Merge pull request #8934 from chengduoZH/feature/Enhance_regularizer_py

Enhance regularizer.py

Author: chengduo

python/paddle/fluid/regularizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import framework
+from . import core
 
 __all__ = [
     'append_regularization_ops',
@@ -46,9 +47,9 @@ def append_regularization_ops(parameters_and_grads, regularization=None):
         regularization_term = None
         if param.regularizer is not None:
             # Add variable for regularization term in grad block
-            regularization_term = param.regularizer(param, grad.block)
+            regularization_term = param.regularizer(param, grad, grad.block)
         elif regularization is not None:
-            regularization_term = regularization(param, grad.block)
+            regularization_term = regularization(param, grad, grad.block)
 
         # If no gradient or no regularization specified,
         # then we don't need to do anything
@@ -82,7 +83,7 @@ class WeightDecayRegularizer(object):
     def __init__(self):
         pass
 
-    def __call__(self, param, block):
+    def __call__(self, param, grad, block):
         """Add corresponding weight decay operations to the network
         """
         raise NotImplementedError()
@@ -102,7 +103,7 @@ def __init__(self, regularization_coeff=0.0):
         super(L2DecayRegularizer, self).__init__()
         self._regularization_coeff = regularization_coeff
 
-    def __call__(self, param, block):
+    def __call__(self, param, grad, block):
         """Add L2 weight decay ops to network
 
         Adds L2 weight decay ops.
@@ -117,8 +118,23 @@ def __call__(self, param, block):
         """
         assert isinstance(param, framework.Parameter)
         assert isinstance(block, framework.Block)
+
         decay = block.create_var(
             dtype="float32", shape=param.shape, lod_level=param.lod_level)
+
+        if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
+            decay = block.create_var(
+                dtype="float32",
+                shape=param.shape,
+                type=core.VarDesc.VarType.SELECTED_ROWS)
+            block.append_op(
+                type='lookup_table',
+                inputs={'W': param,
+                        'Ids': grad},
+                outputs={'Out': decay},
+                attrs={'is_sparse': True})
+            param = decay
+
         # Append Op to calculate decay
         block.append_op(
             type='scale',
@@ -141,7 +157,7 @@ def __init__(self, regularization_coeff=0.0):
         super(L1DecayRegularizer, self).__init__()
         self._regularization_coeff = regularization_coeff
 
-    def __call__(self, param, block):
+    def __call__(self, param, grad, block):
         """Add L1 weight decay ops to network
 
         Adds L1 weight decay ops.
@@ -158,6 +174,19 @@ def __call__(self, param, block):
         assert isinstance(block, framework.Block)
         decay = block.create_var(
             dtype="float32", shape=param.shape, lod_level=param.lod_level)
+
+        if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
+            decay = block.create_var(
+                dtype="float32",
+                shape=param.shape,
+                type=core.VarDesc.VarType.SELECTED_ROWS)
+            block.append_op(
+                type='lookup_table',
+                inputs={'W': param,
+                        'Ids': grad},
+                outputs={'Out': decay},
+                attrs={'is_sparse': True})
+
         # Append sign op
         block.append_op(
             type='sign', inputs={"X": param}, outputs={"Out": decay})

python/paddle/fluid/tests/book/test_machine_translation.py

@@ -181,7 +181,10 @@ def train_main(use_cuda, is_sparse, is_local=True):
     cost = pd.cross_entropy(input=rnn_out, label=label)
     avg_cost = pd.mean(cost)
 
-    optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
+    optimizer = fluid.optimizer.Adagrad(
+        learning_rate=1e-4,
+        regularization=fluid.regularizer.L2DecayRegularizer(
+            regularization_coeff=0.1))
     optimize_ops, params_grads = optimizer.minimize(avg_cost)
 
     train_data = paddle.batch(