Fix dgc param regularizer, test=release/1.7 (#22903)

python/paddle/fluid/optimizer.py

@@ -1121,21 +1121,23 @@ class DGCMomentumOptimizer(Optimizer):
             self._num_trainers = num_trainers
             self._clip_norm = local_grad_clip_norm * (num_trainers**-0.5)
 
-        self._get_dgc_regularization_param()
+        self.regular_type, self.regular_coeff = self._get_regularization_param(
+            self.regularization)
 
-    def _get_dgc_regularization_param(self):
-        self.regular_coeff = 0.0
-        self.regular_type = 0
+    def _get_regularization_param(self, regularization):
+        regular_type = 0
+        regular_coeff = 0.0
 
-        if self.regularization is not None:
-            self.regular_coeff = self.regularization._regularization_coeff
+        if regularization is not None:
+            regular_coeff = regularization._regularization_coeff
             from .regularizer import L1Decay, L2Decay
-            if isinstance(self.regularization, L1Decay):
-                self.regular_type = 1
-            elif isinstance(self.regularization, L2Decay):
-                self.regular_type = 2
+            if isinstance(regularization, L1Decay):
+                regular_type = 1
+            elif isinstance(regularization, L2Decay):
+                regular_type = 2
             else:
                 assert False, 'regularization must be None|L1Decay|L2Deacy'
+        return regular_type, regular_coeff
 
     def _is_use_dgc(self, param_var, grad_var):
         var_numel = abs(reduce(lambda x, y: x * y, param_var.shape))
@@ -1336,6 +1338,13 @@ class DGCMomentumOptimizer(Optimizer):
         block = framework.default_main_program().global_block()
         op_maker = core.op_proto_and_checker_maker
 
+        regular_type = self.regular_type
+        regular_coeff = self.regular_coeff
+        # The regularizer of the Parameters have higher priority
+        if param_var.regularizer is not None:
+            regular_type, regular_coeff = self._get_regularization_param(
+                param_var.regularizer)
+
         dgc_op = block.append_op(
             type="dgc",
             inputs={
@@ -1360,8 +1369,8 @@ class DGCMomentumOptimizer(Optimizer):
                 "use_nesterov": self._use_nesterov,
                 "rampup_begin_step": float(self._rampup_begin_step),
                 "rampup_step": float(self._rampup_step),
-                "regular_coeff": float(self.regular_coeff),
-                "regular_type": int(self.regular_type),
+                "regular_coeff": float(regular_coeff),
+                "regular_type": int(regular_type),
             },
             stop_gradient=True)
 

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

@@ -44,7 +44,9 @@ class TestDGCMomentumOptimizer(unittest.TestCase):
             shape=[dims[0], dims[1]],
             lod_level=0,
             name="mul.x",
-            optimize_attr={'learning_rate': 1.1})
+            optimize_attr={'learning_rate': 1.1},
+            regularizer=None if regularization is not None else
+            regularizer.L2DecayRegularizer(2e-4))
         mul_y = block.create_var(
             dtype="float32",
             shape=[dims[1], dims[2]],
@@ -102,6 +104,14 @@ class TestDGCMomentumOptimizer(unittest.TestCase):
         self.assertEqual(init_ops[0].type, "fill_constant")
         self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
 
+        # check dgc op regularization coeff
+        train_ops = program.global_block().ops
+        for op in train_ops:
+            if op.type == "dgc":
+                coeff = 2e-4 if regularization is None else 1e-4
+                self.assertAlmostEqual(op.attr('regular_coeff'), coeff)
+                print("dgc regular_coeff=" + str(coeff))
+
         with open("test_dgc_optimizer_" + name + ".log", "w") as f:
             program_to_code(program, fout=f)
 
@@ -116,6 +126,10 @@ class TestDGCMomentumOptimizer(unittest.TestCase):
             name="dgc_momentum",
             regularization=regularizer.L2Decay(1e-4))
 
+        # check param.regularizer in dgc
+        self.check_dgc_momentum_optimizer(
+            dims=[16, 1024, 8], name="dgc_momentum")
+
 
 if __name__ == '__main__':
     unittest.main()