diff --git a/paddle/fluid/train/custom_trainer/feed/accessor/input_data_accessor.h b/paddle/fluid/train/custom_trainer/feed/accessor/input_data_accessor.h
index b49eb469abe8338728c7fd36eeb86357df281c43..45c3d051a1d0d0584f193aad35f722dc96cf9371 100644
--- a/paddle/fluid/train/custom_trainer/feed/accessor/input_data_accessor.h
+++ b/paddle/fluid/train/custom_trainer/feed/accessor/input_data_accessor.h
@@ -26,6 +26,10 @@ public:
     virtual int32_t create(::paddle::framework::Scope* scope) {
         return 0;
     }
+    // 裁剪，用于模型裁剪，base级调用
+    virtual int32_t shrink() {
+        return 0;
+    }
 
     // 前向， 一般用于填充输入，在训练网络执行前调用
     virtual int32_t forward(SampleInstance* samples, size_t num,
diff --git a/paddle/fluid/train/custom_trainer/feed/accessor/sparse_input_accessor.cc b/paddle/fluid/train/custom_trainer/feed/accessor/sparse_input_accessor.cc
index 35be38bf315d3745be01473f2637ea57807ef499..68fb7f78cbdb4ccb6b378713c223f6ffbaa9d6f1 100644
--- a/paddle/fluid/train/custom_trainer/feed/accessor/sparse_input_accessor.cc
+++ b/paddle/fluid/train/custom_trainer/feed/accessor/sparse_input_accessor.cc
@@ -253,6 +253,13 @@ public:
             var_data[i] += pull_raw[i + 2];
         }
     }
+    
+    // 裁剪，用于模型裁剪，base级调用
+    virtual int32_t shrink() {
+        auto* ps_client = _trainer_context->pslib->ps_client();
+        auto status = ps_client->shrink(_table_id);
+        return status.get();
+    }
 
     virtual void post_process_input(float* var_data, 
         SparseInputVariable& variable, SampleInstance* samples, size_t num) {
diff --git a/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc b/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc
index 0f7b204efc0f6509359dd6e6493dcf5164fba87a..367f340b20c434f5f103292b12f90f161e3cb555 100755
--- a/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc
+++ b/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc
@@ -169,8 +169,23 @@ int LearnerProcess::run() {
         //Step3. Dump Model For Delta&&Checkpoint
         {
             wait_save_model(epoch_id, ModelSaveWay::ModelSaveInferenceBase);
+            environment->barrier(EnvironmentRole::WORKER); 
             wait_save_model(epoch_id, ModelSaveWay::ModelSaveTrainCheckpoint);
             environment->barrier(EnvironmentRole::WORKER); 
+            if (epoch_accessor->is_last_epoch(epoch_id) &&
+                environment->is_master_node(EnvironmentRole::WORKER)) {
+                paddle::platform::Timer timer;
+                timer.Start();
+                VLOG(2) << "Start shrink table"; 
+                for (auto& executor : _executors) {
+                    const auto& table_accessors = executor->table_accessors();
+                    for (auto& itr : table_accessors) {
+                        CHECK(itr.second[0]->shrink() == 0);
+                    }
+                } 
+                VLOG(2) << "End shrink table, cost" << timer.ElapsedSec();
+            }
+            environment->barrier(EnvironmentRole::WORKER); 
 
             epoch_accessor->epoch_done(epoch_id);
             environment->barrier(EnvironmentRole::WORKER);