Get final strategy (#27602)

* add get final strategy for user to print final strategy

python/paddle/distributed/fleet/__init__.py

@@ -30,6 +30,7 @@ __all__ = [
 ]
 
 fleet = Fleet()
+_final_strategy = fleet._final_strategy
 init = fleet.init
 is_first_worker = fleet.is_first_worker
 worker_index = fleet.worker_index

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -1244,8 +1244,7 @@ class DistributedStrategy(object):
                         if getattr(self.strategy, f.name):
                             draws += border + "\n"
                             draws += h1_format.format(
-                                "{} = True, please check {}_configs".format(
-                                    f.name, f.name))
+                                "{}=True <-> {}_configs".format(f.name, f.name))
                             draws += line + "\n"
                             my_configs = getattr(self.strategy,
                                                  f.name + "_configs")

python/paddle/distributed/fleet/base/fleet_base.py

@@ -119,6 +119,8 @@ class Fleet(object):
         self.strategy_compiler = None
         self._is_collective = False
         self._runtime_handle = None
+        self._util = None
+        self._context = {}
 
     def init(self, role_maker=None, is_collective=False):
         """
@@ -233,7 +235,7 @@ class Fleet(object):
 
         Returns:
             int: worker numbers
-
+        
         Examples:
             .. code-block:: python
 
@@ -569,8 +571,9 @@ class Fleet(object):
 
         if strategy == None:
             strategy = DistributedStrategy()
-        self.user_defined_strategy = strategy
-        self.valid_strategy = None
+
+        self._user_defined_strategy = copy.deepcopy(strategy)
+        self._context = {}
         return self
 
     @dygraph_only
@@ -909,6 +912,15 @@ class Fleet(object):
         # imitate target optimizer retrieval
         return self.user_defined_optimizer.clear_grad()
 
+    def _final_strategy(self):
+        if "valid_strategy" not in self._context:
+            print(
+                "WARNING: You may need to call minimize function before this function is called"
+            )
+            return {}
+        else:
+            return self._context["valid_strategy"]
+
     def minimize(self,
                  loss,
                  startup_program=None,
@@ -958,12 +970,15 @@ class Fleet(object):
                 # for more examples, please reference https://github.com/PaddlePaddle/FleetX
 
         """
+        context = {}
+        context["user_defined_strategy"] = copy.deepcopy(
+            self._user_defined_strategy)
         if paddle.fluid.framework.in_dygraph_mode():
             # imitate target optimizer retrieval
             target_opt = self.user_defined_optimizer
+            self._context = context
             return target_opt.minimize(loss)
 
-        context = {}
         # cache original feed forward program
         self.origin_main_program = loss.block.program
         context["origin_main_program"] = self.origin_main_program
@@ -984,17 +999,19 @@ class Fleet(object):
             MetaOptimizerFactory()._get_valid_meta_optimizers(
                 self.user_defined_optimizer)
 
-        context["user_defined_strategy"] = copy.copy(self.user_defined_strategy)
+        context["user_defined_strategy"] = copy.deepcopy(
+            self._user_defined_strategy)
+        copy_user_defined_strategy = copy.deepcopy(self._user_defined_strategy)
 
         # trigger the auto-parallel in very strict condition
         # strategy = DistributedStrategy()
         # strategy.auto = True
         # optimizer = paddle.optimizer.SGD(learning_rate=0.1)
         # optimizer = fleet.distributed_optimizer(optimizer, strategy)
-        if self.user_defined_strategy._is_strict_auto():
+        if copy_user_defined_strategy._is_strict_auto():
             # turn on all the strategy for each optimizer
             for opt in distributed_optimizer_list:
-                opt._enable_strategy(self.user_defined_strategy, context)
+                opt._enable_strategy(copy_user_defined_strategy, context)
 
         valid_optimizer_list = []
         valid_graph_optimizer_list = []
@@ -1003,7 +1020,7 @@ class Fleet(object):
         for opt in distributed_optimizer_list:
             opt._set_basic_info(loss, self._role_maker,
                                 self.user_defined_optimizer,
-                                self.user_defined_strategy)
+                                copy_user_defined_strategy)
             if opt._can_apply() and not opt._is_graph_out():
                 valid_optimizer_list.append(opt)
             elif opt._can_apply() and opt._is_graph_out():
@@ -1014,13 +1031,15 @@ class Fleet(object):
         meta_optimizer, graph_optimizer = \
             self.strategy_compiler.generate_optimizer(
                 loss, self._role_maker, self.user_defined_optimizer,
-                self.user_defined_strategy, valid_optimizer_list,
+                copy_user_defined_strategy, valid_optimizer_list,
                 valid_graph_optimizer_list)
 
         valid_strategy = self.strategy_compiler._get_valid_strategy(
-            self.user_defined_strategy, can_not_apply_optimizer_list)
+            copy_user_defined_strategy, can_not_apply_optimizer_list)
+
+        context["valid_strategy"] = copy.deepcopy(valid_strategy)
 
-        context["valid_strategy"] = valid_strategy
+        self._context = context
 
         self.valid_strategy = valid_strategy
         self.valid_strategy._enable_env()

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

@@ -60,8 +60,8 @@ class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
         optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
         optimizer.minimize(avg_cost)
 
-        self.assertTrue(optimizer.user_defined_strategy.a_sync)
-        a_sync_configs = optimizer.user_defined_strategy.a_sync_configs
+        self.assertTrue(fleet._final_strategy().a_sync)
+        a_sync_configs = fleet._final_strategy().a_sync_configs
         self.assertTrue(a_sync_configs['k_steps'] == 0)
 
 

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

@@ -72,8 +72,8 @@ class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
         optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
         optimizer.minimize(avg_cost)
 
-        self.assertTrue(optimizer.user_defined_strategy.a_sync)
-        a_sync_configs = optimizer.user_defined_strategy.a_sync_configs
+        self.assertTrue(fleet._final_strategy().a_sync)
+        a_sync_configs = fleet._final_strategy().a_sync_configs
         self.assertTrue(a_sync_configs['k_steps'] == 0)
 
 

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

@@ -60,8 +60,8 @@ class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
         optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
         optimizer.minimize(avg_cost)
 
-        self.assertTrue(optimizer.user_defined_strategy.a_sync)
-        a_sync_configs = optimizer.user_defined_strategy.a_sync_configs
+        self.assertTrue(fleet._final_strategy().a_sync)
+        a_sync_configs = fleet._final_strategy().a_sync_configs
         self.assertTrue(a_sync_configs['k_steps'] == 800)
 
 

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

@@ -18,6 +18,8 @@ import unittest
 import paddle
 import os
 
+paddle.enable_static()
+
 
 class TestFleetAMPOptimizer(unittest.TestCase):
     def setUp(self):
@@ -55,6 +57,8 @@ class TestFleetAMPOptimizer(unittest.TestCase):
         optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
         optimizer.minimize(avg_cost)
 
+        strategy = fleet._final_strategy()
+
         ops = [op.type for op in avg_cost.block.ops]
         self.assertIn('cast', ops)
         self.assertIn('check_finite_and_unscale', ops)

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

@@ -18,6 +18,8 @@ import os
 import paddle.distributed.fleet as fleet
 import paddle.distributed.fleet.base.role_maker as role_maker
 
+paddle.enable_static()
+
 
 class TestDistributedStrategyAuto(unittest.TestCase):
     def setUp(self):

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

@@ -167,6 +167,8 @@ class TestFleetDygraph(unittest.TestCase):
         state_dict = adam.state_dict()
         adam.set_state_dict(state_dict)
 
+        final_strategy = fleet._final_strategy()
+
 
 class TestFleetBaseSingleRunCollective(unittest.TestCase):
     def setUp(self):

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

@@ -19,6 +19,8 @@ import os
 import paddle.distributed.fleet as fleet
 import paddle.distributed.fleet.base.role_maker as role_maker
 
+paddle.enable_static()
+
 
 class TestFleetLambMetaOptimizer(unittest.TestCase):
     def setUp(self):

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

@@ -19,6 +19,8 @@ import os
 import paddle.distributed.fleet as fleet
 import paddle.distributed.fleet.base.role_maker as role_maker
 
+paddle.enable_static()
+
 
 class TestFleetLarsMetaOptimizer(unittest.TestCase):
     def setUp(self):