update error clip

python/paddle/v2/fluid/backward.py

@@ -278,7 +278,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
                 _infer_var_data_type_(arg, block)
 
 
-def append_backward(loss, parameter_list=None, no_grad_set=None):
+def append_backward(loss, parameter_list=None, no_grad_set=None, callback=None):
     """
     Append backward part to main_program
 
@@ -322,7 +322,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None):
     grad_to_var = dict()
 
     _append_backward_ops_(loss, root_block, root_block, no_grad_dict,
-                          grad_to_var)
+                          grad_to_var, callback)
     _append_backward_vars_(root_block, fwd_op_num, grad_to_var, grad_info_map)
 
     program.current_block_idx = current_block_idx

python/paddle/v2/fluid/clip.py

@@ -2,7 +2,9 @@ import functools
 import layers
 from . import core
 
-__all__ = ['GradientClipByValue', 'append_gradient_clip_ops']
+__all__ = [
+    'GradientClipByValue', 'append_gradient_clip_ops', 'error_clip_callback'
+]
 
 
 class BaseErrorClipAttr(object):

python/paddle/v2/fluid/framework.py

@@ -151,6 +151,7 @@ class Variable(object):
                  stop_gradient=False,
                  **kwargs):
         self.block = block
+        self.error_clip = error_clip
 
         if name is None:
             name = Variable._unique_var_name_()

python/paddle/v2/fluid/optimizer.py

@@ -6,7 +6,7 @@ from framework import unique_name, program_guard
 from initializer import Constant
 from layer_helper import LayerHelper
 from regularizer import append_regularization_ops
-from clip import append_gradient_clip_ops
+from clip import append_gradient_clip_ops, error_clip_callback
 
 __all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
 
@@ -197,7 +197,8 @@ class Optimizer(object):
         This method combines interface `append_backward()` and
         `create_optimization_pass()` into one.
         """
-        params_grads = append_backward(loss, parameter_list, no_grad_set)
+        params_grads = append_backward(loss, parameter_list, no_grad_set,
+                                       error_clip_callback)
 
         params_grads = append_gradient_clip_ops(params_grads)