create optimize block in pserver program

python/paddle/v2/fluid/distribute_transpiler.py

@@ -347,7 +347,8 @@ class DistributeTranspiler:
                 j -= 1
             return False
 
-    def _append_pserver_ops(self, program, pserver_program, opt_op, endpoint):
+    def _append_pserver_ops(self, optimize_block, opt_op, endpoint):
+        program = optimize_block.program
         new_inputs = dict()
         # update param/grad shape first, then other inputs like
         # moment can use the updated shape
@@ -371,11 +372,11 @@ class DistributeTranspiler:
                 if self.trainers > 1:
                     vars2merge = self._create_var_for_trainers(
                         program.global_block(), grad_block, self.trainers)
-                    program.global_block().append_op(
+                    optimize_block.append_op(
                         type="sum",
                         inputs={"X": vars2merge},
                         outputs={"Out": merged_var})
-                    program.global_block().append_op(
+                    optimize_block.append_op(
                         type="scale",
                         inputs={"X": merged_var},
                         outputs={"Out": merged_var},
@@ -412,25 +413,18 @@ class DistributeTranspiler:
                 dtype=var.dtype,
                 shape=new_shape)
             new_inputs[key] = tmpvar
-            # create var in pserver program global block.
-            # TODO(typhoonzero): put blocks in one program to avoid create two
-            # variables.
-            pserver_program.global_block().create_var(
-                name=var.name,
-                persistable=var.persistable,
-                dtype=var.dtype,
-                shape=new_shape)
 
         # change output's ParamOut variable
         outputs = self._get_output_map_from_op(program.global_block(), opt_op)
         outputs["ParamOut"] = new_inputs["Param"]
-        program.global_block().append_op(
+        optimize_block.append_op(
             type=opt_op.type,
             inputs=new_inputs,
             outputs=outputs,
             attrs=opt_op.attrs)
 
-    def _append_pserver_non_opt_ops(self, program, pserver_program, opt_op):
+    def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
+        program = optimize_block.program
         # Append the ops for parameters that do not need to be optimized/updated
         inputs = self._get_input_map_from_op(self.program.global_block().vars,
                                              opt_op)
@@ -440,14 +434,8 @@ class DistributeTranspiler:
             else:
                 varlist = [var]
             for var in varlist:
-                # TODO(typhoonzero): will remove below line later.
-                program.global_block().create_var(
-                    name=var.name,
-                    persistable=var.persistable,
-                    dtype=var.dtype,
-                    shape=var.shape)
-                if not pserver_program.global_block().vars.has_key(var.name):
-                    pserver_program.global_block().create_var(
+                if not program.global_block().vars.has_key(var.name):
+                    program.global_block().create_var(
                         name=var.name,
                         persistable=var.persistable,
                         dtype=var.dtype,
@@ -456,7 +444,7 @@ class DistributeTranspiler:
         outputs = self._get_output_map_from_op(self.program.global_block().vars,
                                                opt_op)
 
-        program.global_block().append_op(
+        optimize_block.append_op(
             type=opt_op.type,
             inputs=inputs,
             outputs=outputs,
@@ -489,7 +477,7 @@ class DistributeTranspiler:
                     dtype=v.dtype,
                     shape=v.shape)
         # step6
-        optimize_sub_program = Program()
+        optimize_block = pserver_program.create_block(0)
         # Iterate through the ops and append ops as needed
         for idx, opt_op in enumerate(self.optimize_ops):
             is_op_on_pserver = self._is_op_on_pserver(endpoint,
@@ -497,18 +485,17 @@ class DistributeTranspiler:
             if not is_op_on_pserver:
                 continue
             if "Grad" in opt_op.desc.input_arg_names():
-                self._append_pserver_ops(optimize_sub_program, pserver_program,
-                                         opt_op, endpoint)
+                self._append_pserver_ops(optimize_block, opt_op, endpoint)
             else:
-                self._append_pserver_non_opt_ops(optimize_sub_program,
-                                                 pserver_program, opt_op)
+                self._append_pserver_non_opt_ops(optimize_block, opt_op)
+
         # Append the listen_and_serv op
         pserver_program.global_block().append_op(
             type="listen_and_serv",
             inputs={},
             outputs={},
             attrs={
-                "OptimizeBlock": optimize_sub_program.global_block(),
+                "OptimizeBlock": optimize_block,
                 "endpoint": endpoint,
                 "ParamList": [
                     p.name