remove auto mode from localsgd optimizer (#27237)

* rm auto from localsgd

paddle/fluid/framework/distributed_strategy.proto

@@ -36,7 +36,10 @@ message AMPConfig {
   repeated string custom_black_varnames = 9;
 }
 
-message LocalSGDConfig { optional int32 k_steps = 1 [ default = 4 ]; }
+message LocalSGDConfig {
+  optional int32 k_steps = 1 [ default = 1 ];
+  optional int32 begin_step = 2 [ default = 1 ];
+}
 
 message GradientMergeConfig {
   optional int32 k_steps = 1 [ default = 1 ];

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

@@ -707,11 +707,7 @@ class DistributedStrategy(object):
 
         **Notes**:
             k_steps(int) The local steps for training before parameter synchronization. Default 1.
-
-            If strategy.auto is set True, the local steps will be calculated automatically during training.
-            The algorithm is referenced in this paper: 
-            `Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD <https://arxiv.org/pdf/1810.08313.pdf>`_.
-            In this case, k_steps indicates the first local steps which is suggested setting to 1.
+            begin_step(int) The step of begining training by localsgd. Default 1.
 
         Examples:
           .. code-block:: python
@@ -719,7 +715,8 @@ class DistributedStrategy(object):
             import paddle.distributed.fleet as fleet
             strategy = fleet.DistributedStrategy()
             strategy.localsgd = True
-            strategy.localsgd_configs = {"k_steps": 4}
+            strategy.localsgd_configs = {"k_steps": 4,
+                                         "begin_step": 30}
         """
 
         return get_msg_dict(self.strategy.localsgd_configs)

python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py

@@ -49,7 +49,7 @@ class LocalSGDOptimizer(MetaOptimizerBase):
 
     def _enable_strategy(self, dist_strategy, context):
         dist_strategy.localsgd = True
-        dist_strategy.localsgd_configs = {"k_steps": 1}
+        dist_strategy.localsgd_configs = {"k_steps": 1, "begin_step": 1}
 
     def snapshot_name(self, param_name):
         return param_name + self.snapshot_key
@@ -86,8 +86,9 @@ class LocalSGDOptimizer(MetaOptimizerBase):
         minimized = self.inner_opt.minimize(
             loss, startup_program=startup_program)
 
-        init_k_steps = self.user_defined_strategy.localsgd_configs['k_steps']
-        auto_steps = self.user_defined_strategy.auto
+        k_steps_value = self.user_defined_strategy.localsgd_configs['k_steps']
+        begin_step_value = self.user_defined_strategy.localsgd_configs[
+            'begin_step']
 
         if startup_program is None:
             startup_program = default_startup_program()
@@ -101,45 +102,28 @@ class LocalSGDOptimizer(MetaOptimizerBase):
 
         p2s = self.create_snapshot_vars(main_block.program)
         with program_guard(main_block.program, startup_program):
-            step = layers.autoincreased_step_counter(begin=0)
+            step = layers.autoincreased_step_counter(begin=1)
             k_steps = layers.create_global_var(
                 name="k_steps",
                 shape=[1],
-                value=init_k_steps,
+                value=k_steps_value,
                 dtype='int64',
                 persistable=True)
+
+            begin_step = layers.create_global_var(
+                name="begin_step",
+                shape=[1],
+                value=begin_step_value,
+                dtype='int64',
+                persistable=True)
+
             last_step = layers.create_global_var(
                 name="last_step",
                 shape=[1],
-                value=int(0),
+                value=begin_step_value,
                 dtype='int64',
                 persistable=True)
 
-            if auto_steps:
-                avg_loss = layers.collective._c_allreduce(
-                    loss) / self.role_maker.worker_num()
-
-                lr_0 = layers.create_global_var(
-                    name="lr_0",
-                    shape=[1],
-                    value=float(0),
-                    dtype='float32',
-                    persistable=True)
-                loss_0 = layers.create_global_var(
-                    name="loss_0",
-                    shape=[1],
-                    value=float(0),
-                    dtype='float32',
-                    persistable=True)
-
-                global_lr = self.inner_opt._global_learning_rate()
-
-                def initialize():
-                    layers.assign(loss, loss_0)
-                    layers.assign(global_lr, lr_0)
-
-                layers.cond(step == 0, initialize)
-
             def communicate():
                 sub_block = default_main_program().current_block()
                 ring_id = -1
@@ -195,20 +179,10 @@ class LocalSGDOptimizer(MetaOptimizerBase):
                         inputs={'X': [param]},
                         outputs={'Out': [snapshot]},
                         attrs={OP_ROLE_KEY: OpRole.Optimize})
-
-                if auto_steps:
-                    next_local_steps = layers.cast(
-                        layers.ceil(
-                            layers.sqrt(lr_0 * loss / (global_lr * loss_0) *
-                                        float(init_k_steps))),
-                        dtype='int64')
-                    max_local_steps = layers.fill_constant(
-                        shape=[1], dtype='int64', value=16)
-                    next_local_steps = layers.elementwise_min(next_local_steps,
-                                                              max_local_steps)
-                    layers.assign(next_local_steps, k_steps)
                 layers.assign(step, last_step)
 
-            layers.cond(step - last_step == k_steps, communicate)
+            def begin_localsgd():
+                layers.cond(step - last_step == k_steps, communicate)
 
+            layers.cond(step > begin_step, begin_localsgd, communicate)
         return minimized

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

@@ -81,9 +81,10 @@ class TestStrategyConfig(unittest.TestCase):
 
     def test_localsgd_configs(self):
         strategy = paddle.distributed.fleet.DistributedStrategy()
-        configs = {"k_steps": 4}
+        configs = {"k_steps": 4, "begin_step": 120}
         strategy.localsgd_configs = configs
         self.assertEqual(strategy.localsgd_configs["k_steps"], 4)
+        self.assertEqual(strategy.localsgd_configs["begin_step"], 120)
 
     def test_dgc(self):
         strategy = paddle.distributed.fleet.DistributedStrategy()
@@ -230,7 +231,7 @@ class TestStrategyConfig(unittest.TestCase):
         strategy.a_sync = True
         strategy.localsgd = True
         strategy.dgc = True
-        localsgd_configs = {"k_steps": 5}
+        localsgd_configs = {"k_steps": 5, "begin_step": 1}
         strategy.localsgd_configs = localsgd_configs
         build_strategy = paddle.fluid.BuildStrategy()
         build_strategy.enable_sequential_execution = True

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

@@ -44,6 +44,7 @@ class TestFleetLocalSGDMetaOptimizer(unittest.TestCase):
         strategy.auto = True
         config = strategy.localsgd_configs
         config['k_steps'] = 1
+        config['begin_step'] = 1
         strategy.localsgd_configs = config
 
         optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.01)