Merge pull request #8545 from jacquesqiao/fix-optimize-multi-program

create learning rate for multi program

python/paddle/fluid/optimizer.py

@@ -36,10 +36,18 @@ class Optimizer(object):
     """
 
     def __init__(self, learning_rate, global_step=None, regularization=None):
-        assert learning_rate is not None
+        if not isinstance(learning_rate, float) and \
+                not isinstance(learning_rate, framework.Variable):
+            raise TypeError("learning rate should be float or Variable")
         self._global_step = global_step
         self.regularization = regularization
-        self._global_learning_rate = learning_rate
+        self._learning_rate = learning_rate
+        # each program should have a independent learning rate
+        # program -> Variable(learning_rate)
+        self._learning_rate_map = dict()
+        if isinstance(self._learning_rate, framework.Variable):
+            self._learning_rate_map[framework.default_main_program(
+            )] = self._learning_rate
         # Dictionary of accumulators. Some optimizer subclasses need to
         # allocate and manage extra variables associated with the parameters
         # to train. These variables are called accumulators.
@@ -48,26 +56,33 @@ class Optimizer(object):
         self.helper = None
 
     def _create_global_learning_rate(self):
-        if isinstance(self._global_learning_rate, float):
-            self._global_learning_rate = layers.create_global_var(
+        lr = self.global_learning_rate()
+
+        if isinstance(lr, framework.Variable):
+            return
+        else:
+            if not isinstance(self._learning_rate, float):
+                raise TypeError(
+                    "learning rate variable is create outside optimizer,"
+                    "can not create new learning rate variable for new program")
+
+        # create learning rate in the current main program
+        self._learning_rate_map[framework.default_main_program(
+        )] = layers.create_global_var(
             name=unique_name.generate("learning_rate"),
             shape=[1],
-                value=float(self._global_learning_rate),
+            value=float(self._learning_rate),
             dtype='float32',
             persistable=True)
 
-        if not isinstance(self._global_learning_rate, framework.Variable):
-            raise ValueError("learning rate should be a Variable, "
-                             "actual type is %s",
-                             type(self._global_learning_rate))
-
-    @property
-    def global_learning_rate(self):
+    def global_learning_rate(self, program=None):
         """
         get global decayed learning rate
         :return:
         """
-        return self._global_learning_rate
+        if program is None:
+            program = framework.default_main_program()
+        return self._learning_rate_map.get(program, None)
 
     def _append_optimize_op(self, block, param_and_grad):
         """ append optimize operator to block and return all the added optimize_op
@@ -78,7 +93,7 @@ class Optimizer(object):
         # create learning rate variable for every parameter
         param = param_and_grad[0]
         param_lr = param.optimize_attr['learning_rate']
-        return self._global_learning_rate * param_lr
+        return self.global_learning_rate() * param_lr
 
     def _create_accumulators(self, block, parameters):
         """Create all accumulators needed by the parameters