diff --git a/paddle/fluid/operators/custom_device_common_op_registry.cc b/paddle/fluid/operators/custom_device_common_op_registry.cc
index 729e840e89d3e9b437e1654074687bb64d2df450..6fded8e113751465dab492c7a07683a38b62261b 100644
--- a/paddle/fluid/operators/custom_device_common_op_registry.cc
+++ b/paddle/fluid/operators/custom_device_common_op_registry.cc
@@ -279,7 +279,7 @@ class CEmbeddingGradOpCustomDeviceKernel : public framework::OpKernel<T> {
           x_tensor, start_index + N, x_tensor.dtype(), x_tensor.place());
       auto ids_mask_tensor = paddle::experimental::logical_and(
           x_tensor.greater_equal(start_index_tensor),
-          x_tensor.less_equal(end_index_tensor));
+          x_tensor.less_than(end_index_tensor));
       auto real_ids_tensor = (x_tensor - start_index_tensor)
                                  .multiply(paddle::experimental::cast(
                                      ids_mask_tensor, x_tensor.dtype()));
@@ -668,6 +668,594 @@ class BarrierOpCustomDeviceKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename T>
+class NumberCountOpCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto numbers = context.Input<phi::DenseTensor>("numbers");
+    auto upper_range = context.Attr<int>("upper_range");
+    auto number_count = context.Output<phi::DenseTensor>("Out");
+    const auto& dev_ctx = context.template device_context<phi::CustomContext>();
+    number_count->Resize({upper_range});
+    dev_ctx.template Alloc<T>(number_count);
+    phi::DenseTensor cpu_tensor;
+    framework::TensorCopySync(*numbers, platform::CPUPlace(), &cpu_tensor);
+    std::vector<T> count(upper_range);
+    for (auto i = 0; i < cpu_tensor.numel(); ++i) {
+      auto idx = static_cast<int64_t>(cpu_tensor.data<T>()[i]);
+      if (idx >= 0 && idx < upper_range) {
+        count[idx] += 1;
+      }
+    }
+    framework::TensorFromVector<T>(count, dev_ctx, number_count);
+    number_count->Resize({upper_range});
+  }
+};
+
+template <typename T>
+class LimitByCapacityOpCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto expert_count = context.Input<phi::DenseTensor>("expert_count");
+    auto capacity = context.Input<phi::DenseTensor>("capacity");
+    auto out = context.Output<phi::DenseTensor>("Out");
+    auto n_worker = context.Attr<int>("n_worker");
+    auto n_expert = expert_count->numel() / n_worker;
+
+    const auto& dev_ctx = context.template device_context<phi::CustomContext>();
+
+    dev_ctx.template Alloc<T>(out);
+    std::vector<T> out_data(out->numel());
+    phi::DenseTensor expert_count_cpu, capacity_cpu;
+    framework::TensorCopySync(
+        *expert_count, platform::CPUPlace(), &expert_count_cpu);
+    framework::TensorCopySync(*capacity, platform::CPUPlace(), &capacity_cpu);
+
+    auto* ec_data = expert_count_cpu.data<T>();
+    auto* capacity_data = capacity_cpu.data<T>();
+    int eid, wid;
+    for (int64_t i = 0; i < expert_count->numel(); ++i) {
+      wid = i / n_expert;
+      eid = i % n_expert;
+      auto proposal = ec_data[i];
+      auto cap_left = capacity_data[eid];
+      capacity_data[eid] -= proposal;
+      if (cap_left >= proposal) {
+        out_data[wid * n_expert + eid] = proposal;
+      } else if (cap_left >= 0) {
+        out_data[wid * n_expert + eid] = cap_left;
+      } else {
+        out_data[wid * n_expert + eid] = 0;
+      }
+    }
+
+    auto out_dims = out->dims();
+    framework::TensorFromVector<T>(out_data, dev_ctx, out);
+    out->Resize(out_dims);
+  }
+};
+
+template <typename T>
+class PruneGateByCapacityCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* gate_idx = context.Input<phi::DenseTensor>("GateIdx");
+    auto* expert_count = context.Input<phi::DenseTensor>("ExpertCount");
+    auto* new_gate_idx = context.Output<phi::DenseTensor>("NewGateIdx");
+    const auto& dev_ctx = context.template device_context<phi::CustomContext>();
+    dev_ctx.template Alloc<T>(new_gate_idx);
+
+    phi::DenseTensor expert_count_cpu, gate_idx_cpu;
+    framework::TensorCopySync(
+        *expert_count, platform::CPUPlace(), &expert_count_cpu);
+    framework::TensorCopySync(*gate_idx, platform::CPUPlace(), &gate_idx_cpu);
+    auto expert_count_data = expert_count_cpu.data<T>();
+    auto gate_idx_data = gate_idx_cpu.data<T>();
+    std::vector<T> new_gate_idx_data(gate_idx->numel());
+    for (auto i = 0; i < gate_idx->numel(); ++i) {
+      auto orig_cap = expert_count_data[gate_idx_data[i]]--;
+      if (orig_cap <= 0) {
+        new_gate_idx_data[i] = -1;
+      } else {
+        new_gate_idx_data[i] = gate_idx_data[i];
+      }
+    }
+  }
+};
+
+template <typename T>
+class RandomRoutingOpCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto topk_idx = context.Input<phi::DenseTensor>("TopK_Idx");
+    auto topk_value = context.Input<phi::DenseTensor>("TopK_Value");
+    auto prob = context.Input<phi::DenseTensor>("Prob");
+    auto out = context.Output<phi::DenseTensor>("Out");
+
+    const auto& dev_ctx = context.template device_context<phi::CustomContext>();
+    size_t D = topk_idx->dims()[1];
+
+    phi::DenseTensor topk_value_cpu, prob_cpu;
+    framework::TensorCopySync(
+        *topk_value, platform::CPUPlace(), &topk_value_cpu);
+    framework::TensorCopySync(*prob, platform::CPUPlace(), &prob_cpu);
+    auto* topk_value_data = topk_value_cpu.data<T>();
+    auto* prob_data = prob_cpu.data<T>();
+    std::vector<int64_t> out_data(topk_idx->numel());
+
+    for (int64_t idx = 0; idx < topk_idx->numel(); ++idx) {
+      size_t row = idx / D;
+      size_t col = idx % D;
+      if (col == 1 &&
+          static_cast<T>(2) * topk_value_data[idx] < prob_data[row]) {
+        out_data[idx] = static_cast<int64_t>(-1);
+      }
+    }
+    auto out_dims = out->dims();
+    framework::TensorFromVector<int64_t>(out_data, dev_ctx, out);
+    out->Resize(out_dims);
+  }
+};
+
+template <typename T>
+class AssignPosCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    // assign pos decides which tokens should be fetched belong to specially
+    // counter orderingly.
+    auto cum_count = context.Input<phi::DenseTensor>(
+        "cum_count");  // (counter number) int32 | int64
+    auto numbers = context.Input<phi::DenseTensor>(
+        "X");  // (batch_size * seq_len, topk) int32
+    auto eff_num_len =
+        context.Input<phi::DenseTensor>("eff_num_len");  // (sum(cum_count))
+    auto out =
+        context.Output<phi::DenseTensor>("Out");  // (cum_count) value ranges
+                                                  // from 0 to batch_size *
+                                                  // seq_len * topk
+    const auto& dev_ctx = context.template device_context<phi::CustomContext>();
+
+    phi::DenseTensor cpu_eff_num_len;
+    int64_t cpu_eff_num_len_data = 0;
+    if (platform::is_cpu_place(eff_num_len->place())) {
+      cpu_eff_num_len_data = eff_num_len->data<T>()[0];
+    } else {
+      framework::TensorCopySync(
+          *eff_num_len, platform::CPUPlace(), &cpu_eff_num_len);
+      cpu_eff_num_len_data = cpu_eff_num_len.data<T>()[0];
+    }
+
+    out->Resize({cpu_eff_num_len_data});
+    dev_ctx.template Alloc<T>(out);
+
+    phi::DenseTensor numbers_cpu, cum_count_cpu;
+    framework::TensorCopySync(*numbers, platform::CPUPlace(), &numbers_cpu);
+    framework::TensorCopySync(*cum_count, platform::CPUPlace(), &cum_count_cpu);
+    auto* numbers_data = numbers_cpu.data<T>();
+    auto* cum_count_data = cum_count_cpu.data<T>();
+
+    std::vector<T> out_data(cpu_eff_num_len_data);
+    for (int64_t i = 0; i < numbers->numel(); ++i) {
+      int number_idx = numbers_data[i];
+      if (number_idx > -1) {
+        cum_count_data[number_idx] -= 1;
+        int p = cum_count_data[number_idx];
+        out_data[p] = i;
+      }
+    }
+    framework::TensorFromVector<int64_t>(out_data, dev_ctx, out);
+  }
+};
+
+template <typename T>
+class GlobalScatterOpCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto x = ctx.Input<phi::DenseTensor>("X");
+    auto local_count = ctx.Input<phi::DenseTensor>("local_count");
+    auto global_count = ctx.Input<phi::DenseTensor>("global_count");
+    auto out = ctx.Output<phi::DenseTensor>("Out");
+    const int rid = ctx.Attr<int>("ring_id");
+    const auto& dev_ctx = ctx.template device_context<phi::CustomContext>();
+    auto place = ctx.GetPlace();
+
+    PADDLE_ENFORCE_EQ(local_count->dtype(),
+                      phi::DataType::INT64,
+                      platform::errors::InvalidArgument(
+                          "Please use int64 type in local_count."));
+    PADDLE_ENFORCE_EQ(global_count->dtype(),
+                      phi::DataType::INT64,
+                      platform::errors::InvalidArgument(
+                          "Please use int64 type in global_count."));
+
+    auto map = distributed::ProcessGroupMapFromGid::getInstance();
+    const int64_t* cpu_local_count_data;
+    const int64_t* cpu_global_count_data;
+    phi::DenseTensor cpu_local_count;
+    if (platform::is_cpu_place(local_count->place())) {
+      cpu_local_count_data = local_count->data<int64_t>();
+    } else {
+      framework::TensorCopySync(
+          *local_count, platform::CPUPlace(), &cpu_local_count);
+      cpu_local_count_data = cpu_local_count.data<int64_t>();
+    }
+    auto global_count_len = 0;
+    phi::DenseTensor cpu_global_count;
+    if (platform::is_cpu_place(global_count->place())) {
+      cpu_global_count_data = global_count->data<int64_t>();
+      global_count_len = global_count->numel();
+    } else {
+      framework::TensorCopySync(
+          *global_count, platform::CPUPlace(), &cpu_global_count);
+      cpu_global_count_data = cpu_global_count.data<int64_t>();
+      global_count_len = cpu_global_count.numel();
+    }
+
+    if (map->has(rid)) {
+      distributed::ProcessGroup* pg = map->get(rid);
+      auto stream =
+          reinterpret_cast<phi::CustomContext*>(pg->GetDeviceContext(place))
+              ->GetStream();
+      int nranks = pg->GetSize();
+      int rank = pg->GetRank();
+      auto in_feat = x->dims()[1];
+      auto n_expert = local_count->dims()[0] / nranks;
+      int64_t fwd_count = 0;
+
+      for (auto i = 0; i < global_count_len; ++i) {
+        fwd_count += cpu_global_count_data[i];
+      }
+      framework::DDim out_dims = phi::make_ddim({fwd_count, in_feat});
+      int64_t* expert_ptr = new int64_t[n_expert * nranks];
+      expert_ptr[0] = 0;
+      auto tot_experts = n_expert * nranks;
+      for (auto i = 1; i < tot_experts; ++i) {
+        expert_ptr[i] = expert_ptr[i - 1] + cpu_local_count_data[i - 1];
+      }
+
+      auto recv_ptr = 0;
+      out->Resize(out_dims);
+      dev_ctx.template Alloc<T>(out);
+
+      for (auto i = 0; i < n_expert; ++i) {
+        for (auto j = 0; j < rank; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_global_count_data[idx]) {
+            pg->Recv(out,
+                     j,
+                     recv_ptr * in_feat,
+                     cpu_global_count_data[idx] * in_feat,
+                     /*sync_op*/ true);
+            recv_ptr += cpu_global_count_data[idx];
+          }
+        }
+        for (auto j = 0; j < nranks; ++j) {
+          if (j != rank) {
+            int idx = i + j * n_expert;
+            if (cpu_local_count_data[idx]) {
+              phi::DenseTensor tmp = *x;
+              pg->Send(tmp,
+                       j,
+                       expert_ptr[idx] * in_feat,
+                       cpu_local_count_data[idx] * in_feat,
+                       /*sync_op*/ true);
+            }
+          }
+        }
+        if (cpu_local_count_data[i + rank * n_expert]) {
+          phi::DeviceManager::GetDeviceWithPlace(place)->MemoryCopyD2D(
+              reinterpret_cast<void*>(out->data<T>() + recv_ptr * in_feat),
+              reinterpret_cast<const void*>(x->data<T>() +
+                                            expert_ptr[rank] * in_feat),
+              (cpu_local_count_data[rank] * in_feat) * phi::SizeOf(x->dtype()),
+              stream.get());
+          recv_ptr += cpu_global_count_data[rank];
+        }
+        for (auto j = rank + 1; j < nranks; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_global_count_data[idx]) {
+            pg->Recv(out,
+                     j,
+                     recv_ptr * in_feat,
+                     cpu_global_count_data[idx] * in_feat,
+                     /*sync_op*/ true);
+            recv_ptr += cpu_global_count_data[idx];
+          }
+        }
+      }
+    } else {
+      auto comm = platform::XCCLCommContext::Instance(place.GetDeviceType())
+                      .Get(rid, place);
+      std::shared_ptr<phi::stream::Stream> stream;
+      if (ctx.Attr<bool>("use_calc_stream")) {
+        stream = dev_ctx.GetStream();
+      } else {
+        stream = comm->stream();
+      }
+      int nranks = comm->nranks();
+      int rank = comm->rank();
+      auto in_feat = x->dims()[1];
+      auto n_expert = local_count->dims()[0] / nranks;
+      int64_t fwd_count = 0;
+
+      for (auto i = 0; i < global_count_len; ++i) {
+        fwd_count += cpu_global_count_data[i];
+      }
+      framework::DDim out_dims = phi::make_ddim({fwd_count, in_feat});
+      int64_t* expert_ptr = new int64_t[n_expert * nranks];
+      expert_ptr[0] = 0;
+      auto tot_experts = n_expert * nranks;
+      for (auto i = 1; i < tot_experts; ++i) {
+        expert_ptr[i] = expert_ptr[i - 1] + cpu_local_count_data[i - 1];
+      }
+
+      auto recv_ptr = 0;
+      auto send_buf = x->data<T>();
+      out->Resize(out_dims);
+      auto recv_buf = dev_ctx.template Alloc<T>(out);
+
+      for (auto i = 0; i < n_expert; ++i) {
+        for (auto j = 0; j < rank; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_global_count_data[idx]) {
+            phi::DeviceManager::CCLRecv(
+                place.GetDeviceType(),
+                reinterpret_cast<void*>(recv_buf + recv_ptr * in_feat),
+                cpu_global_count_data[idx] * in_feat,
+                phi::ccl::ToCCLDataType(x->dtype()),
+                j,
+                comm->comm(),
+                *stream);
+            recv_ptr += cpu_global_count_data[idx];
+          }
+        }
+        for (auto j = 0; j < nranks; ++j) {
+          if (j != rank) {
+            int idx = i + j * n_expert;
+            if (cpu_local_count_data[idx]) {
+              phi::DeviceManager::CCLSend(
+                  place.GetDeviceType(),
+                  const_cast<void*>(reinterpret_cast<const void*>(
+                      send_buf + expert_ptr[idx] * in_feat)),
+                  cpu_local_count_data[idx] * in_feat,
+                  phi::ccl::ToCCLDataType(x->dtype()),
+                  j,
+                  comm->comm(),
+                  *stream);
+            }
+          }
+        }
+        if (cpu_local_count_data[i + rank * n_expert]) {
+          phi::DeviceManager::GetDeviceWithPlace(place)->MemoryCopyD2D(
+              reinterpret_cast<void*>(recv_buf + recv_ptr * in_feat),
+              reinterpret_cast<const void*>(send_buf +
+                                            expert_ptr[rank] * in_feat),
+              (cpu_local_count_data[rank] * in_feat) * phi::SizeOf(x->dtype()),
+              stream.get());
+          recv_ptr += cpu_global_count_data[rank];
+        }
+        for (auto j = rank + 1; j < nranks; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_global_count_data[idx]) {
+            phi::DeviceManager::CCLRecv(
+                place.GetDeviceType(),
+                reinterpret_cast<void*>(recv_buf + recv_ptr * in_feat),
+                cpu_global_count_data[idx] * in_feat,
+                phi::ccl::ToCCLDataType(x->dtype()),
+                j,
+                comm->comm(),
+                *stream);
+            recv_ptr += cpu_global_count_data[idx];
+          }
+        }
+      }
+    }
+
+    phi::DeviceManager::SynchronizeDevice(ctx.GetPlace());
+  }
+};
+
+template <typename T>
+class GlobalGatherOpCustomDeviceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto x = ctx.Input<phi::DenseTensor>("X");
+    auto local_count = ctx.Input<phi::DenseTensor>("local_count");
+    auto global_count = ctx.Input<phi::DenseTensor>("global_count");
+    const int rid = ctx.Attr<int>("ring_id");
+    const auto& dev_ctx = ctx.template device_context<phi::CustomContext>();
+    auto place = ctx.GetPlace();
+    auto out = ctx.Output<phi::DenseTensor>("Out");
+
+    PADDLE_ENFORCE_EQ(local_count->dtype(),
+                      phi::DataType::INT64,
+                      platform::errors::InvalidArgument(
+                          "Please use int64 type in local_count."));
+    PADDLE_ENFORCE_EQ(global_count->dtype(),
+                      phi::DataType::INT64,
+                      platform::errors::InvalidArgument(
+                          "Please use int64 type in global_count."));
+
+    const int64_t* cpu_local_count_data;
+    const int64_t* cpu_global_count_data;
+    auto local_count_len = 0;
+    phi::DenseTensor cpu_local_count;
+    if (platform::is_cpu_place(local_count->place())) {
+      cpu_local_count_data = local_count->data<int64_t>();
+      local_count_len = local_count->numel();
+    } else {
+      framework::TensorCopySync(
+          *local_count, platform::CPUPlace(), &cpu_local_count);
+      cpu_local_count_data = cpu_local_count.data<int64_t>();
+      local_count_len = cpu_local_count.numel();
+    }
+    phi::DenseTensor cpu_global_count;
+    if (platform::is_cpu_place(global_count->place())) {
+      cpu_global_count_data = global_count->data<int64_t>();
+    } else {
+      framework::TensorCopySync(
+          *global_count, platform::CPUPlace(), &cpu_global_count);
+      cpu_global_count_data = cpu_global_count.data<int64_t>();
+    }
+
+    auto map = distributed::ProcessGroupMapFromGid::getInstance();
+
+    if (map->has(rid)) {
+      distributed::ProcessGroup* pg = map->get(rid);
+      auto stream =
+          reinterpret_cast<phi::CustomContext*>(pg->GetDeviceContext(place))
+              ->GetStream();
+      int nranks = pg->GetSize();
+      int rank = pg->GetRank();
+      auto in_feat = x->dims()[1];
+      auto n_expert = local_count->dims()[0] / nranks;
+      auto fwd_count = 0;
+      for (auto i = 0; i < local_count_len; ++i) {
+        fwd_count += cpu_local_count_data[i];
+      }
+      framework::DDim out_dims = phi::make_ddim({fwd_count, in_feat});
+      int64_t* expert_ptr = new int64_t[n_expert * nranks];
+      expert_ptr[0] = 0;
+      auto tot_experts = n_expert * nranks;
+      for (auto i = 1; i < tot_experts; ++i) {
+        expert_ptr[i] = expert_ptr[i - 1] + cpu_local_count_data[i - 1];
+      }
+      auto send_ptr = 0;
+      out->Resize(out_dims);
+      dev_ctx.template Alloc<T>(out);
+
+      for (auto i = 0; i < n_expert; ++i) {
+        for (auto j = 0; j < rank; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_local_count_data[idx]) {
+            pg->Recv(out,
+                     j,
+                     expert_ptr[idx] * in_feat,
+                     cpu_local_count_data[idx] * in_feat,
+                     /*sync_op*/ true);
+          }
+        }
+        for (auto j = 0; j < nranks; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_global_count_data[idx]) {
+            if (j != rank) {
+              phi::DenseTensor tmp = *x;
+              pg->Send(tmp,
+                       j,
+                       send_ptr * in_feat,
+                       cpu_global_count_data[idx] * in_feat,
+                       /*sync_op*/ true);
+            } else {
+              phi::DeviceManager::GetDeviceWithPlace(place)->MemoryCopyD2D(
+                  reinterpret_cast<void*>(out->data<T>() +
+                                          expert_ptr[idx] * in_feat),
+                  reinterpret_cast<const void*>(x->data<T>() +
+                                                send_ptr * in_feat),
+                  (cpu_global_count_data[idx] * in_feat) *
+                      phi::SizeOf(x->dtype()),
+                  stream.get());
+            }
+            send_ptr += cpu_global_count_data[idx];
+          }
+        }
+        for (auto j = rank + 1; j < nranks; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_local_count_data[idx]) {
+            pg->Recv(out,
+                     j,
+                     expert_ptr[idx] * in_feat,
+                     cpu_local_count_data[idx] * in_feat,
+                     /*sync_op*/ true);
+          }
+        }
+      }
+    } else {
+      auto comm = platform::XCCLCommContext::Instance(place.GetDeviceType())
+                      .Get(rid, place);
+      std::shared_ptr<phi::stream::Stream> stream;
+      if (ctx.Attr<bool>("use_calc_stream")) {
+        stream = dev_ctx.GetStream();
+      } else {
+        stream = comm->stream();
+      }
+      int nranks = comm->nranks();
+      int rank = comm->rank();
+      auto in_feat = x->dims()[1];
+      auto n_expert = local_count->dims()[0] / nranks;
+
+      auto fwd_count = 0;
+
+      for (auto i = 0; i < local_count_len; ++i) {
+        fwd_count += cpu_local_count_data[i];
+      }
+      framework::DDim out_dims = phi::make_ddim({fwd_count, in_feat});
+      int64_t* expert_ptr = new int64_t[n_expert * nranks];
+      expert_ptr[0] = 0;
+      auto tot_experts = n_expert * nranks;
+      for (auto i = 1; i < tot_experts; ++i) {
+        expert_ptr[i] = expert_ptr[i - 1] + cpu_local_count_data[i - 1];
+      }
+      auto send_ptr = 0;
+      auto send_buf = x->data<T>();
+      out->Resize(out_dims);
+      auto recv_buf = dev_ctx.template Alloc<T>(out);
+
+      for (auto i = 0; i < n_expert; ++i) {
+        for (auto j = 0; j < rank + 1; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_local_count_data[idx]) {
+            phi::DeviceManager::CCLRecv(place.GetDeviceType(),
+                                        recv_buf + expert_ptr[idx] * in_feat,
+                                        cpu_local_count_data[idx] * in_feat,
+                                        phi::ccl::ToCCLDataType(x->dtype()),
+                                        j,
+                                        comm->comm(),
+                                        *stream);
+          }
+        }
+        for (auto j = 0; j < nranks; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_global_count_data[idx]) {
+            if (j != rank) {
+              phi::DeviceManager::CCLSend(
+                  place.GetDeviceType(),
+                  const_cast<void*>(reinterpret_cast<const void*>(
+                      send_buf + send_ptr * in_feat)),
+                  cpu_global_count_data[idx] * in_feat,
+                  phi::ccl::ToCCLDataType(x->dtype()),
+                  j,
+                  comm->comm(),
+                  *stream);
+            } else {
+              phi::DeviceManager::GetDeviceWithPlace(place)->MemoryCopyD2D(
+                  reinterpret_cast<void*>(recv_buf + expert_ptr[idx] * in_feat),
+                  reinterpret_cast<const void*>(send_buf + send_ptr * in_feat),
+                  (cpu_global_count_data[idx] * in_feat) *
+                      phi::SizeOf(x->dtype()),
+                  stream.get());
+            }
+            send_ptr += cpu_global_count_data[idx];
+          }
+        }
+        for (auto j = rank + 1; j < nranks; ++j) {
+          int idx = i + j * n_expert;
+          if (cpu_local_count_data[idx]) {
+            phi::DeviceManager::CCLRecv(place.GetDeviceType(),
+                                        recv_buf + expert_ptr[idx] * in_feat,
+                                        cpu_local_count_data[idx] * in_feat,
+                                        phi::ccl::ToCCLDataType(x->dtype()),
+                                        j,
+                                        comm->comm(),
+                                        *stream);
+          }
+        }
+      }
+    }
+
+    phi::DeviceManager::SynchronizeDevice(ctx.GetPlace());
+  }
+};
+
 template <typename Context>
 void FeedDenseTensorKernel(const Context& dev_ctx,
                            const phi::ExtendedTensor& x,
@@ -918,6 +1506,48 @@ void RegisterCustomDeviceCommonKernel(const std::string& dev_type) {
       barrier,
       device_type,
       paddle::operators::BarrierOpCustomDeviceKernel<int>) {}
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      number_count,
+      device_type,
+      paddle::operators::NumberCountOpCustomDeviceKernel<int64_t>) {}
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      limit_by_capacity,
+      device_type,
+      paddle::operators::LimitByCapacityOpCustomDeviceKernel<int64_t>) {}
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      prune_gate_by_capacity,
+      device_type,
+      paddle::operators::PruneGateByCapacityCustomDeviceKernel<int64_t>) {}
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      random_routing,
+      device_type,
+      paddle::operators::RandomRoutingOpCustomDeviceKernel<float>,
+      paddle::operators::RandomRoutingOpCustomDeviceKernel<double>,
+      paddle::operators::RandomRoutingOpCustomDeviceKernel<
+          paddle::platform::float16>) {}
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      assign_pos,
+      device_type,
+      paddle::operators::AssignPosCustomDeviceKernel<int64_t>) {}
+
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      global_scatter,
+      device_type,
+      paddle::operators::GlobalScatterOpCustomDeviceKernel<float>,
+      paddle::operators::GlobalScatterOpCustomDeviceKernel<double>,
+      paddle::operators::GlobalScatterOpCustomDeviceKernel<int32_t>,
+      paddle::operators::GlobalScatterOpCustomDeviceKernel<int64_t>,
+      paddle::operators::GlobalScatterOpCustomDeviceKernel<
+          paddle::platform::float16>) {}
+  REGISTER_OP_CUSTOM_DEVICE_KERNEL(
+      global_gather,
+      device_type,
+      paddle::operators::GlobalGatherOpCustomDeviceKernel<float>,
+      paddle::operators::GlobalGatherOpCustomDeviceKernel<double>,
+      paddle::operators::GlobalGatherOpCustomDeviceKernel<int32_t>,
+      paddle::operators::GlobalGatherOpCustomDeviceKernel<int64_t>,
+      paddle::operators::GlobalGatherOpCustomDeviceKernel<
+          paddle::platform::float16>) {}
 #endif
 }
 
diff --git a/python/paddle/incubate/distributed/models/moe/moe_layer.py b/python/paddle/incubate/distributed/models/moe/moe_layer.py
index 73d64d1f5243b01c7cf422cf891c67780c9821e9..7c63c431bb95058b5ba494fe281cdbd1410383b9 100644
--- a/python/paddle/incubate/distributed/models/moe/moe_layer.py
+++ b/python/paddle/incubate/distributed/models/moe/moe_layer.py
@@ -19,6 +19,8 @@
 #     Copyright 2021, Jiaao He. All rights reserved.
 #   Licensed under the Apache License, Version 2.0 (the "License").
 
+import os
+
 import numpy as np
 
 import paddle
@@ -352,7 +354,10 @@ class MoELayer(nn.Layer):
         assert experts is not None
         self.experts = experts
 
-        if self.world_size > 1:
+        if (
+            self.world_size > 1
+            and os.getenv("PADDLE_DISTRI_BACKEND", None) != "xccl"
+        ):
             check_nccl_version_for_p2p()
 
         self.mp_group = mp_group
diff --git a/python/paddle/nn/layer/layers.py b/python/paddle/nn/layer/layers.py
index cbd96b97be2b6b26f34fe93ca387cafb8521d9ec..6155fdae7eee1c6f95d3e7c07f5bf971d5548f34 100644
--- a/python/paddle/nn/layer/layers.py
+++ b/python/paddle/nn/layer/layers.py
@@ -1913,6 +1913,13 @@ class Layer:
                     p = core.Place()
                     p.set_place(t._place())
                     place = core.XPUPlace(p.xpu_device_id())
+                elif p.is_custom_place():
+                    p = core.Place()
+                    p.set_place(t._place())
+                    place = core.CustomPlace(
+                        paddle.device.get_device().split(':')[0],
+                        p.custom_device_id(),
+                    )
                 else:
                     p = core.Place()
                     p.set_place(t._place())
diff --git a/python/paddle/static/io.py b/python/paddle/static/io.py
index b0bb6eabbe652a05b478beb4ddbd75b7a90692c0..eef8cacc0e60584895c9baa18835aa474bd2fd2c 100644
--- a/python/paddle/static/io.py
+++ b/python/paddle/static/io.py
@@ -1540,7 +1540,12 @@ def load(program, model_path, executor=None, var_list=None):
             p = paddle.fluid.core.Place()
             p.set_place(t._place())
             place = paddle.fluid.XPUPlace(p.xpu_device_id())
-
+        elif p.is_custom_place():
+            p = paddle.fluid.core.Place()
+            p.set_place(t._place())
+            place = paddle.fluid.CustomPlace(
+                paddle.device.get_device().split(':')[0], p.custom_device_id()
+            )
         else:
             p = paddle.fluid.core.Place()
             p.set_place(t._place())