Merge branch 'develop' of github.com:PaddlePaddle/Paddle into fix_k8s_404

README.md

@@ -61,32 +61,32 @@ Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddl
 ## Installation
 
 It is recommended to check out the
-[Docker installation guide](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html)
+[Docker installation guide](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html)
 before looking into the
-[build from source guide](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html).
+[build from source guide](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/build_from_source_en.html).
 
 ## Documentation
 
-We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
-[Chinese](http://doc.paddlepaddle.org/doc_cn/) documentation.
+We provide [English](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html) and
+[Chinese](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html) documentation.
 
-- [Deep Learning 101](http://book.paddlepaddle.org/index.html)
+- [Deep Learning 101](http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html)
 
   You might want to start from this online interactive book that can run in a Jupyter Notebook.
 
-- [Distributed Training](http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
+- [Distributed Training](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/usage/cluster/cluster_train_en.html)
 
   You can run distributed training jobs on MPI clusters.
 
-- [Distributed Training on Kubernetes](http://doc.paddlepaddle.org/develop/doc/howto/usage/k8s/k8s_en.html)
+- [Distributed Training on Kubernetes](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/usage/cluster/k8s_en.html)
 
    You can also run distributed training jobs on Kubernetes clusters.
 
-- [Python API](http://doc.paddlepaddle.org/develop/doc/api/index_en.html)
+- [Python API](http://www.paddlepaddle.org/docs/develop/documentation/en/api/index_en.html)
 
    Our new API enables much shorter programs.
 
-- [How to Contribute](http://doc.paddlepaddle.org/develop/doc/howto/dev/contribute_to_paddle_en.html)
+- [How to Contribute](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/dev/contribute_to_paddle_en.html)
 
    We appreciate your contributions!
 

doc/api/v2/fluid/layers.rst

@@ -188,12 +188,6 @@ beam_search_decode
     :noindex:
 
 
-lstm
----------
-..  autofunction:: paddle.v2.fluid.layers.lstm
-    :noindex:
-
-
 lod_rank_table
 ---------
 ..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
@@ -300,7 +294,21 @@ conv2d_transpose
 ..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
     :noindex:
 
+
 sequence_expand
 ---------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_expand
     :noindex:
+
+
+lstm_unit
+---------
+..  autofunction:: paddle.v2.fluid.layers.lstm_unit
+    :noindex:
+
+
+sequence_softmax
+---------
+..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
+    :noindex:
+

doc/howto/usage/cluster/cluster_train_cn.md

-# PaddlePaddle分布式训练
+# 分布式训练
 
 
 ## 概述
@@ -181,8 +181,8 @@ PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务
 
 ## 在不同集群中运行
 
-  - [fabric](fabric_cn.md)
-  - [openmpi](openmpi_cn.md)
-  - [kubernetes](k8s_cn.md)
-  - [kubernetes distributed](k8s_distributed_cn.md)
-  - [kubernetes on AWS](k8s_aws_cn.md)
+  - [fabric集群](fabric_cn.md)
+  - [openmpi集群](openmpi_cn.md)
+  - [kubernetes单机](k8s_cn.md)
+  - [kubernetes distributed分布式](k8s_distributed_cn.md)
+  - [AWS上运行kubernetes集群训练](k8s_aws_cn.md)

doc/howto/usage/cluster/cluster_train_en.md

-# PaddlePaddle Distributed Training
+# Distributed Training
 
 ## Introduction
 
@@ -188,5 +188,4 @@ These cluster platforms provide API or environment variables for training proces
   - [fabric](fabric_en.md)
   - [openmpi](openmpi_en.md)
   - [kubernetes](k8s_en.md)
-  - kubernetes distributed
   - [kubernetes on AWS](k8s_aws_en.md)

doc/howto/usage/cluster/k8s_cn.md

 # Kubernetes单机训练
 
-在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。
+在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的PaddlePaddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。
 
 ## 制作Docker镜像
 
@@ -104,7 +104,7 @@ spec:
       restartPolicy: Never
 ```
 
-### 创建Paddle Job
+### 创建PaddlePaddle Job
 
 使用上文创建的yaml文件创建Kubernetes Job，命令为：
 

doc/howto/usage/cluster/k8s_en.md

-# Paddle On Kubernetes
+# PaddlePaddle On Kubernetes
 
->In this article, we will introduce how to run Paddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run Paddle training job on distributed cluster.
+In this article, we will introduce how to run PaddlePaddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run PaddlePaddle training job on distributed cluster.
 
 ## Build Docker Image
 
@@ -76,7 +76,7 @@ $ docker commit quick_start_data mypaddle/paddle:quickstart
 
 ## Use Kubernetes For Training
 
->We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes.
+We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes.
 
 ### Create Yaml Files
 
@@ -108,7 +108,7 @@ spec:
       restartPolicy: Never
 ```
 
-### Start Paddle Job
+### Start PaddlePaddle Job
 
 Using the above yaml file to start the Kubernetes job.
 

paddle/operators/lstm_unit_op.cc

@@ -51,7 +51,10 @@ class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
   LstmUnitOpMaker(framework::OpProto* proto,
                   framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "FC input before the non-linear activation.");
+    AddInput("X",
+             "Lstm unit only applies non-linear activations, please make sure"
+             "that linear tranformation has already been applied to `X`. "
+             "Linear tranformation can be applied by adding a `fc` layer");
     AddInput(
         "C_prev",
         "The cell state tensor of last time-step in the Lstm Unit operator.");

python/paddle/v2/fluid/__init__.py

@@ -16,12 +16,13 @@ import regularizer
 from param_attr import ParamAttr
 from data_feeder import DataFeeder
 from core import LoDTensor, CPUPlace, GPUPlace
+import clip
 
 Tensor = LoDTensor
 __all__ = framework.__all__ + executor.__all__ + [
     'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
     'regularizer', 'LoDTensor', 'CPUPlace', 'GPUPlace', 'Tensor', 'ParamAttr'
-    'DataFeeder'
+    'DataFeeder', 'clip'
 ]
 
 

python/paddle/v2/fluid/clip.py

+import functools
+import layers
+
+__all__ = ['GradientClipByValue', 'append_gradient_clip_ops']
+
+
+class BaseGradientClipAttr(object):
+    def process_context(self, context, p_g):
+        raise NotImplementedError()
+
+    def create_operators(self, param, grad):
+        raise NotImplementedError()
+
+
+class NullGradientClipAttr(BaseGradientClipAttr):
+    def process_context(self, context, p_g):
+        pass
+
+    def create_operators(self, param, grad):
+        return param, grad
+
+
+class GradientClipByValue(BaseGradientClipAttr):
+    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 process_context(self, context, p_g):
+        pass
+
+    def create_operators(self, param, grad):
+        new_grad = layers.clip(x=grad, min=self.min, max=self.max)
+        return param, new_grad
+
+
+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())
+        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_attr.process_context(context=context, p_g=param_grad)
+        create_op_callbacks.append(
+            functools.partial(
+                clip_attr.create_operators, param=p, grad=g))
+
+    return [each_callback() for each_callback in create_op_callbacks]
+
+
+ClipByValue = GradientClipByValue

python/paddle/v2/fluid/framework.py

@@ -704,6 +704,7 @@ class Block(object):
                 trainable=p.trainable,
                 optimize_attr=p.optimize_attr,
                 regularizer=p.regularizer,
+                clip_attr=p.clip_attr,
                 name=v.name)
             self.vars[new_p.name] = new_p
 
@@ -866,6 +867,8 @@ class Parameter(Variable):
 
         self.regularizer = kwargs.get('regularizer', None)
 
+        self.clip_attr = kwargs.get('clip_attr', None)
+
 
 # program is a global instance.
 _main_program_ = Program()

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

@@ -5,12 +5,15 @@ All layers just related to the neural network.
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant
 from ..framework import Variable
+from ..param_attr import ParamAttr
+from tensor import concat
 
 __all__ = [
     'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
     'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
     'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
-    'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand'
+    'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
+    'lstm_unit'
 ]
 
 
@@ -761,7 +764,7 @@ def conv2d_transpose(input,
     return out
 
 
-def sequence_expand(x, y, main_program=None, startup_program=None):
+def sequence_expand(x, y):
     """Sequence Expand Layer. This layer will expand the input variable **x**
     according to LoD information of **y**. And the following examples will
     explain how sequence_expand works:
@@ -805,8 +808,6 @@ def sequence_expand(x, y, main_program=None, startup_program=None):
     Args:
         x (Variable): The input variable which is a Tensor or LoDTensor.
         y (Variable): The input variable which is a LoDTensor.
-        main_program (Program): The main program.
-        startup_program (Program): The startup program.
 
     Returns:
         Variable: The expanded variable which is a LoDTensor.
@@ -826,3 +827,111 @@ def sequence_expand(x, y, main_program=None, startup_program=None):
         type='sequence_expand', inputs={'X': x,
                                         'Y': y}, outputs={'Out': tmp})
     return tmp
+
+
+def lstm_unit(x_t,
+              hidden_t_prev,
+              cell_t_prev,
+              forget_bias=0.0,
+              param_attr=None,
+              bias_attr=None):
+    """Lstm unit layer. The equation of a lstm step is:
+
+        .. math::
+
+            i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
+
+            f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
+
+            c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
+
+            o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
+
+            h_t & = o_t tanh(c_t)
+
+    The inputs of lstm unit includes :math:`x_t`, :math:`h_{t-1}` and
+    :math:`c_{t-1}`. The implementation separates the linear transformation
+    and non-linear transformation apart. Here, we take :math:`i_t` as an
+    example. The linear transformation is applied by calling a `fc` layer and
+    the equation is:
+
+        .. math::
+
+            L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i
+
+    The non-linear transformation is applied by calling `lstm_unit_op` and the
+    equation is:
+
+        .. math::
+
+            i_t = \sigma(L_{i_t})
+
+    This layer has two outputs including :math:`h_t` and :math:`o_t`.
+
+    Args:
+        x_t (Variable): The input value of current step.
+        hidden_t_prev (Variable): The hidden value of lstm unit.
+        cell_t_prev (Variable): The cell value of lstm unit.
+        forget_bias (float): The forget bias of lstm unit.
+        param_attr (ParamAttr): The attributes of parameter weights, used to set
+            initializer, name etc.
+        bias_attr (ParamAttr): The attributes of bias weights, if not False,
+            bias weights will be created and be set to default value.
+
+    Returns:
+        tuple: The hidden value and cell value of lstm unit.
+
+    Raises:
+        ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**\
+                not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev** \
+                and **cell_t_prev** not be the same.
+
+    Examples:
+
+        .. code-block:: python
+
+             x_t = fluid.layers.fc(input=x_t_data, size=10)
+             prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
+             prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+             hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+                                                    hidden_t_prev=prev_hidden,
+                                                    cell_t_prev=prev_cell)
+    """
+    helper = LayerHelper('lstm_unit', **locals())
+
+    if len(x_t.shape) != 2:
+        raise ValueError("Rank of x_t must be 2.")
+
+    if len(hidden_t_prev.shape) != 2:
+        raise ValueError("Rank of hidden_t_prev must be 2.")
+
+    if len(cell_t_prev.shape) != 2:
+        raise ValueError("Rank of cell_t_prev must be 2.")
+
+    if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
+            0] != cell_t_prev.shape[0]:
+        raise ValueError("The 1s dimension of x_t, hidden_t_prev and "
+                         "cell_t_prev must be the same.")
+
+    if bias_attr is None:
+        bias_attr = ParamAttr()
+
+    size = cell_t_prev.shape[1]
+    concat_out = concat(input=[x_t, hidden_t_prev], axis=1)
+    fc_out = fc(input=concat_out,
+                size=4 * size,
+                param_attr=param_attr,
+                bias_attr=bias_attr)
+    dtype = x_t.dtype
+    c = helper.create_tmp_variable(dtype)
+    h = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='lstm_unit',
+        inputs={"X": fc_out,
+                "C_prev": cell_t_prev},
+        outputs={"C": c,
+                 "H": h},
+        attrs={"forget_bias": forget_bias})
+
+    return h, c

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

@@ -2,7 +2,7 @@ from ..registry import register_layer
 __all__ = [
     'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose',
     'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
-    'elementwise_sub', 'elementwise_mul', 'clip', 'abs'
+    'elementwise_sub', 'elementwise_mul', 'clip', 'abs', 'sequence_softmax'
 ]
 
 for _OP in set(__all__):

python/paddle/v2/fluid/optimizer.py

@@ -6,6 +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
 
 __all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
 
@@ -197,9 +198,13 @@ class Optimizer(object):
         `create_optimization_pass()` into one.
         """
         params_grads = append_backward_ops(loss, parameter_list, no_grad_set)
+
+        params_grads = append_gradient_clip_ops(params_grads)
+
         # Add regularization if any
         params_grads = append_regularization_ops(params_grads,
                                                  self.regularization)
+
         optimize_ops = self.create_optimization_pass(params_grads, loss,
                                                      startup_program)
         return optimize_ops

python/paddle/v2/fluid/param_attr.py

 from initializer import Initializer, Xavier, Constant
 from regularizer import WeightDecayRegularizer
 
+__all__ = ['ParamAttr']
+
 
 class ParamAttr(object):
     def __init__(self,
@@ -8,12 +10,14 @@ class ParamAttr(object):
                  initializer=None,
                  learning_rate=1.0,
                  regularizer=None,
-                 trainable=True):
+                 trainable=True,
+                 clip=None):
         self.name = name
         self.initializer = initializer
         self.learning_rate = learning_rate
         self.regularizer = regularizer
         self.trainable = trainable
+        self.clip = clip
 
     def set_default_initializer(self, initializer):
         if initializer is None:
@@ -56,7 +60,8 @@ class ParamAttr(object):
             'name': self.name,
             'learning_rate': self.learning_rate,
             'regularizer': self.regularizer,
-            'trainable': self.trainable
+            'trainable': self.trainable,
+            'clip_attr': self.clip
         }
         if with_initializer:
             kwargs['initializer'] = self.initializer

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

@@ -11,7 +11,9 @@ regularizer = fluid.regularizer.L2Decay(0.0005 * BATCH_SIZE)
 hidden1 = fluid.layers.fc(input=image,
                           size=128,
                           act='relu',
-                          param_attr=regularizer)
+                          param_attr=fluid.ParamAttr(
+                              regularizer=regularizer,
+                              clip=fluid.clip.ClipByValue(10)))
 hidden2 = fluid.layers.fc(input=hidden1,
                           size=64,
                           act='relu',

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

@@ -161,7 +161,7 @@ class TestBook(unittest.TestCase):
                     x=dat, label=lbl))
         print(str(program))
 
-    def test_seq_expand(self):
+    def test_sequence_expand(self):
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[10], dtype='float32')
@@ -170,6 +170,32 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(layers.sequence_expand(x=x, y=y))
         print(str(program))
 
+    def test_lstm_unit(self):
+        program = Program()
+        with program_guard(program):
+            x_t_data = layers.data(
+                name='x_t_data', shape=[10, 10], dtype='float32')
+            x_t = layers.fc(input=x_t_data, size=10)
+            prev_hidden_data = layers.data(
+                name='prev_hidden_data', shape=[10, 20], dtype='float32')
+            prev_hidden = layers.fc(input=prev_hidden_data, size=20)
+            prev_cell_data = layers.data(
+                name='prev_cell', shape=[10, 30], dtype='float32')
+            prev_cell = layers.fc(input=prev_cell_data, size=30)
+            self.assertIsNotNone(
+                layers.lstm_unit(
+                    x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
+        print(str(program))
+
+    def test_sequence_softmax(self):
+        program = Program()
+        with program_guard(program):
+            seq_data = layers.data(
+                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
+            seq = layers.fc(input=seq_data, size=20)
+            self.assertIsNotNone(layers.sequence_softmax(x=seq))
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()