Gradient clip by global norm

python/paddle/v2/fluid/clip.py

@@ -14,6 +14,7 @@
 
 import functools
 import layers
+import framework
 from . import core
 
 __all__ = [
@@ -66,15 +67,15 @@ def error_clip_callback(block, context):
 
 
 class BaseGradientClipAttr(object):
-    def process_context(self, context, p_g):
+    def process_context(self, context, param, grad):
         raise NotImplementedError()
 
     def create_operators(self, param, grad):
         raise NotImplementedError()
 
 
 class NullGradientClipAttr(BaseGradientClipAttr):
-    def process_context(self, context, p_g):
+    def process_context(self, context, param, grad):
         pass
 
     def create_operators(self, param, grad):
@@ -91,27 +92,101 @@ def __init__(self, max, min=None):
         self.max = max
         self.min = min
 
-    def process_context(self, context, p_g):
+    def process_context(self, context, param, grad):
         pass
 
     def create_operators(self, param, grad):
         new_grad = layers.clip(x=grad, min=self.min, max=self.max)
         return param, new_grad
 
 
+class GradientClipByNorm(BaseGradientClipAttr):
+    def __init__(self, clip_norm):
+        self.clip_norm = clip_norm
+
+    def process_context(self, context, param, grad):
+        pass
+
+    def create_operators(self, param, grad):
+        new_grad = layers.clip_by_norm(x=grad, max_norm=self.clip_norm)
+        return param, new_grad
+
+
+class GradientClipByGlobalNorm(BaseGradientClipAttr):
+    def __init__(self, clip_norm, group_name="default_group"):
+        if not isinstance(group_name, basestring):
+            raise TypeError("'group_name' must be a basestring.")
+
+        self.clip_norm = clip_norm
+        self.group_name = group_name
+
+    def process_context(self, context, param, grad):
+        if self.group_name not in context:
+            context[self.group_name] = []
+            context[self.group_name + "_clip_value"] = self.clip_norm
+            context[self.group_name + "_clip"] = layers.fill_constant(
+                shape=[1], dtype="float32", value=self.clip_norm)
+        else:
+            if not self.clip_norm == context[self.group_name + "_clip_value"]:
+                raise ValueError(
+                    "All parameters' 'clip_norm' of a same group should be the same"
+                )
+
+        local_norm_var = layers.reduce_sum(input=layers.pow(x=grad, factor=2.0))
+        context[self.group_name].append(local_norm_var)
+
+        self.context = context
+
+    def create_operators(self, param, grad):
+        group_scale_name = self.group_name + "_scale"
+        if group_scale_name not in self.context:
+            group_norm_var = layers.sums(input=self.context[self.group_name])
+            layers.sqrt(x=group_norm_var, out=group_norm_var)
+            clip_var = self.context[self.group_name + "_clip"]
+            group_scale_var = layers.elementwise_div(
+                x=clip_var,
+                y=layers.elementwise_max(
+                    x=clip_var, y=group_norm_var))
+            assert group_scale_var.shape == (1L, )
+            self.context[group_scale_name] = group_scale_var
+
+        new_grad = layers.elementwise_mul(
+            x=grad, y=self.context[group_scale_name])
+        return param, new_grad
+
+
+def gradient_clip_by_global_norm(clip_norm,
+                                 param_list=None,
+                                 group_name="default_group",
+                                 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)."
+        )
+
+    for param in param_list:
+        param.gradient_clip_attr = GradientClipByGlobalNorm(clip_norm,
+                                                            group_name)
+
+
 def append_gradient_clip_ops(param_grad):
     context = dict()
     create_op_callbacks = []
     for p, g in param_grad:
-        clip_attr = getattr(p, 'clip_attr', NullGradientClipAttr())
+        clip_attr = getattr(p, 'gradient_clip_attr', NullGradientClipAttr())
         if clip_attr is None:
             clip_attr = NullGradientClipAttr()
         if not isinstance(clip_attr, BaseGradientClipAttr):
             raise TypeError(
-                "clip attribute should be an instance of BaseGradientClippingAttr"
-            )
+                "clip attribute should be an instance of BaseGradientClipAttr")
 
-        clip_attr.process_context(context=context, p_g=param_grad)
+        clip_attr.process_context(context=context, param=p, grad=g)
         create_op_callbacks.append(
             functools.partial(
                 clip_attr.create_operators, param=p, grad=g))

python/paddle/v2/fluid/framework.py

@@ -780,7 +780,7 @@ def copy_param_info_from(self, other):
                 trainable=p.trainable,
                 optimize_attr=p.optimize_attr,
                 regularizer=p.regularizer,
-                clip_attr=p.clip_attr,
+                gradient_clip_attr=p.gradient_clip_attr,
                 error_clip=p.error_clip,
                 name=v.name)
             self.vars[new_p.name] = new_p
@@ -948,7 +948,7 @@ def __init__(self, block, shape, dtype, **kwargs):
 
         self.regularizer = kwargs.get('regularizer', None)
 
-        self.clip_attr = kwargs.get('clip_attr', None)
+        self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
 
 
 # program is a global instance.

python/paddle/v2/fluid/layers/ops.py

@@ -46,20 +46,10 @@
 ]
 
 __all__ = [
-    'mean',
-    'mul',
-    'reshape',
-    'scale',
-    'transpose',
-    'sigmoid_cross_entropy_with_logits',
-    'elementwise_add',
-    'elementwise_div',
-    'elementwise_sub',
-    'elementwise_mul',
-    'elementwise_max',
-    'elementwise_min',
-    'clip',
-    'sequence_softmax',
+    'mean', 'mul', 'reshape', 'scale', 'transpose',
+    'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
+    'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
+    'clip', 'clip_by_norm', 'sequence_softmax'
 ] + __activations__
 
 for _OP in set(__all__):

python/paddle/v2/fluid/param_attr.py

@@ -25,13 +25,13 @@ def __init__(self,
                  learning_rate=1.0,
                  regularizer=None,
                  trainable=True,
-                 clip=None):
+                 gradient_clip=None):
         self.name = name
         self.initializer = initializer
         self.learning_rate = learning_rate
         self.regularizer = regularizer
         self.trainable = trainable
-        self.clip = clip
+        self.gradient_clip = gradient_clip
 
     def set_default_initializer(self, initializer):
         if initializer is None:
@@ -77,7 +77,7 @@ def to_kwargs(self, with_initializer=False):
             },
             'regularizer': self.regularizer,
             'trainable': self.trainable,
-            'clip_attr': self.clip
+            'gradient_clip_attr': self.gradient_clip
         }
         if with_initializer:
             kwargs['initializer'] = self.initializer

python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py

@@ -27,7 +27,7 @@
                           act='relu',
                           param_attr=fluid.ParamAttr(
                               regularizer=regularizer,
-                              clip=fluid.clip.ClipByValue(10)))
+                              gradient_clip=fluid.clip.ClipByValue(10)))
 
 hidden2 = fluid.layers.fc(input=hidden1,
                           size=64,

python/paddle/v2/fluid/tests/test_gradient_clip.py

@@ -0,0 +1,81 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+BATCH_SIZE = 128
+CLIP = 1
+
+prog = fluid.framework.Program()
+with fluid.program_guard(main_program=prog):
+    image = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
+    hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
+    hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
+    predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
+
+    label = fluid.layers.data(name='y', shape=[1], dtype='int64')
+
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+prog_clip = prog.clone()
+
+avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
+
+p_g = fluid.backward.append_backward(loss=avg_cost)
+p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
+
+with fluid.program_guard(main_program=prog_clip):
+    fluid.clip.gradient_clip_by_global_norm(clip_norm=CLIP)
+    p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
+
+grad_list = [elem[1] for elem in p_g]
+grad_clip_list = [elem[1] for elem in p_g_clip]
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=8192),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
+exe.run(fluid.default_startup_program())
+
+count = 0
+for data in train_reader():
+    count += 1
+    if count > 5:
+        break
+    out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
+    out_clip = exe.run(prog_clip,
+                       feed=feeder.feed(data),
+                       fetch_list=grad_clip_list)
+    global_norm = 0
+    for v in out[1:]:
+        global_norm += np.sum(np.power(v, 2))
+    global_norm = np.sqrt(global_norm)
+
+    global_norm_clip = 0
+    for v in out_clip[1:]:
+        global_norm_clip += np.sum(np.power(v, 2))
+    global_norm_clip = np.sqrt(global_norm_clip)
+
+    if not np.isclose(
+            a=global_norm_clip, b=np.minimum(global_norm, CLIP), rtol=5e-3):
+        exit(1)
+exit(0)