add export_for_deployment flag to save_inference_model (#12582)

add export_for_deployment flag to save_inference_model

paddle/fluid/API.spec

@@ -6,7 +6,7 @@ paddle.fluid.Program.create_block ArgSpec(args=['self', 'parent_idx'], varargs=N
 paddle.fluid.Program.current_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Program.get_desc ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.inference_optimize ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.Program.inference_optimize ArgSpec(args=['self', 'export_for_deployment'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Program.optimized_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
@@ -74,7 +74,7 @@ paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_pro
 paddle.fluid.io.load_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None, None))
+paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
 paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))

python/paddle/fluid/framework.py

@@ -1518,7 +1518,7 @@ class Program(object):
             The two code snippets above will generate same programs.
         """
         if for_test:
-            p = self.inference_optimize()
+            p = self.inference_optimize(export_for_deployment=False)
         else:
             p = Program()
             p.desc = core.ProgramDesc(self.desc)
@@ -1578,7 +1578,7 @@ class Program(object):
         res._sync_with_cpp()
         return res
 
-    def inference_optimize(self):
+    def inference_optimize(self, export_for_deployment=True):
         """
         This method will create a new program and do following adjustments on it:
         1. Remove all reader variables and their creator ops if exist.
@@ -1589,6 +1589,10 @@ class Program(object):
         attribute of operators to :code:`True`. All the :code:`Parameter`
         information will be lost.
 
+        Args:
+            export_for_deployment(bool): remove the read ops that are added by py_reader
+                                        for cpp inference library
+
         Notes: This API is a very low level API. Use
         :code:`Program.clone(for_test=True)` instead.
 
@@ -1603,16 +1607,17 @@ class Program(object):
         # remove all readers and the read_op if exist
         read_op_idx = 0
         root_block = res.desc.block(0)
-        while True:
-            if read_op_idx >= root_block.op_size() or root_block.op(
-                    read_op_idx).type() == 'read':
-                break
-            read_op_idx += 1
-        if read_op_idx < root_block.op_size():
-            root_block._remove_op(0, read_op_idx + 1)
-        for var in root_block.all_vars():
-            if var.type() == core.VarDesc.VarType.READER:
-                root_block._remove_var(var.name())
+        if export_for_deployment:
+            while True:
+                if read_op_idx >= root_block.op_size() or root_block.op(
+                        read_op_idx).type() == 'read':
+                    break
+                read_op_idx += 1
+            if read_op_idx < root_block.op_size():
+                root_block._remove_op(0, read_op_idx + 1)
+            for var in root_block.all_vars():
+                if var.type() == core.VarDesc.VarType.READER:
+                    root_block._remove_var(var.name())
 
         # change all `is_test` attributes to True
         for i in range(res.desc.num_blocks()):

python/paddle/fluid/io.py

@@ -555,7 +555,8 @@ def save_inference_model(dirname,
                          executor,
                          main_program=None,
                          model_filename=None,
-                         params_filename=None):
+                         params_filename=None,
+                         export_for_deployment=True):
     """
     Prune the given `main_program` to build a new program especially for inference,
     and then save it and all related parameters to given `dirname` by the `executor`.
@@ -577,6 +578,8 @@ def save_inference_model(dirname,
         params_filename(str|None): The name of file to save all related parameters.
                                    If it is setted None, parameters will be saved
                                    in separate files .
+        export_for_deployment(bool): remove the read ops that are added by py_reader
+                                    for cpp inference lib. Default True
 
     Returns:
         None
@@ -643,7 +646,8 @@ def save_inference_model(dirname,
     copy_program.desc.flush()
 
     pruned_program = copy_program.prune(targets=target_vars)
-    inference_program = pruned_program.inference_optimize()
+    inference_program = pruned_program.inference_optimize(
+        export_for_deployment=export_for_deployment)
     fetch_var_names = [v.name for v in target_vars]
 
     prepend_feed_ops(inference_program, feeded_var_names)

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

@@ -17,6 +17,7 @@ import unittest
 
 from paddle.fluid.framework import Program, default_main_program, program_guard, grad_var_name
 import paddle.fluid.layers as layers
+import paddle.fluid as fluid
 
 main_program = default_main_program()
 
@@ -98,6 +99,39 @@ class TestProgram(unittest.TestCase):
         new_program = main_program.clone()
         self.assertNotEqual(0, len(new_program.blocks[0].all_parameters()))
 
+    def test_program_inference_optimize(self):
+        def net():
+            reader = fluid.layers.py_reader(
+                capacity=10,
+                shapes=[[-1, 10], [-1, 1]],
+                lod_levels=[0, 0],
+                dtypes=['float32', 'int64'],
+                use_double_buffer=True)
+            in_data, label = fluid.layers.read_file(reader)
+            predict_label = fluid.layers.fc(in_data, size=2, act='softmax')
+            loss = fluid.layers.mean(
+                fluid.layers.cross_entropy(
+                    input=predict_label, label=label))
+
+            optimizer = fluid.optimizer.Adam()
+            optimizer.minimize(loss)
+
+        startup_program = fluid.Program()
+        main_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+            net()
+        no_read_program = main_program.inference_optimize()
+        keep_read_program = main_program.inference_optimize(
+            export_for_deployment=False)
+        no_read_ops = no_read_program.global_block().ops
+        keep_read_ops = keep_read_program.global_block().ops
+        self.assertEqual(len(keep_read_ops) - len(no_read_ops), 2)
+        self.assertEqual(keep_read_ops[0].type, 'create_double_buffer_reader')
+        self.assertEqual(keep_read_ops[1].type, 'read')
+
+        for i in range(len(no_read_ops)):
+            self.assertEqual(no_read_ops[i].type, keep_read_ops[i + 2].type)
+
 
 if __name__ == '__main__':
     unittest.main()