shape is (N x D), where N is the batch size, D is the output dim of fc")
       .AsIntermediate();
-  AddAttr<std::string>("gate_activation",
-                       "(string, default: sigmoid)"
-                       "The activation for input gate, forget gate and output "
-                       "gate, `sigmoid` by default.")
-      .SetDefault("sigmoid")
-      .InEnum({"sigmoid", "tanh", "relu", "identity"});
-  AddAttr<std::string>("cell_activation",
-                       "(string, default: tanh)"
-                       "The activation for cell output, `tanh` by defalut.")
-      .SetDefault("tanh")
-      .InEnum({"sigmoid", "tanh", "relu", "identity"});
-  AddAttr<std::string>("candidate_activation",
-                       "(string, default: tanh)"
-                       "The activation for candidate hidden state, "
-                       "`tanh` by default.")
-      .SetDefault("tanh")
+  AddAttr<std::string>("fc_activation",
+                       "(string, default: identity)"
+                       "The activation for the result of fc."
+                       "`identity` by default.")
+      .SetDefault("identity")
       .InEnum({"sigmoid", "tanh", "relu", "identity"});
   AddComment(R"DOC(
 Fusion Sequence expand + concat + fc Operator.
 
-Only support seq_expand ref_level=0,
+All below conditions should be meet:
 
-and the ref lod of seq_expand level is the first input of concat,
+The ref_level of seq_expand should be 0.
 
-and the other inputs should have same lod and same batch size of ref lod.
+The ref lod of seq_expand level is the first input of concat.
+
+The other inputs should have same lod and same batch size of ref lod.
+
+The seq len of other inputs should be 1.
+
+The concat axis should be 1.
 
 )DOC");
 }
@@ -257,150 +187,68 @@ class FusionSeqConcatFCKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     using DeviceContext = paddle::platform::CPUDeviceContext;
+    auto* ins = ctx.Input<LoDTensor>("X");
+    auto* w = ctx.Input<Tensor>("FCWeight");
+    auto* b = ctx.Input<Tensor>("FCBias");
 
-    auto* x = ctx.Input<LoDTensor>("X");
-    auto* h0 = ctx.Input<Tensor>("H0");
-    auto* c0 = ctx.Input<Tensor>("C0");
-    auto* atten_w = ctx.Input<Tensor>("AttentionWeight");
-    auto* atten_b = ctx.Input<Tensor>("AttentionBias");
-    auto* atten_scalar = ctx.Input<Tensor>("AttentionScalar");
-    auto* atten_scalar_bias = ctx.Input<Tensor>("AttentionScalarBias");
-    auto* lstm_w = ctx.Input<Tensor>("LSTMWeight");
-    auto* lstm_b = ctx.Input<Tensor>("LSTMBias");
-
-    auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
-    auto* cell_out = ctx.Output<LoDTensor>("Cell");
-    auto* atted_x = ctx.Output<Tensor>("AttentionedX");
-    auto* fc_out = ctx.Output<Tensor>("AttentionFCOut");
-    auto* lstm_x = ctx.Output<Tensor>("LSTMX");
-    auto* lstm_out = ctx.Output<Tensor>("LSTMOUT");
-
-    // some shape should be reshape here since infershape can not get lod info
-    auto x_lod = x->lod();
-    const int N = x_lod[0].size() - 1;  // batch size
-    auto x_dims = x->dims();            // T x M
-    auto w_dims = lstm_w->dims();       // (D+M) x 4D
-    const int total_T = x_dims[0];
-    const int M = x_dims[1];      // x frame size
-    const int D = w_dims[1] / 4;  // gate frame size
-    const int D2 = D * 2;
-    const int D3 = D * 3;
-    const int D4 = w_dims[1];
-    int max_seq_len = x_lod[0][1];
-    for (int i = 1; i < N; ++i) {
-      int len = x_lod[0][i + 1] - x_lod[0][i];
-      max_seq_len = max_seq_len < len ? len : max_seq_len;
-    }
-    PADDLE_ENFORCE_EQ(x_lod.size(), 1, "Input(X)'s lod size must be 1.");
-    PADDLE_ENFORCE_EQ(c0->dims()[0], N, "C0 dims should be %d x %d.", N, D);
-    fc_out->Resize({max_seq_len, 1});
-
-    std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
-    auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
-    auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
-    auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
+    auto* out = ctx.Output<LoDTensor>("Out");
+    auto* fc_out = ctx.Output<Tensor>("FCOUT");
+
+    std::function<void(const int, const T*, T*)> fc_act;
+    auto& fc_act_str = ctx.Attr<std::string>("fc_activation");
     if (platform::jit::MayIUse(platform::jit::avx)) {
       math::VecActivations<T, platform::jit::avx> act_functor;
-      act_gate = act_functor(act_gate_str);
-      act_cell = act_functor(act_cell_str);
-      act_cand = act_functor(act_cand_str);
+      fc_act = act_functor(fc_act_str);
     } else {
       math::VecActivations<T, platform::jit::isa_any> act_functor;
-      act_gate = act_functor(act_gate_str);
-      act_cell = act_functor(act_cell_str);
-      act_cand = act_functor(act_cand_str);
+      fc_act = act_functor(fc_act_str);
     }
 
-    const T* x_data = x->data<T>();
-    const T* h0_data = h0 ? h0->data<T>() : NULL;
-    const T* c0_data = c0->data<T>();
-    const T* lstm_w_data = lstm_w->data<T>();
-    const T* lstm_b_data = lstm_b->data<T>();
-    const T* atten_w_data = atten_w->data<T>();
-    const T* atten_b_data = atten_b ? atten_b->data<T>() : NULL;
-    const T* atten_scalar_data = atten_scalar ? atten_scalar->data<T>() : NULL;
-    const T* atten_scalar_bias_data =
-        atten_scalar_bias ? atten_scalar_bias->data<T>() : NULL;
-
-    T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
-    T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
-    T* atted_x_data = atted_x->mutable_data<T>(ctx.GetPlace());
+    PADDLE_ENFORCE_GT(ins.size(), 1, "Input(X)'s size must larger than 1.");
+    auto* ref_in = ins[0];
+    auto ref_in_lod = ref_in->lod();
+    const int N = ref_in_lod[0].size() - 1;
+    auto ref_in_dims = ref_in->dims();  // T x M0
+    auto w_dims = w->dims();            // (M0+M1+M2+..) x D
+    const int total_T = ref_in_dims[0];
+    const int M0 = ref_in_dims[1];
+    const int M1 = ins[1]->dims()[1];
+    const int D = w_dims[1];
+
+    const T* ref_in_data =
+        ref_in->data<T>();  // size should be check at infershape
+    const T* in1_data = ins[1]->data<T>();
+    const T* w_data = w->data<T>();
+    T* out_data = out->mutable_data<T>(ctx.GetPlace());
     T* fc_out_data = fc_out->mutable_data<T>(ctx.GetPlace());
-    T* lstm_x_data = lstm_x->mutable_data<T>(ctx.GetPlace());
-    T* lstm_out_data = lstm_out->mutable_data<T>(ctx.GetPlace());
 
-    // x(TxM) * fc (Mx1) part of atten_wgt(M+D)x1
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
-    math::FCCompute<DeviceContext, T>(blas, total_T, 1, M, x_data, atten_w_data,
-                                      atted_x_data, atten_b_data);
-
-    const T* cur_atten_x_data = atted_x_data;
-    const T* cur_x_data = x_data;
-    const T* prev_cell_data = NULL;
-    const T* prev_hidden_data = NULL;
-    T* cur_cell_out_data = cell_out_data;
-    T* cur_hidden_out_data = hidden_out_data;
+    math::FCCompute<DeviceContext, T>(blas, total_T, D, M0, ref_in_data, w_data,
+                                      out_data, b ? b->data<T>() : NULL);
+    w_data = w_data + M0 * D;
+
+    // first one use write on
+    blas.MatMul(N, D, M1, in1_data, w_data, fc_out_data);
+    w_data = w_data + M1 * D;
+    for (int i = 2; i < ins.size(); ++i) {
+      // add on
+      const T* in_data = ins[i]->data<T>();
+      const int K = ins[i]->dims()[1];
+      blas.GEMM(CblasNoTrans, CblasNoTrans, N, D, K, static_cast<T>(1), in_data,
+                K, w_data, D, static_cast<T>(1), fc_out_data, D);
+      w_data = w_data + K * D;
+    }
+
     for (int i = 0; i < N; ++i) {
-      int seq_len = x_lod[0][i + 1] - x_lod[0][i];
-      prev_cell_data = c0_data + i * D;
-      prev_hidden_data = h0_data ? h0_data + i * D : NULL;
+      int seq_len = ref_in_lod[0][i + 1] - ref_in_lod[0][i];
+      T* src = fc_out_data + i * D;
       for (int step = 0; step < seq_len; ++step) {
-        /// 1. compute attention vector
-        // 1a. prev_cell(1xD) * fc(D) rest part of atten_wgt
-        T prev_cell_bias = blas.DOT(D, prev_cell_data, atten_w_data + M);
-        // 1b. add cell bias and relu
-        bias_relu<T>(seq_len, cur_atten_x_data, &prev_cell_bias, fc_out_data);
-        // 1c. fc scalar
-        if (atten_scalar_data) {
-          blas.SCAL(seq_len, *atten_scalar_data, fc_out_data);
-          bias_relu<T>(seq_len, fc_out_data, atten_scalar_bias_data,
-                       fc_out_data);
-        }
-        // 1d. softmax
-        vec_softmax<T>(seq_len, fc_out_data, fc_out_data);
-        // mul x(seq_len*M) and sum pool
-        math::FCCompute<DeviceContext, T>(blas, 1, M, seq_len, fc_out_data,
-                                          cur_x_data, lstm_x_data);
-
-        /// 2. compute LSTM step
-        // lstm weight : concat[forget , input , output , tilde]
-        // shape : (D + M) x (4 * D)
-        // fc inputX(1xM) * weightX(M*(4D))  => 1 x 4D
-        blas.MatMul(1, D4, M, lstm_x_data, lstm_w_data + D * D4, lstm_out_data);
-        if (prev_hidden_data) {
-          blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
-                    prev_hidden_data, D, lstm_w_data, D4, static_cast<T>(1),
-                    lstm_out_data, D4);
-        }
-        // since input is 1xM, so can use add bias
-        blas.VADD(D4, lstm_b_data, lstm_out_data, lstm_out_data);
-
-        // gate act: sigmoid
-        act_gate(D3, lstm_out_data, lstm_out_data);
-        // candicate act: tanh
-        act_cand(D, lstm_out_data + D3, lstm_out_data + D3);
-
-        // a = forget * prev_cell
-        blas.VMUL(D, lstm_out_data, prev_cell_data, lstm_out_data);
-
-        // b = input * tilde
-        blas.VMUL(D, lstm_out_data + D, lstm_out_data + D3, lstm_out_data + D);
-
-        // cell_out = a + b
-        blas.VADD(D, lstm_out_data, lstm_out_data + D, cur_cell_out_data);
-
-        // state act tanh(cell_out) * output_gate
-        act_cell(D, cur_cell_out_data, lstm_out_data);
-        blas.VMUL(D, lstm_out_data, lstm_out_data + D2, cur_hidden_out_data);
-
-        prev_hidden_data = cur_hidden_out_data;
-        prev_cell_data = cur_cell_out_data;
-        cur_cell_out_data = cur_cell_out_data + D;
-        cur_hidden_out_data = cur_hidden_out_data + D;
+        blas.VADD(D, out_data, src, out_data);
+        out_data = out_data + D;
       }
-      cur_x_data = cur_x_data + seq_len * M;
-      cur_atten_x_data = cur_atten_x_data + seq_len;
     }
+
+    fc_act(out_dims[0] * out_dims[1], out_data, out_data);
   }
 };