Merge pull request #12463 from jacquesqiao/remove-duplicated-init-op

avoid duplicated init op for one parameter

Author: jacquesqiao

python/paddle/fluid/framework.py

@@ -1038,7 +1038,26 @@ def create_parameter(self, *args, **kwargs):
         global_block = self.program.global_block()
         param = Parameter(global_block, *args, **kwargs)
         if 'initializer' in kwargs:
-            kwargs['initializer'](param, self)
+
+            def _is_inited_by(block, var):
+                init_ops = []
+                for op in block.ops:
+                    if var.name in op.output_arg_names:
+                        init_ops.append(op)
+                return init_ops
+
+            initializer = kwargs['initializer']
+            init_ops = _is_inited_by(global_block, param)
+            init_ops_len = len(init_ops)
+            if init_ops_len > 1:
+                raise RuntimeError("param " + param.name +
+                                   " is inited by multiple init ops " + str(
+                                       init_ops))
+            elif init_ops_len == 1:
+                #TODO already inited, do nothing, should log a warning
+                pass
+            else:
+                initializer(param, self)
         return param
 
     def append_op(self, *args, **kwargs):

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

@@ -73,9 +73,18 @@ def _transpiler_instance(self, config=None):
 
         return self.transpiler
 
+    def transpiler_test_impl(self):
+        pass
 
-class TestBasicModel(TranspilerTest):
     def test_transpiler(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            self.transpiler_test_impl()
+
+
+class TestBasicModel(TranspilerTest):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         pserver2, startup2 = self.get_pserver(self.pserver2_ep)
 
@@ -123,7 +132,7 @@ def test_transpiler(self):
 
 
 class TestBasicModelWithLargeBlockSize(TranspilerTest):
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         config = fluid.DistributeTranspilerConfig()
         config.min_block_size = 1048576
 
@@ -148,7 +157,7 @@ def test_transpiler(self):
                          ["sum", "scale", "sgd"])
         # confirm startup program
         self.assertEqual([op.type for op in startup.global_block().ops],
-                         ["fill_constant", "fill_constant", "fill_constant"])
+                         ["fill_constant", "fill_constant"])
         # the variable #fc_w will be split into two blocks
         fc_w_var = startup2.global_block().var("fc_w")
         self.assertEqual(fc_w_var.shape, (1000L, 1000L))
@@ -177,7 +186,7 @@ class TestNoSliceVar(TranspilerTest):
     def setUp(self):
         super(TestNoSliceVar, self).setUp()
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         config = fluid.DistributeTranspilerConfig()
         config.slice_var_up = False
 
@@ -212,7 +221,7 @@ def net_conf(self):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -242,7 +251,7 @@ def net_conf(self):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -291,7 +300,7 @@ def net_conf(self):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()
 
@@ -326,7 +335,7 @@ def net_conf(self):
         sgd_optimizer.minimize(avg_cost)
         return
 
-    def test_transpiler(self):
+    def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         trainer = self.get_trainer()