prune the feed op in compiler (#18997)

test=develop

python/paddle/fluid/compiler.py

@@ -45,6 +45,15 @@ def _is_pserver_mode(main_program):
     return False
 
 
+def _prune_feed_ops(program):
+    # prune the feed ops in the program.
+    pop_idx = []
+    for i, op in enumerate(program.global_block().ops):
+        if op.type == "feed": pop_idx.append(i)
+    for index in pop_idx[::-1]:
+        program.global_block()._remove_op(index)
+
+
 class CompiledProgram(object):
     """
     Compiles to Graph for execution.
@@ -100,6 +109,7 @@ class CompiledProgram(object):
             # don't not create a new program here.
             self._program = None
         elif isinstance(program_or_graph, framework.Program):
+            _prune_feed_ops(program_or_graph)
             self._graph = core.Graph(program_or_graph.desc)
             self._program = program_or_graph
         else:

python/paddle/fluid/tests/unittests/test_parallel_executor_run_load_infer_program.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+
+import paddle.fluid as fluid
+from simple_nets import simple_fc_net, init_data
+
+
+class TestMNIST(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.save_dirname = "./"
+        cls.model_filename = "test_parallel_executor_run_load_infer_program_model"
+        cls.params_filename = "test_parallel_executor_run_load_infer_program_parameter"
+        cls.place = fluid.CPUPlace()
+        cls.exe = fluid.Executor(cls.place)
+        img, label = init_data()
+        cls.batch_data = []
+        for img, label in zip(img, label):
+            cls.batch_data.append([img, label])
+
+    def test_simple_fc(self):
+        exe_loss = self.run_with_executor()
+
+        [inference_program, feed_target_names,
+         fetch_targets] = fluid.io.load_inference_model(
+             self.save_dirname, self.exe, self.model_filename,
+             self.params_filename)
+
+        train_exe = fluid.ParallelExecutor(
+            use_cuda=False, main_program=inference_program)
+        feed_vars = [
+            inference_program.global_block().var(var_name)
+            for var_name in ["image", "label"]
+        ]
+        feeder = fluid.DataFeeder(place=self.place, feed_list=feed_vars)
+
+        pe_loss = train_exe.run(feed=feeder.feed(self.batch_data),
+                                fetch_list=[fetch_targets[0].name])
+        assert exe_loss == pe_loss
+
+    def run_with_executor(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            loss = simple_fc_net()
+
+        feed_vars = [
+            main.global_block().var(var_name)
+            for var_name in ["image", "label"]
+        ]
+        feeder = fluid.DataFeeder(place=self.place, feed_list=feed_vars)
+
+        self.exe.run(startup)
+
+        loss_data = self.exe.run(main,
+                                 feed=feeder.feed(self.batch_data),
+                                 fetch_list=[loss.name])
+
+        fluid.io.save_inference_model(
+            self.save_dirname, ["image", "label"], [loss],
+            self.exe,
+            model_filename=self.model_filename,
+            params_filename=self.params_filename,
+            main_program=main)
+
+        return loss_data
+
+
+if __name__ == '__main__':
+    unittest.main()