From 1dac173b518faeb8f31c321a61fa287b8de4246e Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 17 Jan 2018 20:15:03 +0800
Subject: [PATCH] add API for clip_by_global_norm

---
 python/paddle/v2/fluid/clip.py | 23 ++++++++++++++++++++---
 1 file changed, 20 insertions(+), 3 deletions(-)

diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index f7917fc142..d1e6987e01 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 import functools
 import layers
-from framework import Variable
+import framework
 from . import core
 
 __all__ = [
@@ -128,8 +128,8 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
 
     @classmethod
     def check_init(cls):
-        if not (isinstance(cls.global_norm_var, Variable) and
-                isinstance(cls.clip_norm_var, Variable)):
+        if not (isinstance(cls.global_norm_var, framework.Variable) and
+                isinstance(cls.clip_norm_var, framework.Variable)):
             raise ValueError(
                 "Class 'GradientClipByGlobalNorm' has not been properly initialized. \
                  Please call GradientClipByGlobalNorm.init() first.")
@@ -158,6 +158,23 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         return param, new_grad
 
 
+def gradient_clip_by_global_norm(clip_norm, param_list=None, program=None):
+    if program is None:
+        program = framework.default_main_program()
+    if param_list is None:
+        param_list = program.block(0).all_parameters()
+    if all(isinstance(elem, basestring) for elem in param_list):
+        param_list = [program.block(0).var(elem) for elem in param_list]
+    if not all(isinstance(elem, framework.Parameter) for elem in param_list):
+        raise TypeError(
+            "'param_list' should be a list of Parameter or basestring(parameter's name)."
+        )
+
+    GradientClipByGlobalNorm.init(clip_norm)
+    for param in param_list:
+        param.gradient_clip_attr = GradientClipByGlobalNorm()
+
+
 def append_gradient_clip_ops(param_grad):
     context = dict()
     create_op_callbacks = []
-- 
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