fix slow var initialize, test=develop (#26516)

python/paddle/fluid/optimizer.py

@@ -1141,7 +1141,7 @@ class MomentumOptimizer(Optimizer):
 
 class DGCMomentumOptimizer(Optimizer):
     """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
     DGC (Deep Gradient Compression) Momentum Optimizer. Original paper is https://arxiv.org/abs/1712.01887
 
@@ -3067,7 +3067,7 @@ Lamb = LambOptimizer
 
 class ModelAverage(Optimizer):
     """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
     The ModelAverage optimizer accumulates specific continuous historical parameters
     during training. The accumulated historical range can be controlled by the passed
@@ -3376,7 +3376,7 @@ class ModelAverage(Optimizer):
 
 class ExponentialMovingAverage(object):
     """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
     Compute the moving average of parameters with exponential decay.
     Given a parameter :math:`\\theta`, its exponential moving average (EMA)
@@ -3626,7 +3626,7 @@ class ExponentialMovingAverage(object):
 
 class PipelineOptimizer(object):
     """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
     Pipeline Optimizer: Make a program to run as pipeline, that is splitting a
     program into multiple sections (sub-programs) and each section run on a
@@ -4477,7 +4477,7 @@ class PipelineOptimizer(object):
 
 class RecomputeOptimizer(Optimizer):
     """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
     Recompute Optimizer Wrapper
 
@@ -4562,7 +4562,7 @@ class RecomputeOptimizer(Optimizer):
 
     def load(self, stat_dict):
         """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
         load function is not supported by Recompute Optimizer for now.
         :return: None
@@ -4786,7 +4786,7 @@ class RecomputeOptimizer(Optimizer):
 
 class LookaheadOptimizer(object):
     """
-	:api_attr: Static Graph
+	:api_attr: Static Graph
 
     This implements the Lookahead optimizer of the
     paper : https://arxiv.org/abs/1907.08610.
@@ -4929,6 +4929,11 @@ class LookaheadOptimizer(object):
 
             mod = layers.elementwise_mod(step, k)
             with layers.control_flow.Switch() as switch:
+                with switch.case(step == one_var):
+                    for param_name in params:
+                        fast_var = main_block.var(param_name)
+                        slow_var = param_to_slow[param_name]
+                        layers.assign(input=fast_var, output=slow_var)
                 with switch.case(mod == zero_var):
                     for param_name in params:
                         fast_var = main_block.var(param_name)