Add flush of program desc to update the proto information.

paddle/fluid/framework/block_desc.cc

@@ -146,6 +146,7 @@ void BlockDesc::RemoveOp(size_t s, size_t e) {
   if (ops_.begin() + s == ops_.end() || ops_.begin() + e == ops_.end()) {
     return;
   }
+  need_update_ = true;
   ops_.erase(ops_.begin() + s, ops_.begin() + e);
 }
 

paddle/fluid/framework/program_desc.cc

@@ -27,10 +27,14 @@ BlockDesc *ProgramDesc::AppendBlock(const BlockDesc &parent) {
   return blocks_.back().get();
 }
 
-proto::ProgramDesc *ProgramDesc::Proto() {
+void ProgramDesc::Flush() {
   for (auto &block : blocks_) {
     block->Flush();
   }
+}
+
+proto::ProgramDesc *ProgramDesc::Proto() {
+  Flush();
   return &desc_;
 }
 

paddle/fluid/framework/program_desc.h

@@ -51,6 +51,8 @@ class ProgramDesc {
 
   size_t Size() const { return blocks_.size(); }
 
+  void Flush();
+
   proto::ProgramDesc *Proto();
 
   // The output variable of feed_op is referenced as feed_target.

paddle/fluid/pybind/protobuf.cc

@@ -127,6 +127,7 @@ void BindProgramDesc(pybind11::module *m) {
       .def("block", &pd::ProgramDesc::MutableBlock,
            pybind11::return_value_policy::reference)
       .def("num_blocks", &pd::ProgramDesc::Size)
+      .def("flush", &pd::ProgramDesc::Flush)
       .def("get_feed_target_names", &pd::ProgramDesc::GetFeedTargetNames)
       .def("get_fetch_target_names", &pd::ProgramDesc::GetFetchTargetNames)
       .def("serialize_to_string", SerializeMessage<pd::ProgramDesc>)

python/paddle/fluid/io.py

@@ -336,18 +336,20 @@ def save_inference_model(dirname,
 
     if main_program is None:
         main_program = default_main_program()
+    copy_program = main_program
 
     if not os.path.isdir(dirname):
         os.makedirs(dirname)
 
     # Clear the is_target information and remove the existed feed and fetch op
-    global_block = main_program.global_block()
+    global_block = copy_program.global_block()
     for i, op in enumerate(global_block.ops):
         op.desc.set_is_target(False)
         if op.type == "feed" or op.type == "fetch":
             global_block.remove_op(i)
+    copy_program.desc.flush()
 
-    pruned_program = main_program.prune(targets=target_vars)
+    pruned_program = copy_program.prune(targets=target_vars)
     inference_program = pruned_program.inference_optimize()
     fetch_var_names = [v.name for v in target_vars]