[Dy2stat] Add descriptor cache for StaticLayer (#26987)

* add descriptor cache

* fix self arugments

* deal case if instance is None

* clean code

* fix usage

python/paddle/fluid/dygraph/dygraph_to_static/convert_call_func.py

@@ -31,6 +31,7 @@ from paddle.fluid.dygraph.dygraph_to_static.convert_operators import convert_len
 from paddle.fluid.dygraph.dygraph_to_static.logging_utils import TranslatorLogger
 from paddle.fluid.dygraph.dygraph_to_static.program_translator import StaticLayer
 from paddle.fluid.dygraph.dygraph_to_static.program_translator import convert_to_static
+from paddle.fluid.dygraph.dygraph_to_static.program_translator import unwrap_decorators
 from paddle.fluid.dygraph.layers import Layer
 
 # TODO(liym27): A better way to do this.
@@ -118,14 +119,9 @@ def convert_call(func):
     func_self = None
     converted_call = None
 
-    # Function in convert_call may be decorated by another `@declarative`,
+    # Function in convert_call may be decorated by another `@to_static`,
     # in this case, unwraps it into a raw method or function.
-    if isinstance(func, StaticLayer):
-        instance = func._class_instance
-        if instance is not None:
-            func = func.dygraph_function.__get__(instance)
-        else:
-            func = func.dygraph_function
+    _, func = unwrap_decorators(func)
 
     if is_builtin_len(func):
         return convert_len
@@ -155,7 +151,8 @@ def convert_call(func):
                 if inspect.isfunction(fn):
                     global_functions.add(fn)
                 elif isinstance(fn, StaticLayer):
-                    global_functions.add(fn.dygraph_function)
+                    _, fn = unwrap_decorators(fn)
+                    global_functions.add(fn)
 
             if func in global_functions:
                 converted_call = convert_to_static(func)
@@ -189,7 +186,8 @@ def convert_call(func):
     elif hasattr(func, '__class__') and hasattr(func.__class__, '__call__'):
         if hasattr(func, 'forward') and isinstance(func, Layer):
             try:
-                forward_func = convert_to_static(func.forward)
+                _, forward_func = unwrap_decorators(func.forward)
+                forward_func = convert_to_static(forward_func)
                 setattr(func, 'forward', forward_func)
                 func_self = func
             except Exception:

python/paddle/fluid/dygraph/dygraph_to_static/program_translator.py

@@ -21,6 +21,7 @@ import six
 import textwrap
 import threading
 import warnings
+import weakref
 
 import gast
 from paddle.fluid import framework
@@ -245,6 +246,7 @@ class StaticLayer(object):
         self._input_spec = input_spec
         self._function_spec = FunctionSpec(function, input_spec)
         self._program_cache = ProgramCache()
+        self._descriptor_cache = weakref.WeakKeyDictionary()
         # Note: Hold a reference to ProgramTranslator for switching `enable_declarative`.
         self._program_trans = ProgramTranslator()
 
@@ -271,8 +273,19 @@ class StaticLayer(object):
         of `Net` instance. After decorated by `@paddle.jit.to_static`, it will firstly to call `__get__`
         to parse the class instance correctly instead of the `StaticLayer` instance.
         """
-        self._class_instance = instance
+        if instance not in self._descriptor_cache:
+            if instance is None:
                 return self
+            # Note(Aurelius84): To construct new instance of StaticLayer when we
+            # first encouter the bound function of layer and cache it.
+            new_static_layer = self._clone()
+            new_static_layer._class_instance = instance
+            self._descriptor_cache[instance] = new_static_layer
+
+        return self._descriptor_cache[instance]
+
+    def _clone(self):
+        return self.__class__(self._dygraph_function, self._input_spec)
 
     def __call__(self, *args, **kwargs):
         """

python/paddle/fluid/tests/unittests/dygraph_to_static/test_declarative.py

@@ -19,7 +19,7 @@ import paddle
 import paddle.fluid as fluid
 from paddle.static import InputSpec
 from paddle.fluid.dygraph import to_variable, declarative, ProgramTranslator, Layer, jit
-from paddle.fluid.dygraph.dygraph_to_static.program_translator import ConcreteProgram
+from paddle.fluid.dygraph.dygraph_to_static.program_translator import ConcreteProgram, StaticLayer
 
 from test_basic_api_transformation import dyfunc_to_variable
 
@@ -84,6 +84,23 @@ class SimpleNet(Layer):
         return z
 
 
+class TestStaticLayerInstance(unittest.TestCase):
+    def test_instance_same_class(self):
+        with fluid.dygraph.guard(fluid.CPUPlace()):
+            net_1 = SimpleNet()
+            net_2 = SimpleNet()
+
+            self.assertTrue(isinstance(net_1.forward, StaticLayer))
+            self.assertTrue(isinstance(net_2.forward, StaticLayer))
+            self.assertNotEqual(net_1.forward, net_2.forward)
+
+            # convert layer into static progam of net_1
+            net_1.forward.concrete_program
+            self.assertTrue(len(net_1.forward.program_cache) == 1)
+            # check no conversion applid with net_2
+            self.assertTrue(len(net_2.forward.program_cache) == 0)
+
+
 class TestInputSpec(unittest.TestCase):
     def setUp(self):
         pass
@@ -224,7 +241,6 @@ class TestDifferentInputSpecCacheProgram(unittest.TestCase):
         # 1. specific InputSpec for `x`/`y`
         concrete_program_1 = foo.get_concrete_program(
             InputSpec([None, 10]), InputSpec([10]))
-        print(concrete_program_1)
         self.assertTrue(len(foo.program_cache) == 1)
 
         # 2. specific `c`/`d` explicitly with same default value

python/paddle/fluid/tests/unittests/dygraph_to_static/test_save_inference_model.py

@@ -133,7 +133,7 @@ class TestPartialProgramRaiseError(unittest.TestCase):
             x = fluid.dygraph.to_variable(x_data)
             out = net(x)
 
-            program_cache = SimpleFcLayer.forward.program_cache
+            program_cache = net.forward.program_cache
             _, (concrete_program, _) = program_cache.last()
 
             params = concrete_program.parameters