export paddle.static.normalize_program method. (#31072)

* export paddle.static.normalize_program method. test=develop

* fix ut coverage.test=develop

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

@@ -356,6 +356,48 @@ class TestSaveInferenceModelNew(unittest.TestCase):
         self.assertRaises(TypeError, paddle.static.io.deserialize_persistables,
                           None, None, None)
 
+    def test_normalize_program(self):
+        init_program = fluid.default_startup_program()
+        program = fluid.default_main_program()
+
+        # fake program without feed/fetch
+        with program_guard(program, init_program):
+            x = layers.data(name='x', shape=[2], dtype='float32')
+            y = layers.data(name='y', shape=[1], dtype='float32')
+
+            y_predict = layers.fc(input=x, size=1, act=None)
+
+            cost = layers.square_error_cost(input=y_predict, label=y)
+            avg_cost = layers.mean(cost)
+
+            sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
+            sgd_optimizer.minimize(avg_cost, init_program)
+
+        place = core.CPUPlace()
+        exe = executor.Executor(place)
+        exe.run(init_program, feed={}, fetch_list=[])
+
+        tensor_x = np.array([[1, 1], [1, 2], [5, 2]]).astype("float32")
+        tensor_y = np.array([[-2], [-3], [-7]]).astype("float32")
+        for i in six.moves.xrange(3):
+            exe.run(program,
+                    feed={'x': tensor_x,
+                          'y': tensor_y},
+                    fetch_list=[avg_cost])
+
+        # test if return type of serialize_program is bytes
+        res = paddle.static.normalize_program(program, [x, y], [avg_cost])
+        self.assertTrue(isinstance(res, Program))
+        # test program type
+        self.assertRaises(TypeError, paddle.static.normalize_program, None,
+                          [x, y], [avg_cost])
+        # test feed_vars type
+        self.assertRaises(TypeError, paddle.static.normalize_program, program,
+                          'x', [avg_cost])
+        # test fetch_vars type
+        self.assertRaises(TypeError, paddle.static.normalize_program, program,
+                          [x, y], 'avg_cost')
+
 
 class TestLoadInferenceModelError(unittest.TestCase):
     def test_load_model_not_exist(self):

python/paddle/static/__init__.py

@@ -59,6 +59,7 @@ from .io import deserialize_program  #DEFINE_ALIAS
 from .io import serialize_program  #DEFINE_ALIAS
 from .io import load_from_file  #DEFINE_ALIAS
 from .io import save_to_file  #DEFINE_ALIAS
+from .io import normalize_program  #DEFINE_ALIAS
 from ..fluid import Scope  #DEFINE_ALIAS
 from .input import data  #DEFINE_ALIAS
 from .input import InputSpec  #DEFINE_ALIAS

python/paddle/static/io.py

@@ -46,6 +46,7 @@ __all__ = [
     'deserialize_program',
     'deserialize_persistables',
     'load_from_file',
+    'normalize_program',
 ]
 
 _logger = get_logger(
@@ -127,10 +128,64 @@ def _clone_var_in_block(block, var):
             persistable=True)
 
 
-def _normalize_program(program, feed_vars, fetch_vars):
+def normalize_program(program, feed_vars, fetch_vars):
     """
-    optimize program according feed_vars and fetch_vars.
+    :api_attr: Static Graph
+
+    Normalize/Optimize a program according to feed_vars and fetch_vars.
+
+    Args:
+        program(Program): Specify a program you want to optimize.
+        feed_vars(Variable | list[Variable]): Variables needed by inference.
+        fetch_vars(Variable | list[Variable]): Variables returned by inference.
+
+    Returns:
+        Program: Normalized/Optimized program.
+
+    Raises:
+        TypeError: If `program` is not a Program, an exception is thrown.
+        TypeError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
+        TypeError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # normalize main program.
+            program = default_main_program()
+            normalized_program = paddle.static.normalize_program(program, [image], [predict])
+
     """
+    if not isinstance(program, Program):
+        raise TypeError(
+            "program type must be `fluid.Program`, but received `%s`" %
+            type(program))
+    if not isinstance(feed_vars, list):
+        feed_vars = [feed_vars]
+    if not all(isinstance(v, Variable) for v in feed_vars):
+        raise TypeError(
+            "feed_vars type must be a Variable or a list of Variable.")
+    if not isinstance(fetch_vars, list):
+        fetch_vars = [fetch_vars]
+    if not all(isinstance(v, Variable) for v in fetch_vars):
+        raise TypeError(
+            "fetch_vars type must be a Variable or a list of Variable.")
+
     # remind users to set auc_states to 0 if auc op were found.
     for op in program.global_block().ops:
         # clear device of Op
@@ -255,7 +310,7 @@ def serialize_program(feed_vars, fetch_vars, **kwargs):
     _check_vars('fetch_vars', fetch_vars)
 
     program = _get_valid_program(kwargs.get('program', None))
-    program = _normalize_program(program, feed_vars, fetch_vars)
+    program = normalize_program(program, feed_vars, fetch_vars)
     return _serialize_program(program)
 
 
@@ -319,7 +374,7 @@ def serialize_persistables(feed_vars, fetch_vars, executor, **kwargs):
     _check_vars('fetch_vars', fetch_vars)
 
     program = _get_valid_program(kwargs.get('program', None))
-    program = _normalize_program(program, feed_vars, fetch_vars)
+    program = normalize_program(program, feed_vars, fetch_vars)
     return _serialize_persistables(program, executor)
 
 
@@ -463,7 +518,7 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor,
     _check_vars('fetch_vars', fetch_vars)
 
     program = _get_valid_program(kwargs.get('program', None))
-    program = _normalize_program(program, feed_vars, fetch_vars)
+    program = normalize_program(program, feed_vars, fetch_vars)
     # serialize and save program
     program_bytes = _serialize_program(program)
     save_to_file(model_path, program_bytes)