Add doc for error clip

doc/design/error_clip.md

+# Error Clip
+
+## Overview
+
+Error clip is widely used in model training to prevent gradient exploding. It takes a value as clip threshold. With error clip, all gradient values will be checked before they are taken by the next `grad_op`, and values greater than the threshold will be clipped.
+
+## Usage
+
+Users can enable clip and set related attributes via invoking `Optimizer`'s `minimize` API:
+
+```python
+def minimize(self,
+             loss,
+             startup_program=None,
+             parameter_list=None,
+             no_grad_set=None,
+             error_clip=None):
+    # ...
+```
+
+The default value of `error_clip` is `None`, which means no error clip is employed. When it's not `None`, it should take an object of `BaseErrorClipAttr`'s derived class. So far, `BaseErrorClipAttr` has only one derived class: `ErrorClipByValue`, whose constructor is:
+
+```python
+ErrorClipByValue(max, min=None)
+```
+
+`max` and `min` represent the maximal and minimal clip threshold respectively. When the `min` is None, the minimal threshold will be assigned with `-max`.
+
+So we can enable the error clip with threshold `[-5.0, 5.0]` by:
+
+```python
+opt = fluid.optimizer.SGD(learning_rate=0.001)
+opt.minimize(loss=avg_cost, error_clip=ErrorClipByValue(max=5.0))
+```
+
+## Implementation
+
+The `BaseErrorClipAttr` and its derived class `ErrorClipByValue` are defined in *clip.py*.
+
+```python
+class BaseErrorClipAttr(object):
+    def create_clip_op_desc(self, grad_name):
+        raise NotImplementedError()
+
+    def prepend_clip_op_desc(self, op_descs):
+        grad_names = set()
+        for op_desc in op_descs:
+            grad_names.update(filter(lambda n: n.find(
+                core.grad_var_suffix()) != -1, op_desc.output_arg_names()))
+        for n in grad_names:
+            op_descs.append(self.create_clip_op_desc(grad_name=n))
+
+
+class ErrorClipByValue(BaseErrorClipAttr):
+    def __init__(self, max, min=None):
+        max = float(max)
+        if min is None:
+            min = -max
+        else:
+            min = float(min)
+        self.max = max
+        self.min = min
+
+    def create_clip_op_desc(self, grad_name):
+        desc = core.OpDesc()
+        desc.set_type("clip")
+        desc.set_input("X", grad_name)
+        desc.set_output("Out", grad_name)
+        desc.set_attr("min", self.min)
+        desc.set_attr("max", self.max)
+        return desc
+```
+
+The `BaseErrorClipAttr` have two main member functions:
+
+- **`create_clip_op_desc(self, grad_name)`**
+
+> This function is used to create a C++ `OpDesc` object of `clip_op` and return its pointer to Python. For different error clips require different `clip_op`, the function is defined as virtual in the base class. All derived classes must implement their own versions of this function.
+
+- **`prepend_clip_op_desc(self, op_descs)`**
+
+> This function takes a list of C++ `OpDesc` as input. It checks each `OpDesc` in the list, creates `clip_op`s for every gradient outputs and then appends them to the input list. The input `op_descs` is supposed to be the backward of a certain forward op. It can contain one or more `OpDesc`s (Some op's backward is a combination of several other ops). 
+
+This two functions take effort during the backward building. Just as we showed in the *Usage* section, `Optimizer`'s `minimize` function can take an object of `ErrorClipByValue`(or some other `BaseErrorClipAttr`'s derived class). Inside the `minimize` function, the `prepend_clip_op_desc` function will be send to backward building process as an callback function:
+
+```python
+params_grads = append_backward(loss=loss, 
+                               parameter_list=parameter_list,
+                               no_grad_set=no_grad_set,
+                               callback=error_clip.prepend_clip_op_desc)
+```
+
+Each time we get the backward of a forward op, we invoke the callback function to append `clip_op` for all the new generated gradients(In the `_append_backward_ops_` function of *backward.py*):
+
+```python
+grad_op_desc, op_grad_to_var = core.get_grad_op_desc(
+            op.desc, no_grad_dict[block.idx], grad_sub_block_list)
+if callback is not None:
+    grad_op_desc = callback(grad_op_desc)
+```

python/paddle/v2/fluid/clip.py

 import functools
 import layers
+from . import core
 
 __all__ = ['GradientClipByValue', 'append_gradient_clip_ops']
 
 
+class BaseErrorClipAttr(object):
+    def create_clip_op_desc(self, grad_name):
+        raise NotImplementedError()
+
+    def prepend_clip_op_desc(self, op_descs):
+        grad_names = set()
+        for op_desc in op_descs:
+            grad_names.update(
+                filter(lambda n: n.find(core.grad_var_suffix()) != -1,
+                       op_desc.output_arg_names()))
+        for n in grad_names:
+            op_descs.append(self.create_clip_op_desc(grad_name=n))
+
+
+class ErrorClipByValue(BaseErrorClipAttr):
+    def __init__(self, max, min=None):
+        max = float(max)
+        if min is None:
+            min = -max
+        else:
+            min = float(min)
+        self.max = max
+        self.min = min
+
+    def create_clip_op_desc(self, grad_name):
+        desc = core.OpDesc()
+        desc.set_type("clip")
+        desc.set_input("X", grad_name)
+        desc.set_output("Out", grad_name)
+        desc.set_attr("min", self.min)
+        desc.set_attr("max", self.max)
+        return desc
+
+
 class BaseGradientClipAttr(object):
     def process_context(self, context, p_g):
         raise NotImplementedError()