[Dy2Stat]Add build_strategy in @to_static to support open pass

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

@@ -131,12 +131,16 @@ class PartialProgramLayer:
         Layer: A Layer object that run all ops internally in static mode.
     """
 
-    def __init__(self, main_program, inputs, outputs, parameters=None):
+    def __init__(self, main_program, inputs, outputs, parameters=None,
+                 **kwargs):
         super(PartialProgramLayer, self).__init__()
         self._inputs = NestSequence(inputs)
         self._outputs = NestSequence(outputs, need_check=True)
         self._params = parameters if parameters is not None else []
 
+        self._build_strategy = kwargs.get('build_strategy', BuildStrategy())
+        assert isinstance(self._build_strategy, BuildStrategy)
+
         self._origin_main_program = self._verify_program(main_program)
         self._tmp_scope_vec = self._create_scope_vec()
         # A fake_var to handle empty input or output
@@ -170,7 +174,11 @@ def _infer_program_id(self):
 
     @LazyInitialized
     def _train_program_id(self):
-        return _hash_with_id(self._train_program, self)
+        program_id = _hash_with_id(self._train_program, self)
+        core._set_cached_executor_build_strategy(program_id,
+                                                 self._build_strategy)
+
+        return program_id
 
     def _verify_program(self, main_program):
         """
@@ -451,6 +459,6 @@ def partial_program_from(concrete_program):
     if inputs and isinstance(inputs[0], layers.Layer):
         inputs = inputs[1:]
 
-    return PartialProgramLayer(concrete_program.main_program, inputs,
-                               concrete_program.outputs,
-                               concrete_program.parameters)
+    return PartialProgramLayer(
+        concrete_program.main_program, inputs, concrete_program.outputs,
+        concrete_program.parameters, **concrete_program.kwargs)

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

@@ -145,14 +145,13 @@ class CacheKey(object):
     """
     Cached key for ProgramCache.
     """
-
     __slots__ = [
         'function_spec', 'input_args_with_spec', 'input_kwargs_with_spec',
-        'class_instance'
+        'class_instance', 'kwargs'
     ]
 
     def __init__(self, function_spec, input_args_with_spec,
-                 input_kwargs_with_spec, class_instance):
+                 input_kwargs_with_spec, class_instance, **kwargs):
         """
         Initializes a cache key.
 
@@ -161,11 +160,14 @@ def __init__(self, function_spec, input_args_with_spec,
             input_args_with_spec(list[InputSpec]): actual input args with some arguments replaced by InputSpec.
             input_kwargs_with_spec(list[{string:InputSpec}]): actual input kwargs with some arguments replaced by InputSpec.
             class_instance(object): a instance of class `Layer`.
+            **kwargs(dict): manage other arguments used for better scalability
         """
         self.function_spec = function_spec
         self.input_args_with_spec = input_args_with_spec
         self.input_kwargs_with_spec = input_kwargs_with_spec
         self.class_instance = class_instance
+        # NOTE: `kwargs` is usually not considered as basic member for `__hash__`
+        self.kwargs = kwargs
 
     @classmethod
     def from_func_and_args(cls, function_spec, args, kwargs, class_instance):
@@ -235,13 +237,14 @@ class StaticFunction(object):
 
     """
 
-    def __init__(self, function, input_spec=None):
+    def __init__(self, function, input_spec=None, **kwargs):
         """
         Initializes a `StaticFunction`.
 
         Args:
             function(callable): A function or method that will be converted into static program.
             input_spec(list[InputSpec]): list of InputSpec to specify the `shape/dtype/name` information for each input argument, default None.
+            **kwargs(dict): other arguments like `build_strategy` et.al.
         """
         # save the instance `self` while decorating a method of class.
         if inspect.ismethod(function):
@@ -257,6 +260,7 @@ def __init__(self, function, input_spec=None):
         self._descriptor_cache = weakref.WeakKeyDictionary()
         # Note: Hold a reference to ProgramTranslator for switching `enable_to_static`.
         self._program_trans = ProgramTranslator()
+        self._kwargs = kwargs
 
     def __get__(self, instance, owner):
         """
@@ -395,7 +399,8 @@ def get_concrete_program(self, *args, **kwargs):
 
         # 2. generate cache key
         cache_key = CacheKey(self._function_spec, input_args_with_spec,
-                             input_kwargs_with_spec, self._class_instance)
+                             input_kwargs_with_spec, self._class_instance,
+                             **self._kwargs)
 
         # 3. check whether hit the cache or build a new program for the input arguments
         concrete_program, partial_program_layer = self._program_cache[cache_key]
@@ -586,7 +591,7 @@ class ConcreteProgram(object):
 
     __slots__ = [
         'inputs', 'outputs', 'main_program', "startup_program", "parameters",
-        "function"
+        "function", 'kwargs'
     ]
 
     def __init__(self,
@@ -595,18 +600,20 @@ def __init__(self,
                  parameters,
                  function,
                  main_program,
-                 startup_program=None):
+                 startup_program=None,
+                 **kwargs):
         self.inputs = inputs
         self.outputs = outputs
         self.main_program = main_program
         self.startup_program = startup_program
         self.parameters = parameters
         self.function = function
+        self.kwargs = kwargs
 
     @staticmethod
     @switch_to_static_graph
-    def from_func_spec(func_spec, input_spec, input_kwargs_spec,
-                       class_instance):
+    def from_func_spec(func_spec, input_spec, input_kwargs_spec, class_instance,
+                       **kwargs):
         """
         Builds the main_program with specialized inputs and returns outputs
         of program as fetch_list.
@@ -635,8 +642,8 @@ def from_func_spec(func_spec, input_spec, input_kwargs_spec,
                 # 1. Adds `fluid.data` layers for input if needed
                 inputs = func_spec.to_static_inputs_with_spec(input_spec,
                                                               main_program)
-                kwargs = func_spec.to_static_inputs_with_spec(input_kwargs_spec,
-                                                              main_program)
+                _kwargs = func_spec.to_static_inputs_with_spec(
+                    input_kwargs_spec, main_program)
                 if class_instance:
                     inputs = tuple([class_instance] + list(inputs))
 
@@ -649,8 +656,8 @@ def from_func_spec(func_spec, input_spec, input_kwargs_spec,
                         class_instance, False)), param_guard(
                             get_buffers(class_instance, False)):
                     try:
-                        if kwargs:
-                            outputs = static_func(*inputs, **kwargs)
+                        if _kwargs:
+                            outputs = static_func(*inputs, **_kwargs)
                         else:
                             outputs = static_func(*inputs)
                     except BaseException as e:
@@ -675,7 +682,8 @@ def from_func_spec(func_spec, input_spec, input_kwargs_spec,
             parameters=all_parameters_and_buffers,
             function=dygraph_function,
             main_program=main_program,
-            startup_program=startup_program)
+            startup_program=startup_program,
+            **kwargs)
 
 
 def _extract_indeed_params_buffers(class_instance):
@@ -702,7 +710,8 @@ def _build_once(self, cache_key):
             func_spec=cache_key.function_spec,
             input_spec=cache_key.input_args_with_spec,
             input_kwargs_spec=cache_key.input_kwargs_with_spec,
-            class_instance=cache_key.class_instance)
+            class_instance=cache_key.class_instance,
+            **cache_key.kwargs)
         return concrete_program, partial_program_from(concrete_program)
 
     def __getitem__(self, item):

python/paddle/fluid/dygraph/jit.py

@@ -158,7 +158,7 @@ def copy_decorator_attrs(original_func, decorated_obj):
     return decorated_obj
 
 
-def declarative(function=None, input_spec=None):
+def declarative(function=None, input_spec=None, build_strategy=None):
     """
     Converts imperative dygraph APIs into declarative function APIs. Decorator
     @declarative handles the Program and Executor of static mode and returns
@@ -171,6 +171,12 @@ def declarative(function=None, input_spec=None):
         function (callable): callable imperative function.
         input_spec(list[InputSpec]|tuple[InputSpec]): list/tuple of InputSpec to specific the shape/dtype/name
             information of each input Tensor.
+        build_strategy(BuildStrategy|None): This argument is used to compile the
+            converted program with the specified options, such as operators' fusion
+            in the computational graph and memory optimization during the execution
+            of the computational graph. For more information about build_strategy,
+            please refer to :code:`paddle.static.BuildStrategy`. The default is None.
+
 
     Returns:
         Tensor(s): containing the numerical result.
@@ -206,10 +212,18 @@ def decorated(python_func):
         static_layer = copy_decorator_attrs(
             original_func=python_func,
             decorated_obj=StaticFunction(
-                function=python_func, input_spec=input_spec))
+                function=python_func,
+                input_spec=input_spec,
+                build_strategy=build_strategy))
 
         return static_layer
 
+    build_strategy = build_strategy or BuildStrategy()
+    if not isinstance(build_strategy, BuildStrategy):
+        raise TypeError(
+            "Required type(build_strategy) shall be `paddle.static.BuildStrategy`, but received {}".
+            format(type(build_strategy).__name__))
+
     # for usage: `declarative(foo, ...)`
     if function is not None:
         if isinstance(function, Layer):

python/paddle/fluid/tests/unittests/dygraph_to_static/CMakeLists.txt

@@ -25,6 +25,7 @@ set_tests_properties(test_reinforcement_learning PROPERTIES TIMEOUT 120)
 set_tests_properties(test_transformer PROPERTIES TIMEOUT 200)
 set_tests_properties(test_bmn PROPERTIES TIMEOUT 120)
 #set_tests_properties(test_mnist PROPERTIES TIMEOUT 120)
+set_tests_properties(test_build_strategy PROPERTIES TIMEOUT 120)
 
 if(NOT WIN32)
     set_tests_properties(test_resnet_v2 PROPERTIES TIMEOUT 120)

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

@@ -0,0 +1,88 @@
+# Copyright (c) 2021 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.
+
+import os
+import paddle
+import unittest
+import numpy as np
+from paddle.jit import ProgramTranslator
+
+from test_resnet import ResNet, train, predict_dygraph_jit
+from test_resnet import predict_dygraph, predict_static, predict_analysis_inference
+
+program_translator = ProgramTranslator()
+
+
+class TestResnetWithPass(unittest.TestCase):
+    def setUp(self):
+        self.build_strategy = paddle.static.BuildStrategy()
+        self.build_strategy.fuse_elewise_add_act_ops = True
+        self.build_strategy.fuse_bn_act_ops = True
+        self.build_strategy.fuse_bn_add_act_ops = True
+        self.build_strategy.enable_addto = True
+        # NOTE: for enable_addto
+        paddle.fluid.set_flags({"FLAGS_max_inplace_grad_add": 8})
+
+    def train(self, to_static):
+        program_translator.enable(to_static)
+
+        return train(to_static, self.build_strategy)
+
+    def verify_predict(self):
+        image = np.random.random([1, 3, 224, 224]).astype('float32')
+        dy_pre = predict_dygraph(image)
+        st_pre = predict_static(image)
+        dy_jit_pre = predict_dygraph_jit(image)
+        predictor_pre = predict_analysis_inference(image)
+        self.assertTrue(
+            np.allclose(dy_pre, st_pre),
+            msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
+        self.assertTrue(
+            np.allclose(dy_jit_pre, st_pre),
+            msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
+        self.assertTrue(
+            np.allclose(predictor_pre, st_pre),
+            msg="predictor_pre:\n {}\n, st_pre: \n{}.".format(predictor_pre,
+                                                              st_pre))
+
+    def test_resnet(self):
+        static_loss = self.train(to_static=True)
+        dygraph_loss = self.train(to_static=False)
+        self.assertTrue(
+            np.allclose(static_loss, dygraph_loss),
+            msg="static_loss: {} \n dygraph_loss: {}".format(static_loss,
+                                                             dygraph_loss))
+        self.verify_predict()
+
+    def test_in_static_mode_mkldnn(self):
+        paddle.fluid.set_flags({'FLAGS_use_mkldnn': True})
+        try:
+            if paddle.fluid.core.is_compiled_with_mkldnn():
+                train(True, self.build_strategy)
+        finally:
+            paddle.fluid.set_flags({'FLAGS_use_mkldnn': False})
+
+
+class TestError(unittest.TestCase):
+    def test_type_error(self):
+        def foo(x):
+            out = x + 1
+            return out
+
+        with self.assertRaises(TypeError):
+            static_foo = paddle.jit.to_static(foo, build_strategy="x")
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -190,7 +190,6 @@ def __init__(self, layers=50, class_dim=102):
             param_attr=fluid.param_attr.ParamAttr(
                 initializer=fluid.initializer.Uniform(-stdv, stdv)))
 
-    @declarative
     def forward(self, inputs):
         y = self.conv(inputs)
         y = self.pool2d_max(y)
@@ -213,7 +212,7 @@ def __reader__():
     return __reader__
 
 
-def train(to_static):
+def train(to_static, build_strategy=None):
     """
     Tests model decorated by `dygraph_to_static_output` in static mode. For users, the model is defined in dygraph mode and trained in static mode.
     """
@@ -231,6 +230,8 @@ def train(to_static):
         data_loader.set_sample_list_generator(train_reader)
 
         resnet = ResNet()
+        if to_static:
+            resnet = paddle.jit.to_static(resnet, build_strategy=build_strategy)
         optimizer = optimizer_setting(parameter_list=resnet.parameters())
 
         for epoch in range(epoch_num):

tools/windows/run_unittests.sh

@@ -96,6 +96,7 @@ disable_wincpu_test="^jit_kernel_test$|\
 ^test_bmn$|\
 ^test_mobile_net$|\
 ^test_resnet_v2$|\
+^test_build_strategy$|\
 ^test_se_resnet$|\
 ^disable_wincpu_test$"