diff --git a/adversarial/README.md b/adversarial/README.md new file mode 100644 index 0000000000000000000000000000000000000000..51da21918a9d6e2192a2e03eabef4fde97896bc5 --- /dev/null +++ b/adversarial/README.md @@ -0,0 +1,9 @@ +# Advbox + +Advbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python and paddle. + +## How to use + +1. train a model and save it's parameters. (like fluid_mnist.py) +2. load the parameters which is trained in step1, then reconstruct the model.(like mnist_tutorial_fgsm.py) +3. use advbox to generate the adversarial sample. diff --git a/adversarial/advbox/__init__.py b/adversarial/advbox/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f56f14f18dafdfe1e712cea178a63f09a087b587 --- /dev/null +++ b/adversarial/advbox/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved +# +# 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. +""" + A set of tools for generating adversarial example on paddle platform +""" diff --git a/adversarial/advbox/attacks/base.py b/adversarial/advbox/attacks/base.py new file mode 100644 index 0000000000000000000000000000000000000000..98a65f2fddff999ac6fa98a5733128a63a60f916 --- /dev/null +++ b/adversarial/advbox/attacks/base.py @@ -0,0 +1,39 @@ +""" +The base model of the model. +""" +from abc import ABCMeta, abstractmethod + + +class Attack(object): + """ + Abstract base class for adversarial attacks. `Attack` represent an adversarial attack + which search an adversarial example. subclass should implement the _apply() method. + + Args: + model(Model): an instance of the class advbox.base.Model. + + """ + __metaclass__ = ABCMeta + + def __init__(self, model): + self.model = model + + def __call__(self, image_label): + """ + Generate the adversarial sample. + + Args: + image_label(list): The image and label tuple list with one element. + """ + adv_img = self._apply(image_label) + return adv_img + + @abstractmethod + def _apply(self, image_label): + """ + Search an adversarial example. + + Args: + image_batch(list): The image and label tuple list with one element. + """ + raise NotImplementedError diff --git a/adversarial/advbox/attacks/gradientsign.py b/adversarial/advbox/attacks/gradientsign.py new file mode 100644 index 0000000000000000000000000000000000000000..15b1d176cb11330ac290d73aec1419a3d8f3cc4c --- /dev/null +++ b/adversarial/advbox/attacks/gradientsign.py @@ -0,0 +1,38 @@ +""" +This module provide the attack method for FGSM's implement. +""" +from __future__ import division +import numpy as np +from collections import Iterable +from .base import Attack + + +class GradientSignAttack(Attack): + """ + This attack was originally implemented by Goodfellow et al. (2015) with the + infinity norm (and is known as the "Fast Gradient Sign Method"). This is therefore called + the Fast Gradient Method. + Paper link: https://arxiv.org/abs/1412.6572 + """ + + def _apply(self, image_label, epsilons=1000): + assert len(image_label) == 1 + pre_label = np.argmax(self.model.predict(image_label)) + + min_, max_ = self.model.bounds() + gradient = self.model.gradient(image_label) + gradient_sign = np.sign(gradient) * (max_ - min_) + + if not isinstance(epsilons, Iterable): + epsilons = np.linspace(0, 1, num=epsilons + 1) + + for epsilon in epsilons: + adv_img = image_label[0][0].reshape( + gradient_sign.shape) + epsilon * gradient_sign + adv_img = np.clip(adv_img, min_, max_) + adv_label = np.argmax(self.model.predict([(adv_img, 0)])) + if pre_label != adv_label: + return adv_img + + +FGSM = GradientSignAttack diff --git a/adversarial/advbox/models/__init__.py b/adversarial/advbox/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eee0f6efd4774b42fcd082eb06d1398d2ee51bc4 --- /dev/null +++ b/adversarial/advbox/models/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved +# +# 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. +""" +Paddle model for target of attack +""" diff --git a/adversarial/advbox/models/base.py b/adversarial/advbox/models/base.py new file mode 100644 index 0000000000000000000000000000000000000000..74e1045def7648b4a8df30e89312d73c0d4fe7e1 --- /dev/null +++ b/adversarial/advbox/models/base.py @@ -0,0 +1,90 @@ +""" +The base model of the model. +""" +from abc import ABCMeta +import abc + +abstractmethod = abc.abstractmethod + + +class Model(object): + """ + Base class of model to provide attack. + + + Args: + bounds(tuple): The lower and upper bound for the image pixel. + channel_axis(int): The index of the axis that represents the color channel. + preprocess(tuple): Two element tuple used to preprocess the input. First + substract the first element, then divide the second element. + """ + __metaclass__ = ABCMeta + + def __init__(self, bounds, channel_axis, preprocess=None): + assert len(bounds) == 2 + assert channel_axis in [0, 1, 2, 3] + + if preprocess is None: + preprocess = (0, 1) + self._bounds = bounds + self._channel_axis = channel_axis + self._preprocess = preprocess + + def bounds(self): + """ + Return the upper and lower bounds of the model. + """ + return self._bounds + + def channel_axis(self): + """ + Return the channel axis of the model. + """ + return self._channel_axis + + def _process_input(self, input_): + res = input_ + sub, div = self._preprocess + if sub != 0: + res = input_ - sub + assert div != 0 + if div != 1: + res /= div + return res + + @abstractmethod + def predict(self, image_batch): + """ + Calculate the prediction of the image batch. + + Args: + image_batch(numpy.ndarray): image batch of shape (batch_size, height, width, channels). + + Return: + numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes). + """ + raise NotImplementedError + + @abstractmethod + def num_classes(self): + """ + Determine the number of the classes + + Return: + int: the number of the classes + """ + raise NotImplementedError + + @abstractmethod + def gradient(self, image_batch): + """ + Calculate the gradient of the cross-entropy loss w.r.t the image. + + Args: + image_batch(list): The image and label tuple list. + + Return: + numpy.ndarray: gradient of the cross-entropy loss w.r.t the image with + the shape (height, width, channel). + """ + raise NotImplementedError diff --git a/adversarial/advbox/models/paddle.py b/adversarial/advbox/models/paddle.py new file mode 100644 index 0000000000000000000000000000000000000000..33b2a3d5c6973470fb25c98872cd53b3ff11bab4 --- /dev/null +++ b/adversarial/advbox/models/paddle.py @@ -0,0 +1,101 @@ +from __future__ import absolute_import + +import numpy as np +import paddle.v2 as paddle +import paddle.v2.fluid as fluid +from paddle.v2.fluid.framework import program_guard + +from .base import Model + + +class PaddleModel(Model): + """ + Create a PaddleModel instance. + When you need to generate a adversarial sample, you should construct an instance of PaddleModel. + + Args: + program(paddle.v2.fluid.framework.Program): The program of the model which generate the adversarial sample. + input_name(string): The name of the input. + logits_name(string): The name of the logits. + predict_name(string): The name of the predict. + cost_name(string): The name of the loss in the program. + """ + + def __init__(self, + program, + input_name, + logits_name, + predict_name, + cost_name, + bounds, + channel_axis=3, + preprocess=None): + super(PaddleModel, self).__init__( + bounds=bounds, channel_axis=channel_axis, preprocess=preprocess) + + if preprocess is None: + preprocess = (0, 1) + + self._program = program + self._place = fluid.CPUPlace() + self._exe = fluid.Executor(self._place) + + self._input_name = input_name + self._logits_name = logits_name + self._predict_name = predict_name + self._cost_name = cost_name + + # gradient + loss = self._program.block(0).var(self._cost_name) + param_grads = fluid.backward.append_backward( + loss, parameter_list=[self._input_name]) + self._gradient = dict(param_grads)[self._input_name] + + def predict(self, image_batch): + """ + Predict the label of the image_batch. + + Args: + image_batch(list): The image and label tuple list. + Return: + numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes). + """ + feeder = fluid.DataFeeder( + feed_list=[self._input_name, self._logits_name], + place=self._place, + program=self._program) + predict_var = self._program.block(0).var(self._predict_name) + predict = self._exe.run(self._program, + feed=feeder.feed(image_batch), + fetch_list=[predict_var]) + return predict + + def num_classes(self): + """ + Calculate the number of classes of the output label. + + Return: + int: the number of classes + """ + predict_var = self._program.block(0).var(self._predict_name) + assert len(predict_var.shape) == 2 + return predict_var.shape[1] + + def gradient(self, image_batch): + """ + Calculate the gradient of the loss w.r.t the input. + + Args: + image_batch(list): The image and label tuple list. + Return: + list: The list of the gradient of the image. + """ + feeder = fluid.DataFeeder( + feed_list=[self._input_name, self._logits_name], + place=self._place, + program=self._program) + + grad, = self._exe.run(self._program, + feed=feeder.feed(image_batch), + fetch_list=[self._gradient]) + return grad diff --git a/adversarial/fluid_mnist.py b/adversarial/fluid_mnist.py new file mode 100644 index 0000000000000000000000000000000000000000..db4d4b51868ffa8be13d4d57a40e1def7e25d1a8 --- /dev/null +++ b/adversarial/fluid_mnist.py @@ -0,0 +1,86 @@ +""" +CNN on mnist data using fluid api of paddlepaddle +""" +import paddle.v2 as paddle +import paddle.v2.fluid as fluid + + +def mnist_cnn_model(img): + """ + Mnist cnn model + + Args: + img(Varaible): the input image to be recognized + + Returns: + Variable: the label prediction + """ + conv_pool_1 = fluid.nets.simple_img_conv_pool( + input=img, + num_filters=20, + filter_size=5, + pool_size=2, + pool_stride=2, + act='relu') + + conv_pool_2 = fluid.nets.simple_img_conv_pool( + input=conv_pool_1, + num_filters=50, + filter_size=5, + pool_size=2, + pool_stride=2, + act='relu') + + logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax') + return logits + + +def main(): + """ + Train the cnn model on mnist datasets + """ + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + logits = mnist_cnn_model(img) + cost = fluid.layers.cross_entropy(input=logits, label=label) + avg_cost = fluid.layers.mean(x=cost) + optimizer = fluid.optimizer.Adam(learning_rate=0.01) + optimizer.minimize(avg_cost) + + accuracy = fluid.evaluator.Accuracy(input=logits, label=label) + + BATCH_SIZE = 50 + PASS_NUM = 3 + ACC_THRESHOLD = 0.98 + LOSS_THRESHOLD = 10.0 + train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=500), + batch_size=BATCH_SIZE) + + place = fluid.CPUPlace() + exe = fluid.Executor(place) + feeder = fluid.DataFeeder(feed_list=[img, label], place=place) + exe.run(fluid.default_startup_program()) + + for pass_id in range(PASS_NUM): + accuracy.reset(exe) + for data in train_reader(): + loss, acc = exe.run(fluid.default_main_program(), + feed=feeder.feed(data), + fetch_list=[avg_cost] + accuracy.metrics) + pass_acc = accuracy.eval(exe) + print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" + + str(pass_acc)) + if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD: + break + + pass_acc = accuracy.eval(exe) + print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc)) + fluid.io.save_params( + exe, dirname='./mnist', main_program=fluid.default_main_program()) + print('train mnist done') + + +if __name__ == '__main__': + main() diff --git a/adversarial/mnist_tutorial_fgsm.py b/adversarial/mnist_tutorial_fgsm.py new file mode 100644 index 0000000000000000000000000000000000000000..8b29346b8cd7f643771640afc4f783f7544cd071 --- /dev/null +++ b/adversarial/mnist_tutorial_fgsm.py @@ -0,0 +1,87 @@ +""" +FGSM demos on mnist using advbox tool. +""" +import paddle.v2 as paddle +import paddle.v2.fluid as fluid +import matplotlib.pyplot as plt +import numpy as np + +from advbox.models.paddle import PaddleModel +from advbox.attacks.gradientsign import GradientSignAttack + + +def cnn_model(img): + """ + Mnist cnn model + Args: + img(Varaible): the input image to be recognized + Returns: + Variable: the label prediction + """ + #conv1 = fluid.nets.conv2d() + conv_pool_1 = fluid.nets.simple_img_conv_pool( + input=img, + num_filters=20, + filter_size=5, + pool_size=2, + pool_stride=2, + act='relu') + + conv_pool_2 = fluid.nets.simple_img_conv_pool( + input=conv_pool_1, + num_filters=50, + filter_size=5, + pool_size=2, + pool_stride=2, + act='relu') + + logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax') + return logits + + +def main(): + """ + Advbox demo which demonstrate how to use advbox. + """ + IMG_NAME = 'img' + LABEL_NAME = 'label' + + img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32') + # gradient should flow + img.stop_gradient = False + label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64') + logits = cnn_model(img) + cost = fluid.layers.cross_entropy(input=logits, label=label) + avg_cost = fluid.layers.mean(x=cost) + + place = fluid.CPUPlace() + exe = fluid.Executor(place) + + BATCH_SIZE = 1 + train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=500), + batch_size=BATCH_SIZE) + feeder = fluid.DataFeeder( + feed_list=[IMG_NAME, LABEL_NAME], + place=place, + program=fluid.default_main_program()) + + fluid.io.load_params( + exe, "./mnist/", main_program=fluid.default_main_program()) + + # advbox demo + m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME, + logits.name, avg_cost.name, (-1, 1)) + att = GradientSignAttack(m) + for data in train_reader(): + # fgsm attack + adv_img = att(data) + plt.imshow(n[0][0], cmap='Greys_r') + plt.show() + #np.save('adv_img', adv_img) + break + + +if __name__ == '__main__': + main() diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst index a7c8670f66cc7f319e41155211ead2d89126117f..696a8012aa4aacb78250be3a2d7f49718c009839 100644 --- a/doc/api/v2/fluid/layers.rst +++ b/doc/api/v2/fluid/layers.rst @@ -38,6 +38,16 @@ elementwise_add .. autofunction:: paddle.v2.fluid.layers.elementwise_add :noindex: +elementwise_sub +--------------- +.. autofunction:: paddle.v2.fluid.layers.elementwise_sub + :noindex: + +elementwise_mul +--------------- +.. autofunction:: paddle.v2.fluid.layers.elementwise_mul + :noindex: + elementwise_div --------------- .. autofunction:: paddle.v2.fluid.layers.elementwise_div diff --git a/doc/design/block.md b/doc/design/block.md index fab7f2dc481ae51aa982164dc5048d90fcdc2b0b..907a2def557fd472ac4d679c73447bd9107d1190 100644 --- a/doc/design/block.md +++ b/doc/design/block.md @@ -202,8 +202,8 @@ This `OpDesc` value is in the `ops` field of the `BlockDesc` value representing During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator). -VarDesc in a block should have its name scope to avoid local variables affect parent block's name scope. -Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that stored in parent block. For example: +VarDesc in a block should have its name scope to avoid local variables affecting parent block's name scope. +Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that is stored in the parent block. For example: ```python a = pd.Variable(shape=[20, 20]) diff --git a/doc/design/memory_optimization.md b/doc/design/memory_optimization.md index 00f514711a46bfd5af3bae51e0d9225ecc4c8998..1f68cef4cc28cd005acbeaa5c03cc0d84a83939c 100644 --- a/doc/design/memory_optimization.md +++ b/doc/design/memory_optimization.md @@ -5,28 +5,28 @@ In a lecture from Andrew Ng, he attributes the recent sucess of AI due to a combination of these: -- availability of Big Data -- supercomputing power to process this Big Data over very large neural networks -- modern algorithms +- Availability of Big Data +- Supercomputing power to process this Big Data over very large neural networks +- Modern algorithms Following graph shows the details: ![](images/deep_learning.png) -Larger model usually brings better performance. However, GPU memory is certain limited. For example, the memory size of a GTX TITAN X is only 12GB. To train complex and large model, we have to take care of memory using. Besides, memory optimization is also necessary in both online/mobile inference. +Larger model usually bring better performance. However, GPU memory is limited. For example, the memory size of a GTX TITAN X is only 12GB. To train complex and large models, we have to take care of memory usage. Besides, memory optimization is also necessary in both online/mobile inference. ## Solution ### Basic Strategy -There are some basic strategies to make memory optimization, including in-place operation and memory sharing. +There are some basic strategies to improve memory usage, including in-place operations and memory sharing. #### In-place Operation In a relu activation operator： $y = \max(x, 0)$ -If the variable x is not used in any other operator, we can make an in-place operation. In other words, the memory block of variable y and variable x are the same. In-place operation will save 50% memory occupancy immediately. +If the variable x is not used in any other operator, we can make an in-place operation. In other words, the memory block of variable y and variable x will be the same. In-place operations will save 50% memory occupancy immediately. #### Memory Sharing @@ -40,18 +40,18 @@ d = op2(a) e = op3(d, f) ``` -In this case, variable a is no longer used, and op2 does not support in-place operation. After op2 finished, we can put the memory of variable a to a memory pool. Then, variable e can share the memory of variable a from the pool. +In this case, variable a is no longer used, and op2 does not support in-place operation. After op2 finishes, we can put the memory of variable a to a memory pool. Then, variable e can share the memory of variable a from the pool. ### Live Variable Analysis -It's not enough to only have some basic strategies. The prerequisite of memory optimization is to know if a variable is still "live" after an operation. +It's not enough to only have some basic strategies. The pre-requisite of memory optimization is to know if a variable is still "live" after an operation. In our design, the neural network topology is defined as a program. Luckily, [live variable analysis](https://en.wikipedia.org/wiki/Live_variable_analysis) is a classic problem in compilers which can be used in many stages, such as register allocation. -In compilers, the front end of the compilers translates programs into an intermediate language with an unbounded number of temporaries. This program must run on a machine with a bounded number of registers. Two temporaries a and b can fit into the same register, if a and b are never "in use" at the same time. Thus, many temporaries can fit in few registers; if they don't all fit, the excess temporaries can be kept in memory. +In compilers, the front end of the compiler translates programs into an intermediate language with an unbounded number of temporary variables. This program must run on a machine with a bounded number of registers. Two temporary variables a and b can fit into the same register, if a and b are never "in use" at the same time. Thus, many temporary variables can fit in few registers; if they don't all fit, the excess tempory variables can be kept in memory. -Therefore, the compiler needs to analyze the intermediate-representation program to determine which temporaries are in use at the same time. We say a variable is "live" if it holds a value that may be needed in the future, so this analysis is called liveness analysis. +Therefore, the compiler needs to analyze the intermediate-representation program to determine which temporary variables are in use at the same time. We say a variable is "live" if it holds a value that may be needed in the future, so this analysis is called liveness analysis. We can leran these techniques from compilers. There are mainly two stages to make live variable analysis: @@ -60,7 +60,7 @@ We can leran these techniques from compilers. There are mainly two stages to mak #### Control Flow Graph -To preform analyses on a program, it is often useful to make a control flow graph. A [control flow graph](https://en.wikipedia.org/wiki/Control_flow_graph) (CFG) in computer science is a representation, using graph notation, of all paths that might be traversed through a program during its execution. Each statement in the program is a node in the flow graph; if statemment x can be followed by statement y, there is an egde from x to y. +To perform analysis on a program, it is often useful to make a control flow graph. A [control flow graph](https://en.wikipedia.org/wiki/Control_flow_graph) (CFG) in computer science is a representation, using graph notation, of all paths that might be traversed through a program during its execution. Each statement in the program is a node in the flow graph; if statemment x can be followed by statement y, there is an egde from x to y. Following is the flow graph for a simple loop. @@ -68,18 +68,18 @@ Following is the flow graph for a simple loop. #### Dataflow Analysis -liveness of variable "flows" around the edges of the control flow graph; determining the live range of each variable is an example of a dataflow problem. [Dataflow analysis](https://en.wikipedia.org/wiki/Data-flow_analysis) is a technique for gathering information about the possible set of values calculated at various points in a computer program. +Liveness of variable "flows" around the edges of the control flow graph; determining the live range of each variable is an example of a dataflow problem. [Dataflow analysis](https://en.wikipedia.org/wiki/Data-flow_analysis) is a technique for gathering information about the possible set of values calculated at various points in a computer program. A simple way to perform data-flow analysis of programs is to set up dataflow equations for each node of the control flow graph and solve them by repeatedly calculating the output from the input locally at each node until the whole system stabilizes. - Flow Graph Terminology -A flow graph node has out-edges that lead to sucessor nodes, and in-edges that come from presucessor nodes. The set *pred[n]* is all the predecessors of node n, and *succ[n]* is the set of sucessors. +A flow graph node has out-edges that lead to sucessor nodes, and in-edges that come from predecessor nodes. The set *pred[n]* is all the predecessors of node n, and *succ[n]* is the set of sucessors. In former control flow graph, the out-edges of node 5 are 5 --> 6 and 5 --> 2, and *succ[5]* = {2, 6}. The in-edges of 2 are 5 --> 2 and 1 --> 2, and *pred[2]* = {1, 5}. - Uses and Defs -An assignmemt to a variable or temporary defines that variable. An occurence of a variable on the right-hand side of an assginment(or in other expressions) uses the variable. We can speak the *def* of a variable as the set of graph nodes that define it; or the *def* of a graph node as the set of variables that it defines; and the similarly for the *use* of a variable or graph node. In former control flow graph, *def(3)* = {c}, *use(3)* = {b, c}. +An assignmemt to a variable or temporary defines that variable. An occurence of a variable on the right-hand side of an assginment(or in other expressions) uses the variable. We can define the *def* of a variable as the set of graph nodes that define it; or the *def* of a graph node as the set of variables that it defines; and the similarly for the *use* of a variable or graph node. In former control flow graph, *def(3)* = {c}, *use(3)* = {b, c}. - Liveness @@ -168,9 +168,9 @@ class ControlFlowGraph(object): return self._program ``` -#### make dataflow analysis +#### Make dataflow analysis -We follow guide from compilers and try to solve the dataflow equation to get liveness of every variable. If the live-in of an operator node is different from the live-out, then we can make memory sharing. +We follow the guide from compilers and try to solve the dataflow equation to get liveness of every variable. If the live-in of an operator node is different from the live-out, then we can make memory sharing. For example: diff --git a/doc/design/operator_kernel_type.md b/doc/design/operator_kernel_type.md index aa82e96bf79319f1a57e2ad58aa9826e57be6470..f86e6b7a564ed23f2bddbec25da1c110014f941d 100644 --- a/doc/design/operator_kernel_type.md +++ b/doc/design/operator_kernel_type.md @@ -1,6 +1,6 @@ # Design Doc: The Keys of Operator Kernel Type ## Problem -An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows: +An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique kernel. Before an operator runs, a certain type of kernel must be chosen via a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows: ```cpp struct OpKernelType { @@ -10,13 +10,13 @@ struct OpKernelType { ``` For more details, please refer to [codes](https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374) in github. -It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of `OpKernelType`. +It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys do not provide enough information. We need a more complete representation of `OpKernelType`. -We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices. +We often implement a kernel of an operator with some computing library on certain device(place). Please note that computing library and device do not have a one-to-one correspondence. A device can have a lot of computing libraries and a computing library can also support different devices. -For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`. +For example, Eigen library supports Nvidia GPU/AMD GPU/CPU and MKLDNN library supports Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`. -It's obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209). Data Layout should also be taken into consideration. +Different DataTypes, such as fp64/fp32/int8, will obviously have different kernels. But different data layout of a Tensor will also lead to different implementations. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209) as an example. Data layout should also be taken into consideration. ## Solution @@ -31,17 +31,17 @@ struct OpKernelType { }; ``` -Following is the details: +The details are as follows: ### Place -`Place` is defined as follows: +`Place` is defined as: ```cpp typedef boost::variant Place; ``` -`Place` is to represent the device memory where data is locating. +`Place` represents the device memory where data is located. ### Library @@ -52,10 +52,10 @@ One operator kernel is usually implemented based on one library. `Library` is de enum Library { Plain, MKLDNN, CUDNN }; ``` -We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on `Eigen` library, we take `Eigen` library as the `Plain` enumerator. -A library usually has a corresponding `DeviceContext` which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains a Eigen library handle and `CDUADeviceContext` contains a Eigen library handle and cuBLAS handle. +We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on the `Eigen` library, we take `Eigen` library as the `Plain` enumerator. +A library usually has a corresponding `DeviceContext` which contains some handles needed for computation. Fluid now has two default DeviceContexts for CPU and CUDA, namely, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains an Eigen library handle and `CDUADeviceContext` contains an Eigen library handle and a cuBLAS handle. -If we want to support new Library, a new enumerator need to be added to `Library` and a new corresponding `LibraryDeviceContext` will be created. +If we want to support new library, a new enumerator need to be added to `Library` and a corresponding new `LibraryDeviceContext` need to be created. ### DataType @@ -67,15 +67,15 @@ If we want to support new Library, a new enumerator need to be added to `Library Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout. -Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework. +Different layout leads to different implementation of the operator kernel. There are mainly 4 principles we have to follow to support layout in our Fluid framework. -- We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too. +- We take layout as a data member of Tensor. Layout is actually a enum variable. If Fluid is built with MKLDNN, then the memory format in MKLDNN will also be added into this enum variable. -- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW. +- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout for generating data. Of course, we can have some default layout, like NCHW. -- The inference of Layout is at run-time, not compile-time. +- The inference of Layout is at run-time, not at compile-time. -- Every operator have to implement different kernels for different layouts. Let's take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to do registering kernels for MKLDNN operators. +- Every operator has to implement different kernels for different layouts. Let's take MKLDNN as an example. If we want to implement an MKLDNN convolution operator, we have to implement all the kernels for different layouts, which are listed [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to register kernels for MKLDNN operators. `Layout` is also defined as a enum variable: diff --git a/doc/design/python_api.md b/doc/design/python_api.md index cb5fdc765b7126fc66a1c8978d4b96c0dc5a9f2c..73f6d7b90c7dca0d48109cf3d28d5f7cd56b5c0b 100644 --- a/doc/design/python_api.md +++ b/doc/design/python_api.md @@ -279,6 +279,26 @@ class LayerHelper(object): return tmp ``` +### Return value of layer functions + +The layer will return a Variable, which is also the output of an operator. However, outputs of a layer function have more attributes than an operator. There are parameter variables, and their gradient variables need to return. To return them is useful. For example, + +1. Users can debug the network by printing parameter gradients. +2. Users can append attributes to a parameter, such as, `param.stop_gradient=True` will make a parameter stop generate the gradient. We can fix the parameter value during training by using this attribute. + +However, it is good to return a Variable for layers, since all layers and operators use Variables as their parameters. We can just append a `param` field and a `grad` field for layer function since the Python is dynamic typing. + +The sample usage is + +```python +data = fluid.layers.data(...) +hidden = fluid.layers.fc(data, ...) +... + +executor.run(fetch_list=[hidden.param, hidden.param.grad], ...) +``` + + ## Optimizer [Optimizer Design Doc](./optimizer.md) diff --git a/doc/design/var_desc.md b/doc/design/var_desc.md index 0b2958c1b10ef6a6ce51aa75f61e15a7f2d94b3f..89fa95326c5c4909137544c6b5fd574e1281abe2 100644 --- a/doc/design/var_desc.md +++ b/doc/design/var_desc.md @@ -1,12 +1,12 @@ ## Background -PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime. +PaddlePaddle divides the description of neural network computation into two stages: compile time and runtime. At compile time, the neural network computation is described as a `ProgramDesc` whereas at runtime an `Executor` interprets the `ProgramDesc` to compute the operations. -PaddlePaddle use proto message to describe compile time graph because +PaddlePaddle use proto message to describe compile time program because -1. Computation graph should be able to be saved to a file. -1. In distributed training, the graph will be serialized and send to multiple workers. +1. The computation program description must be serializable and saved in a file. +1. During distributed training, the sreialized program will be sent to multiple workers. It should also be possible to break the program into different components, each of which can be executed on different workers. -The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below. +The computation `Program` consists of nested `Blocks`. Each `Block` will consist of data(i.e. `Variable`) and `Operations`. The concept to represent them is in the table below. | |compile time|runtime| |---|---|---| diff --git a/doc/howto/read_source.md b/doc/howto/read_source.md index e4211abb3be9cace80bc14dbe3db3e0a31221dd0..31987920f32f217ac2db42548874cfe7da57dd72 100644 --- a/doc/howto/read_source.md +++ b/doc/howto/read_source.md @@ -26,16 +26,16 @@ sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_cost) ``` -- Variables: `x`, `y`, `y_predict`, `cost` and `avg_cost`. [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/framework.py#L93) -- Layers: `fluid.layers.data`, `fluid.layers.fc` and `fluid.layers.mean` are layers. [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/layers.py) +- Variables: `x`, `y`, `y_predict`, `cost` and `avg_cost`. [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/framework.py#) +- Layers: `fluid.layers.data`, `fluid.layers.fc` and `fluid.layers.mean` are layers. [Python](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid/layers) - Every Layer has one or more operators and variables/parameters - All the operators are defined at [`paddle/operators/`](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators). Other worth-looking files: - Base class: [`paddle/framework/operator.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h) - Operator Registration: [`paddle/framework/op_registry.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h) - Operator Lookup: [`paddle/framework/op_info.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_info.h) - Optimizer: `fluid.optimizer.SGD`. It does the following - - Add backward operators. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/backward.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/backward.cc)] - - Add optimizer operators. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/optimizer.py), [C++](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/optimizer)] + - Add backward operators. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/backward.py)] + - Add optimizer operators. [[Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/optimizer.py)] # Run Time diff --git a/paddle/framework/init.cc b/paddle/framework/init.cc index e12bac1d78e3f6bbc46849c06b53e3b93e147cfc..4ef82a541efaa35bcf831d5122570154f2fa2423 100644 --- a/paddle/framework/init.cc +++ b/paddle/framework/init.cc @@ -11,6 +11,7 @@ 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. */ +#include // for strdup #include #include @@ -60,7 +61,9 @@ void InitDevices() { } void InitGLOG(const std::string &prog_name) { - google::InitGoogleLogging(prog_name.c_str()); + // glog will not hold the ARGV[0] inside. + // Use strdup to alloc a new string. + google::InitGoogleLogging(strdup(prog_name.c_str())); google::InstallFailureSignalHandler(); } diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index ef2c55cc3799ba2fac54f3c9370505b63ef22ad3..7756a52ca9b794d70c7d98ffddcec264861f3e0e 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -11,6 +11,7 @@ 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. */ +#include #include #include @@ -21,6 +22,10 @@ limitations under the License. */ #include "paddle/framework/shape_inference.h" #include "paddle/framework/var_type.h" +DEFINE_bool(op_sync, false, + "Default cuda is asynchronous device, set to True will" + "force op run in synchronous mode."); + namespace paddle { namespace framework { @@ -542,8 +547,14 @@ void OperatorWithKernel::Run(const Scope& scope, auto kernel_iter = kernels.find(expected_kernel_key); - kernel_iter->second->Compute(ExecutionContext( - *this, new_scope, *pool.Get(expected_kernel_key.place_))); + auto* new_dev_ctx = pool.Get(expected_kernel_key.place_); + kernel_iter->second->Compute( + ExecutionContext(*this, new_scope, *new_dev_ctx)); + + /*For profiling/benchmark only*/ + if (FLAGS_op_sync) { + new_dev_ctx->Wait(); + } } proto::DataType OperatorWithKernel::IndicateDataType( diff --git a/paddle/operators/detail/CMakeLists.txt b/paddle/operators/detail/CMakeLists.txt index f6bdc63cc2cfae526fe911ee4d989675452d5c5d..571a75c9dcd903672d460f192bf28ddbeaea7c78 100644 --- a/paddle/operators/detail/CMakeLists.txt +++ b/paddle/operators/detail/CMakeLists.txt @@ -1 +1 @@ -grpc_library(sendrecvop_grpc SRCS recv_impl.cc send_impl.cc PROTO send_recv.proto DEPS lod_tensor selected_rows) +grpc_library(sendrecvop_grpc SRCS sendrecvop_utils.cc grpc_client.cc grpc_server.cc PROTO send_recv.proto DEPS lod_tensor selected_rows) diff --git a/paddle/operators/detail/grpc_client.cc b/paddle/operators/detail/grpc_client.cc new file mode 100644 index 0000000000000000000000000000000000000000..5a4db2d7e686ce84abef620f890be8f3aa82cb73 --- /dev/null +++ b/paddle/operators/detail/grpc_client.cc @@ -0,0 +1,147 @@ +/* Copyright (c) 2016 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. */ + +#include "grpc_client.h" +namespace paddle { +namespace operators { +namespace detail { + +bool RPCClient::AsyncSendVariable(const std::string& ep, + const platform::DeviceContext& ctx, + const framework::Scope& scope, + const std::string& var_name, + int64_t time_out) { + sendrecv::VariableMessage req; + auto* var = scope.FindVar(var_name); + SerializeToMessage(var_name, var, ctx, &req); + + // varhandle + VarHandle var_h; + var_h.ep = ep; + var_h.scope = &scope; + var_h.name = var_name; + var_h.ctx = &ctx; + + // stub context + auto ch = GetChannel(ep); + SendProcessor* s = new SendProcessor(ch); + s->Prepare(var_h, time_out); + s->response_call_back_ = NULL; + + auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_); + rpc->Finish(&s->reply_, &s->status_, (void*)s); + + req_count_++; + + return true; +} + +void ProcGetResponse(const VarHandle& var_h, + const sendrecv::VariableMessage& ret_msg) { + auto* outvar = var_h.scope->FindVar(var_h.name); + + std::istringstream iss(ret_msg.serialized()); + DeserializeFromMessage(ret_msg, *var_h.ctx, outvar); +} + +bool RPCClient::AsyncGetVariable(const std::string& ep, + const platform::DeviceContext& ctx, + const framework::Scope& scope, + const std::string& var_name, + int64_t time_out) { + sendrecv::VariableMessage req; + req.set_varname(var_name); + + auto* var = scope.FindVar(var_name); + SerializeToMessage(var_name, var, ctx, &req); + + // varhandle + VarHandle var_h; + var_h.ep = ep; + var_h.scope = &scope; + var_h.name = var_name; + var_h.ctx = &ctx; + + // stub context + auto ch = GetChannel(ep); + GetProcessor* s = new GetProcessor(ch); + s->Prepare(var_h, time_out); + s->response_call_back_ = ProcGetResponse; + + auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_); + rpc->Finish(&s->reply_, &s->status_, (void*)s); + + req_count_++; + + return true; +} + +bool RPCClient::wait() { + bool ok = true; + + while (true) { + if (req_count_ <= 0) { + break; + } + + if (!Proceed()) { + LOG(ERROR) << "Get meets CompletionQueue error"; + return false; + } + } + + return ok; +} + +bool RPCClient::Proceed() { + void* tag = NULL; + bool ok = false; + + // request counts. + if (!cq_.Next(&tag, &ok)) { + return false; + } + req_count_--; + + GPR_ASSERT(ok); + PADDLE_ENFORCE(tag); + + // TODO(gongwb): add more retries. + ClientBase* c = static_cast(tag); + if (!c->status_.ok()) { + delete c; + return true; + } + + c->Process(); + delete c; + return true; +} + +std::shared_ptr RPCClient::GetChannel(const std::string& ep) { + auto it = channels_.find(ep); + if (it != channels_.end()) { + return it->second; + } + + auto ch = std::shared_ptr( + grpc::CreateChannel(ep, grpc::InsecureChannelCredentials())); + + channels_[ep] = ch; + return ch; +} + +} // namespace detail +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/detail/grpc_client.h b/paddle/operators/detail/grpc_client.h new file mode 100644 index 0000000000000000000000000000000000000000..d27b5ced9ece67f9b9da3b7f87ec231477603580 --- /dev/null +++ b/paddle/operators/detail/grpc_client.h @@ -0,0 +1,147 @@ +/* Copyright (c) 2016 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. */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "paddle/framework/data_type.h" +#include "paddle/framework/lod_tensor.h" +#include "paddle/framework/scope.h" +#include "paddle/framework/selected_rows.h" +#include "paddle/operators/detail/sendrecvop_utils.h" +#include "paddle/operators/detail/simple_block_queue.h" + +namespace paddle { +namespace operators { +namespace detail { + +struct VarHandle { + std::string ep; + const platform::DeviceContext* ctx; + const framework::Scope* scope; + std::string name; + + std::string String() const { + std::ostringstream s; + s << "name:[" << name << "] ep:[" << ep << "]"; + return s.str(); + } +}; + +void ProcGetResponse(const VarHandle& var_h, + const sendrecv::VariableMessage& msg); + +class ClientBase { + public: + explicit ClientBase(std::shared_ptr ch) { + stub_ = sendrecv::SendRecvService::NewStub(ch); + context_ = NULL; + } + + virtual ~ClientBase() {} + + virtual void Prepare(const VarHandle& var_info, int64_t time_out) { + context_.reset(new grpc::ClientContext()); + var_h_ = var_info; + + std::chrono::system_clock::time_point deadline = + std::chrono::system_clock::now() + std::chrono::milliseconds(time_out); + + context_->set_deadline(deadline); + } + + virtual void Process() = 0; + + std::unique_ptr stub_; + std::unique_ptr context_; + grpc::Status status_; + VarHandle var_h_; +}; + +typedef std::function + RequestSendCallBack; + +class SendProcessor : public ClientBase { + public: + explicit SendProcessor(std::shared_ptr ch) : ClientBase(ch) {} + + virtual ~SendProcessor() {} + + virtual void Process() { + if (response_call_back_) { + response_call_back_(var_h_, reply_); + } + } + + sendrecv::VoidMessage reply_; + RequestSendCallBack response_call_back_ = NULL; +}; + +typedef std::function + RequestGetCallBack; + +class GetProcessor : public ClientBase { + public: + explicit GetProcessor(std::shared_ptr ch) : ClientBase(ch) {} + + virtual ~GetProcessor() {} + + virtual void Process() { + if (response_call_back_) { + response_call_back_(var_h_, reply_); + } + } + + sendrecv::VariableMessage reply_; + RequestGetCallBack response_call_back_ = ProcGetResponse; +}; + +class RPCClient { + public: + bool AsyncSendVariable(const std::string& ep, + const platform::DeviceContext& ctx, + const framework::Scope& scope, + const std::string& var_name, + int64_t time_out = 600 * 1000); + + bool AsyncGetVariable(const std::string& ep, + const platform::DeviceContext& ctx, + const framework::Scope& scope, + const std::string& var_name, + int64_t time_out = 600 * 1000); + bool wait(); + + private: + bool Proceed(); + std::shared_ptr GetChannel(const std::string& ep); + + private: + grpc::CompletionQueue cq_; + std::map> channels_; + int64_t req_count_ = 0; +}; + +} // namespace detail +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/detail/grpc_server.cc b/paddle/operators/detail/grpc_server.cc new file mode 100644 index 0000000000000000000000000000000000000000..e8d561a57ff59e9221400241f881cb26fb6c6f06 --- /dev/null +++ b/paddle/operators/detail/grpc_server.cc @@ -0,0 +1,237 @@ +/* Copyright (c) 2016 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. */ + +#include "paddle/operators/detail/grpc_server.h" + +using grpc::ServerAsyncResponseWriter; + +namespace paddle { +namespace operators { +namespace detail { + +enum CallStatus { PROCESS = 0, FINISH }; + +// reference: +// https://stackoverflow.com/questions/41732884/grpc-multiple-services-in-cpp-async-server +class RequestBase { + public: + explicit RequestBase(sendrecv::SendRecvService::AsyncService* service, + grpc::ServerCompletionQueue* cq) + : service_(service), cq_(cq), status_(PROCESS) {} + virtual ~RequestBase() {} + virtual void Process() { assert(false); } + + CallStatus Status() { return status_; } + void SetStatus(CallStatus status) { status_ = status; } + + protected: + grpc::ServerContext ctx_; + sendrecv::SendRecvService::AsyncService* service_; + grpc::ServerCompletionQueue* cq_; + CallStatus status_; +}; + +typedef std::pair MessageWithName; + +class RequestSend final : public RequestBase { + public: + explicit RequestSend(sendrecv::SendRecvService::AsyncService* service, + grpc::ServerCompletionQueue* cq, + SimpleBlockQueue* queue) + : RequestBase(service, cq), queue_(queue), responder_(&ctx_) { + service_->RequestSendVariable(&ctx_, &request_, &responder_, cq_, cq_, + this); + } + + virtual ~RequestSend() {} + + virtual void Process() { + MessageWithName msg_with_name = + std::make_pair(request_.varname(), std::move(request_)); + queue_->Push(std::move(msg_with_name)); + // TODO(gongwb): check var's info. + responder_.Finish(reply_, grpc::Status::OK, this); + } + + protected: + sendrecv::VariableMessage request_; + sendrecv::VoidMessage reply_; + SimpleBlockQueue* queue_; + ServerAsyncResponseWriter responder_; +}; + +class RequestGet final : public RequestBase { + public: + explicit RequestGet(sendrecv::SendRecvService::AsyncService* service, + grpc::ServerCompletionQueue* cq, framework::Scope* scope) + : RequestBase(service, cq), responder_(&ctx_), scope_(scope) { + service_->RequestGetVariable(&ctx_, &request_, &responder_, cq_, cq_, this); + } + + virtual ~RequestGet() {} + + virtual void Process() { + // proc request. + std::string var_name = request_.varname(); + auto* var = scope_->FindVar(var_name); + SerializeToMessage(var_name, var, platform::CPUDeviceContext(), &reply_); + // TODO(gongwb): check var's info. + responder_.Finish(reply_, grpc::Status::OK, this); + } + + protected: + sendrecv::VariableMessage request_; + sendrecv::VariableMessage reply_; + ServerAsyncResponseWriter responder_; + framework::Scope* scope_; +}; + +void AsyncGRPCServer::RunSyncUpdate() { + grpc::ServerBuilder builder; + builder.AddListeningPort(address_, grpc::InsecureServerCredentials()); + builder.RegisterService(&service_); + + cq_send_ = builder.AddCompletionQueue(); + cq_get_ = builder.AddCompletionQueue(); + server_ = builder.BuildAndStart(); + LOG(INFO) << "Server listening on " << address_ << std::endl; + + std::function send_register = + std::bind(&AsyncGRPCServer::TryToRegisterNewSendOne, this); + std::function get_register = + std::bind(&AsyncGRPCServer::TryToRegisterNewGetOne, this); + + t_send_.reset( + new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, false, + cq_send_.get(), "cq_send", send_register))); + + t_get_.reset( + new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, true, + cq_get_.get(), "cq_get", get_register))); + + // wait server + server_->Wait(); + t_send_->join(); + t_get_->join(); +} + +void AsyncGRPCServer::ShutdownQueue() { + std::unique_lock lock(cq_mutex_); + cq_send_->Shutdown(); + cq_get_->Shutdown(); + is_shut_down_ = true; +} + +// This URL explains why shutdown is complicate: +// https://stackoverflow.com/questions/35708348/grpc-what-is-the-recommended-way-to-shut-down-an-asynchronous-server-in-c +void AsyncGRPCServer::ShutDown() { + server_->Shutdown(); + ShutdownQueue(); +} + +void AsyncGRPCServer::TryToRegisterNewSendOne() { + std::unique_lock lock(cq_mutex_); + if (is_shut_down_) { + return; + } + RequestSend* send = + new RequestSend(&service_, cq_send_.get(), &var_recv_queue_); + VLOG(4) << "create RequestSend status:" << send->Status(); +} + +void AsyncGRPCServer::TryToRegisterNewGetOne() { + std::unique_lock lock(cq_mutex_); + if (is_shut_down_) { + return; + } + RequestGet* get = new RequestGet(&service_, cq_get_.get(), scope_); + VLOG(4) << "create Requestget status:" << get->Status(); +} + +void AsyncGRPCServer::SetFinishOrDelete(RequestBase*& last) { + std::unique_lock lock(cq_mutex_); + if (is_shut_down_) { + delete last; + last = NULL; + return; + } + + last->SetStatus(FINISH); + return; +} + +void AsyncGRPCServer::HandleRequest(bool wait, grpc::ServerCompletionQueue* cq, + std::string cq_name, + std::function TryToRegisterNewOne) { + TryToRegisterNewOne(); + + void* tag = NULL; + bool ok = false; + while (true) { + if (!cq->Next(&tag, &ok)) { + LOG(INFO) << cq_name << " get CompletionQueue shutdown!"; + break; + } + + if (wait && !done_) { + Wait(); + } + + RequestBase* base = (RequestBase*)tag; + if (!ok) { + VLOG(4) << cq_name << " recv no regular event"; + TryToRegisterNewOne(); + delete base; + continue; + } + + switch (base->Status()) { + case PROCESS: { + VLOG(4) << cq_name << " status:" << base->Status(); + TryToRegisterNewOne(); + base->Process(); + SetFinishOrDelete(base); + break; + } + case FINISH: { + VLOG(4) << cq_name << " status:" << base->Status(); + delete base; + break; + } + default: { assert(false); } + } + } +} + +void AsyncGRPCServer::Wait() { + std::unique_lock lock(this->mutex_); + condition_.wait(lock, [=] { return this->done_ == true; }); +} + +void AsyncGRPCServer::Reset() { + std::lock_guard lock(this->mutex_); + done_ = false; +} + +void AsyncGRPCServer::Done() { + { + std::lock_guard lock(this->mutex_); + done_ = true; + } + condition_.notify_all(); +} + +} // namespace detail +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/detail/grpc_server.h b/paddle/operators/detail/grpc_server.h new file mode 100644 index 0000000000000000000000000000000000000000..041fe05b2e9c37e8a91669b8f523c47b56e14cba --- /dev/null +++ b/paddle/operators/detail/grpc_server.h @@ -0,0 +1,91 @@ +/* Copyright (c) 2016 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. */ + +#pragma once + +#include "paddle/framework/lod_tensor.h" +#include "paddle/framework/scope.h" +#include "paddle/framework/selected_rows.h" +#include "paddle/framework/var_type.h" +#include "paddle/operators/detail/simple_block_queue.h" + +#include "paddle/operators/detail/send_recv.grpc.pb.h" +#include "paddle/operators/detail/send_recv.pb.h" + +#include +#include +#include +#include "paddle/operators/detail/sendrecvop_utils.h" + +namespace paddle { +namespace operators { +namespace detail { + +typedef std::pair MessageWithName; +class RequestBase; + +class AsyncGRPCServer final : public sendrecv::SendRecvService::Service { + public: + explicit AsyncGRPCServer(std::string address) { address_ = address; } + + void RunSyncUpdate(); + + void Reset(); + + void Done(); + + void SetScope(framework::Scope *scope) { scope_ = scope; } + + const MessageWithName Get() { return this->var_recv_queue_.Pop(); } + + void Push(const MessageWithName &msg) { this->var_recv_queue_.Push(msg); } + + void ShutDown(); + + protected: + void Wait(); + void HandleRequest(bool wait, grpc::ServerCompletionQueue *cq, + std::string cq_name, + std::function TryToRegisterNewOne); + void TryToRegisterNewSendOne(); + void TryToRegisterNewGetOne(); + void SetFinishOrDelete(RequestBase *&last); + void ShutdownQueue(); + + private: + std::mutex cq_mutex_; + volatile bool is_shut_down_ = false; + std::unique_ptr cq_send_; + std::unique_ptr cq_get_; + + sendrecv::SendRecvService::AsyncService service_; + std::unique_ptr server_; + + std::string address_; + framework::Scope *scope_; + // received variable from RPC, operators fetch variable from this queue. + SimpleBlockQueue var_recv_queue_; + + // condition of the sub program + std::mutex mutex_; + volatile mutable bool done_; + std::condition_variable condition_; + + std::unique_ptr t_send_; + std::unique_ptr t_get_; +}; + +}; // namespace detail +}; // namespace operators +}; // namespace paddle diff --git a/paddle/operators/detail/recv_impl.cc b/paddle/operators/detail/recv_impl.cc deleted file mode 100644 index 319404e56a5f3c407f313991240bbbb85fd39a2a..0000000000000000000000000000000000000000 --- a/paddle/operators/detail/recv_impl.cc +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright (c) 2016 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. */ - -#include "send_recv_impl.h" - -namespace paddle { -namespace operators { -namespace detail { - -Status SendRecvServerImpl::SendVariable(ServerContext *context, - const VariableMessage *in_var, - VoidMessage *out_var) { - MessageWithName msg_with_name = - std::make_pair(in_var->varname(), std::move(*in_var)); - var_recv_queue_.Push(std::move(msg_with_name)); - return Status::OK; -} - -Status SendRecvServerImpl::GetVariable(ServerContext *context, - const VariableMessage *in_var, - VariableMessage *out_var) { - std::string get_var_name = in_var->varname(); - auto *var = scope_->FindVar(get_var_name); - - SerializeToMessage(get_var_name, var, platform::CPUDeviceContext(), out_var); - return Status::OK; -} - -Status SendRecvServerImpl::Wait(ServerContext *context, - const VoidMessage *in_var, - VoidMessage *out_var) { - { - std::unique_lock lock(this->mutex_); - condition_.wait(lock, [=] { return this->done_ == true; }); - } - return Status::OK; -} - -void SendRecvServerImpl::Reset() { - std::lock_guard lock(this->mutex_); - done_ = false; -} - -void SendRecvServerImpl::Done() { - { - std::lock_guard lock(this->mutex_); - done_ = true; - } - condition_.notify_all(); -} - -} // namespace detail -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/detail/send_impl.cc b/paddle/operators/detail/send_impl.cc deleted file mode 100644 index ae85cf2cec2cd8e046c0c7fd3408f2212f225819..0000000000000000000000000000000000000000 --- a/paddle/operators/detail/send_impl.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* Copyright (c) 2016 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. */ - -#include "send_recv_impl.h" - -namespace paddle { -namespace operators { -namespace detail { - -bool RPCClient::SendVariable(const framework::Scope& scope, - const std::string& inname) { - ClientContext context; - VariableMessage msg; - VoidMessage out_msg; - // FIXME(typhoonzero): pass device context to here. - auto ctx = platform::CPUDeviceContext(); - auto* var = scope.FindVar(inname); - PADDLE_ENFORCE(var); - SerializeToMessage(inname, var, ctx, &msg); - - Status status = stub_->SendVariable(&context, msg, &out_msg); - if (!status.ok()) { - LOG(ERROR) << "gRPC error: " << status.error_message(); - return false; - } - return true; -} - -bool RPCClient::GetVariable(const framework::Scope& scope, - const std::string& outname) { - ClientContext context; - VariableMessage call_msg, ret_msg; - call_msg.set_varname(outname); - auto ctx = platform::CPUDeviceContext(); - Status status = stub_->GetVariable(&context, call_msg, &ret_msg); - auto* outvar = scope.FindVar(outname); - if (!status.ok()) { - LOG(ERROR) << "gRPC error: " << status.error_message(); - return false; - } - - std::istringstream iss(ret_msg.serialized()); - DeserializeFromMessage(ret_msg, ctx, outvar); - - return true; -} - -void RPCClient::Wait() { - ClientContext context; - VoidMessage call_msg, ret_msg; - stub_->Wait(&context, call_msg, &ret_msg); -} - -} // namespace detail -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/detail/send_recv.proto b/paddle/operators/detail/send_recv.proto index f141c755ce14ef540aeab32c11c289179aff3f8c..8f962b4c69cc83dc2ab98b7dc27e18bc4b42bf18 100644 --- a/paddle/operators/detail/send_recv.proto +++ b/paddle/operators/detail/send_recv.proto @@ -21,8 +21,6 @@ service SendRecvService { rpc SendVariable(VariableMessage) returns (VoidMessage) {} // Argument VariableMessage for GetVariable should only contain varname. rpc GetVariable(VariableMessage) returns (VariableMessage) {} - // wait for one execution of the program - rpc Wait(VoidMessage) returns (VoidMessage) {} } // VariableMessage is serialized paddle variable message. diff --git a/paddle/operators/detail/send_recv_impl.h b/paddle/operators/detail/send_recv_impl.h deleted file mode 100644 index 1fe54f1f0536aed7d41bbdeeca076534abafe98d..0000000000000000000000000000000000000000 --- a/paddle/operators/detail/send_recv_impl.h +++ /dev/null @@ -1,141 +0,0 @@ -/* Copyright (c) 2016 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. */ - -#pragma once - -#include "paddle/framework/lod_tensor.h" -#include "paddle/framework/scope.h" -#include "paddle/framework/selected_rows.h" -#include "paddle/framework/var_type.h" -#include "paddle/operators/detail/simple_block_queue.h" - -#include "paddle/operators/detail/send_recv.grpc.pb.h" -#include "paddle/operators/detail/send_recv.pb.h" - -#include - -using grpc::Channel; -using grpc::Server; -using grpc::ServerContext; -using grpc::ServerReader; -using grpc::ServerBuilder; - -using grpc::ClientContext; -using grpc::ClientReader; -using grpc::ClientReaderWriter; -using grpc::ClientWriter; -using grpc::Status; -using sendrecv::SendRecvService; -using sendrecv::VariableMessage; -using sendrecv::VoidMessage; - -namespace paddle { -namespace operators { -namespace detail { - -typedef std::pair MessageWithName; - -class SendRecvServerImpl final : public SendRecvService::Service { - public: - explicit SendRecvServerImpl() {} - - Status SendVariable(ServerContext *context, const VariableMessage *in_var, - VoidMessage *out_var) override; - Status GetVariable(ServerContext *context, const VariableMessage *in_var, - VariableMessage *out_var) override; - Status Wait(ServerContext *context, const VoidMessage *in_var, - VoidMessage *out_var) override; - void Reset(); - void Done(); - void SetScope(framework::Scope *scope) { scope_ = scope; }; - - const MessageWithName Get() { return this->var_recv_queue_.Pop(); } - - void Push(const MessageWithName &msg) { this->var_recv_queue_.Push(msg); } - - private: - // received variable from RPC, operators fetch variable from this queue. - SimpleBlockQueue var_recv_queue_; - framework::Scope *scope_; - // condition of the sub program - std::mutex mutex_; - bool done_; - std::condition_variable condition_; -}; - -// RPCClient is a class to send tensors to pserver sub-network -// using different hashing methods. -class RPCClient { - public: - RPCClient(std::shared_ptr channel) - : stub_(SendRecvService::NewStub(channel)) {} - - bool SendVariable(const framework::Scope &scope, const std::string &inname); - bool GetVariable(const framework::Scope &scope, const std::string &outname); - void Wait(); - - private: - std::unique_ptr stub_; -}; - -inline void SerializeToMessage(const std::string &name, - const framework::Variable *var, - const platform::DeviceContext &ctx, - VariableMessage *msg) { - msg->set_varname(name); - std::ostringstream oss; - switch (framework::ToVarType(var->Type())) { - case framework::proto::VarDesc_VarType_LOD_TENSOR: - msg->set_type(sendrecv::VarType::LOD_TENSOR); - framework::SerializeToStream(oss, var->Get(), ctx); - break; - case framework::proto::VarDesc_VarType_SELECTED_ROWS: - msg->set_type(sendrecv::VarType::SELECTED_ROWS); - framework::SerializeToStream(oss, var->Get(), - ctx); - break; - default: { - PADDLE_THROW("Serialize does not support type: %s", - typeid(var->Type()).name()); - break; - } - } - msg->set_serialized(oss.str()); -} - -inline void DeserializeFromMessage(const VariableMessage &msg, - const platform::DeviceContext &ctx, - framework::Variable *var) { - using namespace paddle::framework::proto; - std::istringstream iss(msg.serialized()); - switch (msg.type()) { - case sendrecv::VarType::LOD_TENSOR: - DeserializeFromStream(iss, var->GetMutable(), ctx); - break; - case sendrecv::VarType::SELECTED_ROWS: { - DeserializeFromStream(iss, var->GetMutable(), - ctx); - break; - } - default: { - PADDLE_THROW("Deserialize does not support type: %s", - typeid(var->Type()).name()); - break; - } - } -} - -} // namespace detail -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/detail/sendrecvop_utils.cc b/paddle/operators/detail/sendrecvop_utils.cc new file mode 100644 index 0000000000000000000000000000000000000000..7635b9e8dbdff624bb42a9de346b8d05a980f9b6 --- /dev/null +++ b/paddle/operators/detail/sendrecvop_utils.cc @@ -0,0 +1,68 @@ +/* Copyright (c) 2016 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. */ + +#include "paddle/operators/detail/sendrecvop_utils.h" + +namespace paddle { +namespace operators { +namespace detail { + +void SerializeToMessage(const std::string& name, const framework::Variable* var, + const platform::DeviceContext& ctx, + sendrecv::VariableMessage* msg) { + msg->set_varname(name); + std::ostringstream oss; + switch (framework::ToVarType(var->Type())) { + case framework::proto::VarDesc_VarType_LOD_TENSOR: + msg->set_type(sendrecv::VarType::LOD_TENSOR); + framework::SerializeToStream(oss, var->Get(), ctx); + break; + case framework::proto::VarDesc_VarType_SELECTED_ROWS: + msg->set_type(sendrecv::VarType::SELECTED_ROWS); + framework::SerializeToStream(oss, var->Get(), + ctx); + break; + default: { + PADDLE_THROW("Serialize does not support type: %s", + typeid(var->Type()).name()); + break; + } + } + msg->set_serialized(oss.str()); +} + +void DeserializeFromMessage(const sendrecv::VariableMessage& msg, + const platform::DeviceContext& ctx, + framework::Variable* var) { + std::istringstream iss(msg.serialized()); + switch (msg.type()) { + case sendrecv::VarType::LOD_TENSOR: + DeserializeFromStream(iss, var->GetMutable(), ctx); + break; + case sendrecv::VarType::SELECTED_ROWS: { + DeserializeFromStream(iss, var->GetMutable(), + ctx); + break; + } + default: { + PADDLE_THROW("Deserialize does not support type: %s", + typeid(var->Type()).name()); + break; + } + } +} + +} // namespace detail +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/detail/sendrecvop_utils.h b/paddle/operators/detail/sendrecvop_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..bc6581afab93c626c7c2439d699c6c2d858df9fa --- /dev/null +++ b/paddle/operators/detail/sendrecvop_utils.h @@ -0,0 +1,42 @@ +/* Copyright (c) 2016 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. */ + +#pragma once +#include +#include +#include + +#include "paddle/framework/data_type.h" +#include "paddle/framework/lod_tensor.h" +#include "paddle/framework/scope.h" +#include "paddle/framework/selected_rows.h" +#include "paddle/framework/var_type.h" + +#include "paddle/operators/detail/send_recv.grpc.pb.h" +#include "paddle/operators/detail/send_recv.pb.h" + +namespace paddle { +namespace operators { +namespace detail { + +void SerializeToMessage(const std::string& name, const framework::Variable* var, + const platform::DeviceContext& ctx, + sendrecv::VariableMessage* msg); + +void DeserializeFromMessage(const sendrecv::VariableMessage& msg, + const platform::DeviceContext& ctx, + framework::Variable* var); +} // namespace detail +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/elementwise_add_op.cc b/paddle/operators/elementwise_add_op.cc index 70b7c9f2ec11bf8ad56a24324a53792955edc77d..37951fa7587c8a200f4733e5a46575461f5026cd 100644 --- a/paddle/operators/elementwise_add_op.cc +++ b/paddle/operators/elementwise_add_op.cc @@ -21,7 +21,7 @@ class ElementwiseAddOpMaker : public ElementwiseOpMaker { public: ElementwiseAddOpMaker(OpProto* proto, OpAttrChecker* op_checker) : ElementwiseOpMaker(proto, op_checker) { - SetComment("Add", "$Out = X + Y$"); + SetComment("Add", "Out = X + Y"); AddComment(comment_); } }; diff --git a/paddle/operators/elementwise_div_op.cc b/paddle/operators/elementwise_div_op.cc index 1fa960866fa2066a351ef2e65a3c77cf8b6595f7..6ebd58b1b3dd79a465e70a24f7aab56261290bf6 100644 --- a/paddle/operators/elementwise_div_op.cc +++ b/paddle/operators/elementwise_div_op.cc @@ -21,7 +21,7 @@ class ElementwiseDivOpMaker : public ElementwiseOpMaker { public: ElementwiseDivOpMaker(OpProto* proto, OpAttrChecker* op_checker) : ElementwiseOpMaker(proto, op_checker) { - SetComment("Div", "$Out = X / Y$"); + SetComment("Div", "Out = X / Y"); AddComment(comment_); } }; diff --git a/paddle/operators/elementwise_mul_op.cc b/paddle/operators/elementwise_mul_op.cc index a6d11736194cb79bdc247c721acf8bda9c81dbe5..450dd05c796e22794315274b73398e85c8145940 100644 --- a/paddle/operators/elementwise_mul_op.cc +++ b/paddle/operators/elementwise_mul_op.cc @@ -22,7 +22,7 @@ class ElementwiseMulOpMaker : public ElementwiseOpMaker { public: ElementwiseMulOpMaker(OpProto* proto, OpAttrChecker* op_checker) : ElementwiseOpMaker(proto, op_checker) { - SetComment("Mul", "$Out = X \\odot\\ Y$"); + SetComment("Mul", "Out = X \\odot\\ Y"); AddComment(comment_); } }; diff --git a/paddle/operators/elementwise_op.h b/paddle/operators/elementwise_op.h index f308ee05e11210540e41cda4b9a896f9f96c4730..a342595b546bfca1a344cf8a549597df6a29adec 100644 --- a/paddle/operators/elementwise_op.h +++ b/paddle/operators/elementwise_op.h @@ -58,7 +58,8 @@ Limited Elementwise {name} Operator. The equation is: -{equation} +.. math:: + {equation} X is a tensor of any dimension and the dimensions of tensor Y must be smaller than or equal to the dimensions of X. @@ -71,15 +72,16 @@ For case 2: Y will be broadcasted to match the shape of X and axis should be the starting dimension index for broadcasting Y onto X. -example: - shape(X) = (2, 3, 4, 5), shape(Y) = (,) - shape(X) = (2, 3, 4, 5), shape(Y) = (5,) - shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) - shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 - shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 +For example + .. code-block:: python -Both the input X and Y can carry the LoD (Level of Details) information, -or not. But the output only shares the LoD information with input X. + shape(X) = (2, 3, 4, 5), shape(Y) = (,) + shape(X) = (2, 3, 4, 5), shape(Y) = (5,) + shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) + shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 + shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 + +Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X. )DOC"; AddComment(comment_); diff --git a/paddle/operators/elementwise_sub_op.cc b/paddle/operators/elementwise_sub_op.cc index 2a8d0845b1800277a7d3cd6ff6c5c984e92197ee..d3c51f0a697b7cb07a46871cdba2e84e902fd0f2 100644 --- a/paddle/operators/elementwise_sub_op.cc +++ b/paddle/operators/elementwise_sub_op.cc @@ -21,7 +21,7 @@ class ElementwiseSubOpMaker : public ElementwiseOpMaker { public: ElementwiseSubOpMaker(OpProto* proto, OpAttrChecker* op_checker) : ElementwiseOpMaker(proto, op_checker) { - SetComment("Sub", "$Out = X - Y$"); + SetComment("Sub", "Out = X - Y"); AddComment(comment_); } }; diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc index 9331c7b563491902b2824898766cacb9bfdee2d9..55b33343af43802e1b6b95a32603bfee806c9764 100644 --- a/paddle/operators/recv_op.cc +++ b/paddle/operators/recv_op.cc @@ -24,7 +24,8 @@ limitations under the License. */ #include "paddle/framework/lod_tensor.h" #include "paddle/framework/op_registry.h" #include "paddle/framework/proto_desc.h" -#include "paddle/operators/detail/send_recv_impl.h" +#include "paddle/operators/detail/grpc_server.h" +#include "paddle/operators/detail/sendrecvop_utils.h" #include "paddle/operators/detail/simple_block_queue.h" #define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV" @@ -32,6 +33,11 @@ limitations under the License. */ namespace paddle { namespace operators { +void RunServer(std::shared_ptr service) { + service->RunSyncUpdate(); + VLOG(4) << "RunServer thread end"; +} + static void CreateTensorFromMessageType(framework::Variable *var, sendrecv::VarType var_type) { if (var_type == sendrecv::VarType::LOD_TENSOR) { @@ -46,18 +52,6 @@ static void CreateTensorFromMessageType(framework::Variable *var, } } -void RunServer(Server **rpc_server, - std::shared_ptr service, - const std::string &server_address) { - ServerBuilder builder; - builder.AddListeningPort(server_address, grpc::InsecureServerCredentials()); - builder.RegisterService(service.get()); - std::unique_ptr server(builder.BuildAndStart()); - *rpc_server = server.get(); - LOG(INFO) << "Server listening on " << server_address; - server->Wait(); -} - class RecvOp : public framework::OperatorBase { public: RecvOp(const std::string &type, const framework::VariableNameMap &inputs, @@ -65,10 +59,9 @@ class RecvOp : public framework::OperatorBase { const framework::AttributeMap &attrs) : OperatorBase(type, inputs, outputs, attrs) { if (!rpc_service_) { - rpc_service_.reset(new detail::SendRecvServerImpl()); std::string endpoint = Attr("endpoint"); - server_thread_.reset( - new std::thread(RunServer, &rpc_server_, rpc_service_, endpoint)); + rpc_service_.reset(new detail::AsyncGRPCServer(endpoint)); + server_thread_.reset(new std::thread(RunServer, rpc_service_)); } } @@ -76,7 +69,7 @@ class RecvOp : public framework::OperatorBase { detail::MessageWithName term_msg; term_msg.first = LISTEN_TERMINATE_MESSAGE; rpc_service_->Push(term_msg); - rpc_server_->Shutdown(); + rpc_service_->ShutDown(); server_thread_->join(); } @@ -99,10 +92,12 @@ class RecvOp : public framework::OperatorBase { auto grad_list = Attr>("GradList"); auto trainer_count = Attr("Trainers"); size_t param_count = param_list.size(); + rpc_service_->Reset(); // TODO(typhoonzero): change this to a while_op for every cluster-batch. bool exit_flag = false; while (!exit_flag) { + // TODO(gognwb): simply this loop. // Get from multiple trainers, we don't care about order in which // the gradient arrives, just add suffix 0~n then average the gradient. for (size_t i = 0; i < param_count * trainer_count; ++i) { @@ -110,6 +105,7 @@ class RecvOp : public framework::OperatorBase { const detail::MessageWithName &v = rpc_service_->Get(); auto grad_var_name = v.first; if (grad_var_name == LISTEN_TERMINATE_MESSAGE) { + VLOG(4) << "received LISTEN_TERMINATE_MESSAGE and RunOp.Run() exit"; exit_flag = true; break; } @@ -118,10 +114,12 @@ class RecvOp : public framework::OperatorBase { if (it != grad_list.end()) { param_var_name = param_list[it - grad_list.begin()]; } else { - LOG(ERROR) << "grad have no paired param found!"; + LOG(ERROR) << "grad have no paired param found!\"" << grad_var_name + << "\""; } VLOG(3) << "recved grad: " << grad_var_name << " updating param: " << param_var_name; + auto *merged_grad = recv_scope.FindVar(grad_var_name); if (merged_grad == nullptr) { auto *ptr = recv_scope.Var(grad_var_name); @@ -141,9 +139,11 @@ class RecvOp : public framework::OperatorBase { auto &dev_ctx = *pool.Get(dev_place); detail::DeserializeFromMessage(v.second, dev_ctx, var); } + if (exit_flag) { break; } + rpc_service_->Reset(); std::string program_str = Attr("OptimizeProgram"); @@ -158,17 +158,14 @@ class RecvOp : public framework::OperatorBase { } catch (std::exception &e) { LOG(ERROR) << "run sub program error " << e.what(); } + rpc_service_->Done(); grads_counter_.clear(); } // while(true) } protected: - // grpc server instance to track status and gracefully shutdown. - // borrow an pointer from server thread. - Server *rpc_server_{nullptr}; - // grpc send/recv service implement to register. - std::shared_ptr rpc_service_; + std::shared_ptr rpc_service_; std::shared_ptr server_thread_; mutable std::unordered_map grads_counter_; }; diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc index 95c207221a7b34732eca4cfd07fed0a8f1671981..4d145250bdc73607c8817e20fdb753f4c96e2391 100644 --- a/paddle/operators/send_op.cc +++ b/paddle/operators/send_op.cc @@ -19,59 +19,45 @@ limitations under the License. */ #include "paddle/framework/lod_tensor.h" #include "paddle/framework/op_registry.h" -#include "paddle/operators/detail/send_recv_impl.h" -#include "paddle/operators/detail/simple_block_queue.h" +#include +#include "paddle/operators/detail/grpc_client.h" namespace paddle { namespace operators { -// TODO(typhoonzero): this is a simple implementation which only send -// one tensor class SendOp : public framework::OperatorBase { public: - SendOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorBase(type, inputs, outputs, attrs) { - // init client when the operator is created at runtime. - std::vector endpoints = - Attr>("endpoints"); - for (auto ep : endpoints) { - client_map_[ep].reset(new detail::RPCClient( - grpc::CreateChannel(ep, grpc::InsecureChannelCredentials()))); - } - } + SendOp(const std::string& type, const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs) + : OperatorBase(type, inputs, outputs, attrs) {} - void Run(const framework::Scope &scope, - const platform::Place &dev_place) const override { + void Run(const framework::Scope& scope, + const platform::Place& dev_place) const override { auto ins = Inputs("X"); auto outs = Outputs("Out"); std::vector epmap = Attr>("epmap"); - // TODO(typhoonzero): use async calls to send multiple variable asyncly. - for (size_t i = 0; i < ins.size(); ++i) { - bool ret = client_map_[epmap[i]]->SendVariable(scope, ins[i]); - if (!ret) { - LOG(ERROR) << "send variable error: " << ins[i]; - } + + // FIXME(gongwb): DeviceContext? + auto ctx = platform::CPUDeviceContext(); + for (size_t i = 0; i < ins.size(); i++) { + client_.AsyncSendVariable(epmap[i], ctx, scope, ins[i]); } - // TODO(typhoonzero): support async optimization - client_map_[epmap[0]]->Wait(); - for (size_t i = 0; i < outs.size(); ++i) { - bool ret = client_map_[epmap[i]]->GetVariable(scope, outs[i]); - if (!ret) { - LOG(ERROR) << "GetVariable error: " << outs[i]; - } + + for (size_t i = 0; i < outs.size(); i++) { + client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]); } + + client_.wait(); } - protected: - mutable std::unordered_map> - client_map_; + private: + mutable detail::RPCClient client_; }; class SendOpMaker : public framework::OpProtoAndCheckerMaker { public: - SendOpMaker(OpProto *proto, OpAttrChecker *op_checker) + SendOpMaker(OpProto* proto, OpAttrChecker* op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "(Tensor) Input tensor to be send").AsDuplicable(); AddOutput("Out", "(Tensor) Output tensor to get from server") diff --git a/paddle/operators/send_recv_op_test.cc b/paddle/operators/send_recv_op_test.cc index fa94424bf9e8e719ec0822268685b0806a109d21..ea091694798475dfd9631910a750405be950c20c 100644 --- a/paddle/operators/send_recv_op_test.cc +++ b/paddle/operators/send_recv_op_test.cc @@ -140,7 +140,7 @@ void StartServerNet(bool is_sparse) { TEST(SendRecvOp, CPUDense) { std::thread server_thread(StartServerNet, false); - sleep(3); // wait server to start + sleep(10); // wait server to start // local net f::Scope scope; p::CPUPlace place; diff --git a/paddle/operators/sequence_erase_op.cc b/paddle/operators/sequence_erase_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..d17b2686238b2d2f872331edfdbb095fb8693b87 --- /dev/null +++ b/paddle/operators/sequence_erase_op.cc @@ -0,0 +1,89 @@ +/* Copyright (c) 2016 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. */ + +#include "paddle/operators/sequence_erase_op.h" + +namespace paddle { +namespace operators { + +class SequenceEraseOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of SequenceEraseOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of SequenceEraseOp should not be null."); + auto x_dims = ctx->GetInputDim("X"); + PADDLE_ENFORCE(x_dims.size() == 2 && x_dims[1] == 1, + "Input(X) of SequenceEraseOp should be a 2-D LoDTensor " + "with the 2nd dimension equal to 1."); + ctx->SetOutputDim("Out", x_dims); + } +}; + +class SequenceEraseOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SequenceEraseOpMaker(OpProto* proto, OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(2-D LoDTensor with the 2nd dim. equal to 1) " + "Input LoDTensor of SequenceEraseOp."); + AddOutput("Out", + "(2-D LoDTensor with the 2nd dim. equal to 1) " + "Output LoDTensor of SequenceEraseOp."); + AddAttr>("tokens", + "(vector) Tokens need to be erased from " + "input sequences."); + AddComment(R"DOC( +Sequence Erase Operator. + +Sequence erase operator erases tokens specified by Attr(tokens) from the input +sequences Input(X), and outputs the remaining data and modifies the LoD +information at the same time. For example, given a 2-D LoDTensor + + X = [[2, 2, 6, 1, 3, 9, 6, 1, 0, 1]]^T + +with lod = [[0, 3, 6, 10]], there are three sequences in the input: + + X1 = [[2, 2, 6]]^T, X2 = [[1, 3, 9]]^T and X3 = [[6, 1, 0, 1]]^T. + +If the tokens to be erased are Attr(tokens) = [2, 3, 5], after the erasing +operation, the three sequences become + + X1' = [[6]]^T, X2' = [[1, 9]]^T and X3' = [[6, 1, 0, 1]]^T. + +Hence the LoDTensor Output(Out) should be + + Out = [[6, 1, 9, 6, 1, 0, 1]]^T, + +with lod = [[0, 1, 3, 7]]. + +An example usage for this operator is to remove the special tokens when +computing the edit distance between two strings, such as blank, start token, +and end token. +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(sequence_erase, ops::SequenceEraseOp, + ops::SequenceEraseOpMaker); +REGISTER_OP_CPU_KERNEL( + sequence_erase, + ops::SequenceEraseKernel); diff --git a/paddle/operators/sequence_erase_op.cu b/paddle/operators/sequence_erase_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..5da8eba3e1ac1fb85dfc65c2fd801574599e02d9 --- /dev/null +++ b/paddle/operators/sequence_erase_op.cu @@ -0,0 +1,133 @@ +/* Copyright (c) 2016 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. */ + +#include +#include +#include "paddle/operators/sequence_erase_op.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { +using platform::PADDLE_CUDA_NUM_THREADS; +using LoDTensor = framework::LoDTensor; + +template +__global__ void LabelErasedIdx(const T* in_dat, const int in_len, + const T* tokens, const int tokens_len, + int* num_erased) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < in_len) { + int erased = 0; + for (int i = 0; i < tokens_len; ++i) { + if (in_dat[index] == tokens[i]) { + erased = 1; + } + } + num_erased[index + 1] = erased; + if (index == 0) { + num_erased[0] = 0; + } + } +} + +template +__global__ void GetOutLod(const T* num_erased, const int* in_lod, + const int lod_len, int* out_lod0) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < lod_len) { + out_lod0[index] = in_lod[index] - num_erased[in_lod[index]]; + } +} + +template +__global__ void SetOutput(const T* in_dat, const int in_len, + const int* num_erased, T* out_dat) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < in_len) { + if (in_dat[index] != in_dat[index + 1]) { + out_dat[index - num_erased[index]] = in_dat[index]; + } + } +} + +template +class SequenceEraseOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* in = ctx.Input("X"); + auto* out = ctx.Output("Out"); + + auto lod = in->lod(); + PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now."); + PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(), + "The actual size mismatches with the LoD information."); + auto tokens = ctx.Attr>("tokens"); + auto tokens_len = tokens.size(); + auto in_len = in->numel(); + auto in_dat = in->data(); + auto lod0 = lod[0]; + + thrust::host_vector host_tokens(tokens_len); + for (size_t i = 0; i < tokens.size(); ++i) { + host_tokens[i] = tokens[i]; + } + thrust::device_vector dev_tokens = host_tokens; + thrust::device_vector num_erased(in_len + 1); + + T* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data()); + int* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data()); + + auto stream = ctx.cuda_device_context().stream(); + LabelErasedIdx<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, + PADDLE_CUDA_NUM_THREADS, 0, stream>>>( + in_dat, in_len, dev_tokens_ptr, tokens_len, num_erased_ptr); + thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(), + num_erased.begin() + 1); + + // Calc LoD + auto lod_len = lod0.size(); + thrust::host_vector host_lod(lod_len); + for (size_t i = 0; i < lod_len; ++i) { + host_lod[i] = lod0[i]; + } + thrust::device_vector dev_in_lod = host_lod; + thrust::device_vector dev_out_lod(lod_len); + int* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data()); + int* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data()); + GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, + PADDLE_CUDA_NUM_THREADS, 0, stream>>>( + num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr); + thrust::host_vector host_out_lod = dev_out_lod; + std::vector out_lod0(lod_len, 0); + for (size_t i = 0; i < lod_len; i++) { + out_lod0[i] = host_out_lod[i]; + } + framework::LoD out_lod; + out_lod.push_back(out_lod0); + out->set_lod(out_lod); + + // Set output + out->Resize({out_lod0.back(), 1}); + auto out_dat = out->mutable_data(ctx.GetPlace()); + SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, + PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len, + num_erased_ptr, out_dat); + } +}; + +} // namespace operators +} // namespace paddle + +REGISTER_OP_CUDA_KERNEL(sequence_erase, + paddle::operators::SequenceEraseOpCUDAKernel); diff --git a/paddle/operators/sequence_erase_op.h b/paddle/operators/sequence_erase_op.h new file mode 100644 index 0000000000000000000000000000000000000000..cb2d7be009dcbe0138818457249e95fbdd27fc0a --- /dev/null +++ b/paddle/operators/sequence_erase_op.h @@ -0,0 +1,70 @@ +/* Copyright (c) 2016 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. */ + +#pragma once + +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class SequenceEraseKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* in = ctx.Input("X"); + auto* out = ctx.Output("Out"); + + auto lod = in->lod(); + PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now."); + PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(), + "The actual size mismatches with the LoD information."); + auto tokens = ctx.Attr>("tokens"); + auto in_len = in->numel(); + auto in_dat = in->data(); + auto lod0 = lod[0]; + + std::vector num_erased(in_len + 1, 0); + std::vector out_lod0(1, 0); + for (size_t i = 0; i < lod0.size() - 1; ++i) { + size_t num_out = 0; + for (auto j = lod0[i] + 1; j <= lod0[i + 1]; ++j) { + num_erased[j] = num_erased[j - 1]; + if (std::find(tokens.begin(), tokens.end(), in_dat[j - 1]) != + tokens.end()) { + num_erased[j] += 1; + } else { + num_out += 1; + } + } + out_lod0.push_back(out_lod0.back() + num_out); + } + + auto out_len = in_len - num_erased[in_len]; + out->Resize({static_cast(out_len), 1}); + auto out_dat = out->mutable_data(ctx.GetPlace()); + + for (int64_t i = 0; i < in_len; ++i) { + if (num_erased[i] == num_erased[i + 1]) { + out_dat[i - num_erased[i]] = in_dat[i]; + } + } + framework::LoD out_lod; + out_lod.push_back(out_lod0); + out->set_lod(out_lod); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py index ccd5998e3592a1f5dc795ee24875c1aed230587e..422aa0a5ba2e490d9d0667c9eff7c46f1b751b4c 100644 --- a/python/paddle/v2/fluid/__init__.py +++ b/python/paddle/v2/fluid/__init__.py @@ -58,7 +58,7 @@ def __bootstrap__(): read_env_flags = ['use_pinned_memory', 'check_nan_inf'] if core.is_compile_gpu(): - read_env_flags.append('fraction_of_gpu_memory_to_use') + read_env_flags += ['fraction_of_gpu_memory_to_use', 'op_sync'] core.init_gflags([sys.argv[0]] + ["--tryfromenv=" + ",".join(read_env_flags)]) core.init_glog(sys.argv[0]) diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py index 2fb388acfc0a9f19b26c92de95de6a0dc0d9c018..3ef6b33192d9509b765173de8981bc7ff18486e5 100644 --- a/python/paddle/v2/fluid/framework.py +++ b/python/paddle/v2/fluid/framework.py @@ -236,6 +236,9 @@ class Variable(object): __repr__ = __str__ + def set_desc(self, input): + self.desc = input + @property def persistable(self): return self.desc.persistable() diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py index b1534c5a886db3c9694637e4a4195427c3538bb7..1fb6523f5523a173ac8821b5493173e8a8746463 100644 --- a/python/paddle/v2/fluid/layers/nn.py +++ b/python/paddle/v2/fluid/layers/nn.py @@ -14,7 +14,7 @@ __all__ = [ 'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d', 'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand', 'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', - 'sequence_first_step', 'sequence_last_step' + 'sequence_first_step', 'sequence_last_step', 'dropout' ] @@ -386,6 +386,21 @@ def cos_sim(X, Y, **kwargs): return out +def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs): + helper = LayerHelper('dropout', **kwargs) + out = helper.create_tmp_variable(dtype=x.dtype) + mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True) + helper.append_op( + type='dropout', + inputs={'X': [x]}, + outputs={'Out': [out], + 'Mask': [mask]}, + attrs={'dropout_prob': dropout_prob, + 'is_test': is_test, + 'seed': seed}) + return out + + def cross_entropy(input, label, **kwargs): """ **Cross Entropy Layer** @@ -968,7 +983,7 @@ def batch_norm(input, default_initializer=Constant(1.0)) bias = helper.create_parameter( - attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True) + attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True) mean = helper.create_global_variable( dtype=input.dtype, diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py index 544623c4bce0cb75ea727906c4879e986c8d1ce8..d3a5b70785947148d6e208b4d8dafec8bb52ff85 100644 --- a/python/paddle/v2/fluid/layers/ops.py +++ b/python/paddle/v2/fluid/layers/ops.py @@ -1,23 +1,12 @@ from ..registry import register_layer __activations__ = [ - 'abs', - 'ceil', - 'exp', - 'floor', - 'log', - 'relu', - 'round', - 'sigmoid', - 'sqrt', - 'square', - 'tanh', + 'abs', 'tanh', 'sigmoid', 'relu', 'sqrt', 'ceil', 'floor', 'log', 'round' ] __all__ = [ 'mean', 'mul', - 'dropout', 'reshape', 'scale', 'transpose', diff --git a/python/paddle/v2/fluid/memory_optimization_transpiler.py b/python/paddle/v2/fluid/memory_optimization_transpiler.py index 571fce7fac616356ae0368b407e90537caa42977..6800d7ddbb141a8bc2be10abe68ab86771c71156 100644 --- a/python/paddle/v2/fluid/memory_optimization_transpiler.py +++ b/python/paddle/v2/fluid/memory_optimization_transpiler.py @@ -3,6 +3,17 @@ import framework from framework import Program, default_main_program, Parameter, Variable import backward from backward import _rename_arg_ +from . import core + +dtype_to_size = { + core.DataType.FP16: 2, + core.DataType.FP32: 4, + core.DataType.FP64: 8, + core.DataType.INT16: 2, + core.DataType.INT32: 4, + core.DataType.INT64: 8, + core.DataType.BOOL: 1 +} class ControlFlowGraph(object): @@ -28,18 +39,33 @@ class ControlFlowGraph(object): block_size = program_desc.num_blocks() # TODO(qijun) handle Program with if/while operators - self.global_block = program_desc.block(0) - self.op_size = self.global_block.op_size() + self.global_block_desc = program_desc.block(0) + self.op_size = self.global_block_desc.op_size() op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)] self._add_connections(op_node_connections) - self.ops = [self.global_block.op(i) for i in range(self.op_size)] + self.ops = [self.global_block_desc.op(i) for i in range(self.op_size)] for i in range(self.op_size): self._uses[i].update(self.ops[i].input_arg_names()) self._defs[i].update(self.ops[i].output_arg_names()) + def _update_graph(self, old_name, new_name, begin_idx=0): + for i in range(begin_idx, self.op_size): + if old_name in self._uses[i]: + self._uses[i].remove(old_name) + self._uses[i].add(new_name) + if old_name in self._defs[i]: + self._defs[i].remove(old_name) + self._defs[i].add(new_name) + if old_name in self._live_in[i]: + self._live_in[i].remove(old_name) + self._live_out[i].add(new_name) + if old_name in self._live_out[i]: + self._live_out[i].remove(old_name) + self._live_out[i].add(new_name) + def _reach_fixed_point(self, live_in, live_out): if len(live_in) != len(self._live_in): return False @@ -79,30 +105,45 @@ class ControlFlowGraph(object): self.pool = [] for i in range(self.op_size): if self.pool: - out_pair = [(x, self.global_block.var(str(x)).shape()) + out_pair = [(x, self.global_block_desc.var(str(x)).shape()) for x in self._defs[i]] for x, x_shape in out_pair: - for index, cache_pair in enumerate(self.pool): - cache_var = cache_pair[0] - cache_shape = cache_pair[1] - if x_shape == cache_shape: - print( - "Hit Cache !!!! cache pool index is %d, var name is %s, cached var name is %s, var shape is %s " - % (index, x, cache_var, str(cache_shape))) - self.pool.pop(index) - _rename_arg_(self.ops, x, cache_var, begin_idx=i) - self._dataflow_analyze() - break + if not self.global_block_desc.var(str(x)).persistable(): + for index, cache_pair in enumerate(self.pool): + cache_var = cache_pair[0] + cache_shape = cache_pair[1] + if x_shape == cache_shape: + x_dtype = self.global_block_desc.var(str( + x)).dtype() + cache_dtype = self.global_block_desc.var( + str(cache_var)).dtype() + # TODO(qijun): actually, we should compare dtype_to_size[x_dtype] + # and dtype_to_size[cache_dtype] + if x_dtype == cache_dtype: + print( + "Hit Cache !!!! cache pool index is %d, var name is %s, cached var name is %s, var shape is %s " + % + (index, x, cache_var, str(cache_shape))) + self.pool.pop(index) + _rename_arg_( + self.ops, x, cache_var, begin_idx=i) + self._program.current_block().var(str( + x)).desc = self.global_block_desc.var( + str(cache_var)) + self._update_graph( + x, cache_var, begin_idx=i) + break in_diff, out_diff = self._get_diff(self._live_in[i], self._live_out[i]) can_optimize = filter( - lambda x: not self.global_block.var(str(x)).persistable(), + lambda x: not self.global_block_desc.var(str(x)).persistable(), in_diff) if can_optimize: for var_name in can_optimize: - self.pool.append(( - var_name, self.global_block.var(str(var_name)).shape())) + self.pool.append( + (var_name, + self.global_block_desc.var(str(var_name)).shape())) def get_program(self): return self._program diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py b/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py new file mode 100644 index 0000000000000000000000000000000000000000..bb339c440bd0d229d2ae348cf5a7745b16d156d5 --- /dev/null +++ b/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py @@ -0,0 +1,62 @@ +import numpy as np +import paddle.v2 as paddle +import paddle.v2.fluid as fluid +import os + +x = fluid.layers.data(name='x', shape=[13], dtype='float32') + +y_predict = fluid.layers.fc(input=x, size=1, act=None) + +y = fluid.layers.data(name='y', shape=[1], dtype='float32') + +cost = fluid.layers.square_error_cost(input=y_predict, label=y) +avg_cost = fluid.layers.mean(x=cost) + +sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) +optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) + +BATCH_SIZE = 20 + +train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.uci_housing.train(), buf_size=500), + batch_size=BATCH_SIZE) + +place = fluid.CPUPlace() +feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) +exe = fluid.Executor(place) + +t = fluid.DistributeTranspiler() +# all parameter server endpoints list for spliting parameters +pserver_endpoints = os.getenv("PSERVERS") +# server endpoint for current node +current_endpoint = os.getenv("SERVER_ENDPOINT") +# run as trainer or parameter server +training_role = os.getenv("TRAINING_ROLE", + "TRAINER") # get the training role: trainer/pserver +t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) + +if training_role == "PSERVER": + if not current_endpoint: + print("need env SERVER_ENDPOINT") + exit(1) + pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops) + exe.run(fluid.default_startup_program()) + exe.run(pserver_prog) +else: + trainer_prog = t.get_trainer_program() + + exe.run(fluid.default_startup_program()) + + PASS_NUM = 100 + for pass_id in range(PASS_NUM): + fluid.io.save_persistables(exe, "./fit_a_line.model/") + fluid.io.load_persistables(exe, "./fit_a_line.model/") + for data in train_reader(): + avg_loss_value, = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost]) + + if avg_loss_value[0] < 10.0: + exit(0) +exit(1) diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py new file mode 100644 index 0000000000000000000000000000000000000000..5fa5e0e5f34e6904e0e66d3ab4149cdfcffeb244 --- /dev/null +++ b/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py @@ -0,0 +1,225 @@ +import math + +import numpy as np +import paddle.v2 as paddle +import paddle.v2.dataset.conll05 as conll05 +import paddle.v2.fluid as fluid +import time +import os + +word_dict, verb_dict, label_dict = conll05.get_dict() +word_dict_len = len(word_dict) +label_dict_len = len(label_dict) +pred_len = len(verb_dict) + +mark_dict_len = 2 +word_dim = 32 +mark_dim = 5 +hidden_dim = 512 +depth = 8 +mix_hidden_lr = 1e-3 + +IS_SPARSE = True +PASS_NUM = 10 +BATCH_SIZE = 20 + +embedding_name = 'emb' + + +def load_parameter(file_name, h, w): + with open(file_name, 'rb') as f: + f.read(16) # skip header. + return np.fromfile(f, dtype=np.float32).reshape(h, w) + + +def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, + **ignored): + # 8 features + predicate_embedding = fluid.layers.embedding( + input=predicate, + size=[pred_len, word_dim], + dtype='float32', + is_sparse=IS_SPARSE, + param_attr='vemb') + + mark_embedding = fluid.layers.embedding( + input=mark, + size=[mark_dict_len, mark_dim], + dtype='float32', + is_sparse=IS_SPARSE) + + word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2] + emb_layers = [ + fluid.layers.embedding( + size=[word_dict_len, word_dim], + input=x, + param_attr=fluid.ParamAttr( + name=embedding_name, trainable=False)) for x in word_input + ] + emb_layers.append(predicate_embedding) + emb_layers.append(mark_embedding) + + hidden_0_layers = [ + fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers + ] + + hidden_0 = fluid.layers.sums(input=hidden_0_layers) + + lstm_0 = fluid.layers.dynamic_lstm( + input=hidden_0, + size=hidden_dim, + candidate_activation='relu', + gate_activation='sigmoid', + cell_activation='sigmoid') + + # stack L-LSTM and R-LSTM with direct edges + input_tmp = [hidden_0, lstm_0] + + for i in range(1, depth): + mix_hidden = fluid.layers.sums(input=[ + fluid.layers.fc(input=input_tmp[0], size=hidden_dim), + fluid.layers.fc(input=input_tmp[1], size=hidden_dim) + ]) + + lstm = fluid.layers.dynamic_lstm( + input=mix_hidden, + size=hidden_dim, + candidate_activation='relu', + gate_activation='sigmoid', + cell_activation='sigmoid', + is_reverse=((i % 2) == 1)) + + input_tmp = [mix_hidden, lstm] + + feature_out = fluid.layers.sums(input=[ + fluid.layers.fc(input=input_tmp[0], size=label_dict_len), + fluid.layers.fc(input=input_tmp[1], size=label_dict_len) + ]) + + return feature_out + + +def to_lodtensor(data, place): + seq_lens = [len(seq) for seq in data] + cur_len = 0 + lod = [cur_len] + for l in seq_lens: + cur_len += l + lod.append(cur_len) + flattened_data = np.concatenate(data, axis=0).astype("int64") + flattened_data = flattened_data.reshape([len(flattened_data), 1]) + res = fluid.LoDTensor() + res.set(flattened_data, place) + res.set_lod([lod]) + return res + + +def main(): + # define network topology + word = fluid.layers.data( + name='word_data', shape=[1], dtype='int64', lod_level=1) + predicate = fluid.layers.data( + name='verb_data', shape=[1], dtype='int64', lod_level=1) + ctx_n2 = fluid.layers.data( + name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1) + ctx_n1 = fluid.layers.data( + name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1) + ctx_0 = fluid.layers.data( + name='ctx_0_data', shape=[1], dtype='int64', lod_level=1) + ctx_p1 = fluid.layers.data( + name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1) + ctx_p2 = fluid.layers.data( + name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1) + mark = fluid.layers.data( + name='mark_data', shape=[1], dtype='int64', lod_level=1) + feature_out = db_lstm(**locals()) + target = fluid.layers.data( + name='target', shape=[1], dtype='int64', lod_level=1) + crf_cost = fluid.layers.linear_chain_crf( + input=feature_out, + label=target, + param_attr=fluid.ParamAttr( + name='crfw', learning_rate=mix_hidden_lr)) + avg_cost = fluid.layers.mean(x=crf_cost) + + # TODO(qiao) + # check other optimizers and check why out will be NAN + sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001) + optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) + + # TODO(qiao) + # add dependency track and move this config before optimizer + crf_decode = fluid.layers.crf_decoding( + input=feature_out, param_attr=fluid.ParamAttr(name='crfw')) + + chunk_evaluator = fluid.evaluator.ChunkEvaluator( + input=crf_decode, + label=target, + chunk_scheme="IOB", + num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0))) + + train_data = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.conll05.test(), buf_size=8192), + batch_size=BATCH_SIZE) + place = fluid.CPUPlace() + feeder = fluid.DataFeeder( + feed_list=[ + word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target + ], + place=place) + exe = fluid.Executor(place) + + t = fluid.DistributeTranspiler() + pserver_endpoints = os.getenv("PSERVERS") + # server endpoint for current node + current_endpoint = os.getenv("SERVER_ENDPOINT") + # run as trainer or parameter server + training_role = os.getenv( + "TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver + t.transpile( + optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) + + if training_role == "PSERVER": + if not current_endpoint: + print("need env SERVER_ENDPOINT") + exit(1) + pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops) + exe.run(fluid.default_startup_program()) + exe.run(pserver_prog) + elif training_role == "TRAINER": + trainer_prog = t.get_trainer_program() + start_time = time.time() + batch_id = 0 + exe.run(fluid.default_startup_program()) + embedding_param = fluid.global_scope().find_var( + embedding_name).get_tensor() + embedding_param.set( + load_parameter(conll05.get_embedding(), word_dict_len, word_dim), + place) + for pass_id in xrange(PASS_NUM): + chunk_evaluator.reset(exe) + for data in train_data(): + cost, precision, recall, f1_score = exe.run( + trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost] + chunk_evaluator.metrics) + pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval( + exe) + + if batch_id % 10 == 0: + print("avg_cost:" + str(cost) + " precision:" + str( + precision) + " recall:" + str(recall) + " f1_score:" + + str(f1_score) + " pass_precision:" + str( + pass_precision) + " pass_recall:" + str( + pass_recall) + " pass_f1_score:" + str( + pass_f1_score)) + if batch_id != 0: + print("second per batch: " + str((time.time( + ) - start_time) / batch_id)) + + batch_id = batch_id + 1 + + +if __name__ == '__main__': + main() diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py b/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py new file mode 100644 index 0000000000000000000000000000000000000000..db419e23abcd06ca39011b1bef078b0cafb5100e --- /dev/null +++ b/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py @@ -0,0 +1,110 @@ +from __future__ import print_function +import os +import numpy as np +import paddle.v2 as paddle +import paddle.v2.fluid as fluid + + +def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32, + hid_dim=32): + emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim]) + conv_3 = fluid.nets.sequence_conv_pool( + input=emb, + num_filters=hid_dim, + filter_size=3, + act="tanh", + pool_type="sqrt") + conv_4 = fluid.nets.sequence_conv_pool( + input=emb, + num_filters=hid_dim, + filter_size=4, + act="tanh", + pool_type="sqrt") + prediction = fluid.layers.fc(input=[conv_3, conv_4], + size=class_dim, + act="softmax") + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002) + optimize_ops, params_grads = adam_optimizer.minimize(avg_cost) + accuracy = fluid.evaluator.Accuracy(input=prediction, label=label) + return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads + + +def to_lodtensor(data, place): + seq_lens = [len(seq) for seq in data] + cur_len = 0 + lod = [cur_len] + for l in seq_lens: + cur_len += l + lod.append(cur_len) + flattened_data = np.concatenate(data, axis=0).astype("int64") + flattened_data = flattened_data.reshape([len(flattened_data), 1]) + res = fluid.LoDTensor() + res.set(flattened_data, place) + res.set_lod([lod]) + return res + + +def main(): + BATCH_SIZE = 100 + PASS_NUM = 5 + + word_dict = paddle.dataset.imdb.word_dict() + dict_dim = len(word_dict) + class_dim = 2 + + data = fluid.layers.data( + name="words", shape=[1], dtype="int64", lod_level=1) + label = fluid.layers.data(name="label", shape=[1], dtype="int64") + cost, accuracy, acc_out, optimize_ops, params_grads = convolution_net( + data, label, input_dim=dict_dim, class_dim=class_dim) + + train_data = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.imdb.train(word_dict), buf_size=1000), + batch_size=BATCH_SIZE) + place = fluid.CPUPlace() + exe = fluid.Executor(place) + + t = fluid.DistributeTranspiler() + + # all parameter server endpoints list for spliting parameters + pserver_endpoints = os.getenv("PSERVERS") + # server endpoint for current node + current_endpoint = os.getenv("SERVER_ENDPOINT") + # run as trainer or parameter server + training_role = os.getenv( + "TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver + t.transpile( + optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) + + exe.run(fluid.default_startup_program()) + + if training_role == "PSERVER": + if not current_endpoint: + print("need env SERVER_ENDPOINT") + exit(1) + pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops) + exe.run(pserver_prog) + elif training_role == "TRAINER": + trainer_prog = t.get_trainer_program() + feeder = fluid.DataFeeder(feed_list=[data, label], place=place) + + for pass_id in xrange(PASS_NUM): + accuracy.reset(exe) + for data in train_data(): + cost_val, acc_val = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[cost, acc_out]) + pass_acc = accuracy.eval(exe) + print("cost=" + str(cost_val) + " acc=" + str(acc_val) + + " pass_acc=" + str(pass_acc)) + if cost_val < 1.0 and pass_acc > 0.8: + exit(0) + else: + print("environment var TRAINER_ROLE should be TRAINER os PSERVER") + + +if __name__ == '__main__': + main() diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py index 59ed041e7fa1dd68c0f8d610f2575886442d1b4d..2b51a1f50473d0728b8180772f42584797143b4e 100644 --- a/python/paddle/v2/fluid/tests/test_parallel_op.py +++ b/python/paddle/v2/fluid/tests/test_parallel_op.py @@ -1,45 +1,156 @@ import unittest - -import paddle.v2.fluid.layers as layers import paddle.v2.fluid as fluid -from paddle.v2.fluid.framework import Program -from paddle.v2.fluid.executor import Executor -from paddle.v2.fluid.backward import append_backward -import numpy as np -import paddle.v2.fluid.core as core - - -class ParallelOpTest(unittest.TestCase): - def setUp(self): - x = layers.data( - shape=[-1, 30, 40], - dtype='float32', - name='x', - append_batch_size=False, - stop_gradient=False) - - places = layers.get_places(device_count=4) - pd = layers.ParallelDo(places=places) - - with pd.do(): - data = pd.read_input(x) - hidden = layers.fc(input=data, size=7) - pd.write_output(hidden) - data = pd() - loss = layers.mean(x=data) - sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) - sgd_optimizer.minimize(loss) - - exe = fluid.Executor(fluid.CPUPlace()) - exe.run(fluid.default_startup_program()) - exe.run(fluid.default_main_program(), - feed={ - x.name: np.random.uniform(0.1, 0.6, - (20, 30, 40)).astype("float32") - }) - - def test_forward(self): - pass +import numpy + + +class BaseParallelForTest(unittest.TestCase): + def run_test(self, callback, feed, fetch): + """ + Run the unittest for parallel.for + Args: + callback(callable): A callable function returns a generator. There + are two yields in the generator function. The first yield + returns the data layers, and the second yield returns the loss. + The modified data variables will be sent back during the first + yield. + + feed(dict): The executor feeding dictionary. + fetch(list|basestr): The fetch name lists. + + Returns: + None + + Raises: + AssertionError when the computation of cpu, parallel.for in cpu, + gpu, parallel.for in gpu are different. + + """ + cpu = fluid.CPUPlace() + result_cpu = self._run_test_impl_( + callback=callback, + feed=feed, + fetch=fetch, + place=cpu, + use_parallel=False) + result_cpu_parallel = self._run_test_impl_( + callback=callback, + feed=feed, + fetch=fetch, + place=cpu, + use_parallel=True) + if fluid.core.is_compile_gpu(): + gpu = fluid.CUDAPlace(0) + result_gpu = self._run_test_impl_( + callback=callback, + feed=feed, + fetch=fetch, + place=gpu, + use_parallel=False) + result_gpu_parallel = self._run_test_impl_( + callback=callback, + feed=feed, + fetch=fetch, + place=gpu, + use_parallel=True) + self._assert_same_(fetch, result_cpu, result_cpu_parallel, + result_gpu, result_gpu_parallel) + else: + self._assert_same_(fetch, result_cpu, result_cpu_parallel) + + def _run_test_impl_(self, callback, feed, fetch, place, use_parallel=False): + """ + Run a single test, returns the fetch values + Args: + place(Place): the computation place. + use_parallel(bool): Whether use parallel.for or not. + + Returns: + Fetched numpy arrays. + + """ + if isinstance(fetch, basestring): + fetch = [fetch] + main = fluid.Program() + startup = fluid.Program() + # Fix seed + main.random_seed = 10 + startup.random_seed = 10 + + with fluid.program_guard(main, startup): + generator = callback() + # Automatically insert parallel do if use_parallel = True + if use_parallel: + places = fluid.layers.get_places() + pd = fluid.layers.ParallelDo(places) + data = next(generator) + + if isinstance(data, fluid.Variable): + data = [data] + + with pd.do(): + ins = map(pd.read_input, data) + if len(ins) == 1: + ins = ins[0] + loss = generator.send(ins) # patch input + pd.write_output(loss) + + loss = pd() + else: + data = next(generator) + loss = generator.send(data) + self.assertIsNotNone(loss) + avg_loss = fluid.layers.mean(x=loss) + fluid.backward.append_backward(loss=avg_loss) + + exe = fluid.Executor(place) + exe.run(startup) + return exe.run(main, feed=feed, fetch_list=fetch) + + def _assert_same_(self, fetch, *args): + """ + Assert the return values of `run_test` are same. + Args: + fetch: Fetch list. Used for print error message + *args: The fetch result lists of each situations. + + Returns: + None + + Raises: + AssertionError + + """ + + def _impl_(a, b, fetch_id, item_id): + item_str = ['CPU', 'ParallelCPU', 'GPU', 'ParallelGPU'] + flag = numpy.allclose(a, b, rtol=0.1) + self.assertTrue(flag, "The {0} are different in {1}".format( + fetch[fetch_id], item_str[item_id])) + + for i, items in enumerate(zip(*args)): + self.assertGreater(len(items), 0) + for j in range(1, len(items)): + _impl_(items[0], items[j], fetch_id=i, item_id=j) + + +class ParallelOpTest(BaseParallelForTest): + def test_simple_fc(self): + def __network__(): + x = fluid.layers.data(shape=[784], dtype='float32', name='img') + # FIXME: This is a bug of parallel.do + x.stop_gradient = False + x = yield x + hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w') + loss = fluid.layers.mean(x=hidden) + yield loss + + self.run_test( + callback=__network__, + feed={ + 'img': + numpy.random.random(size=(128 * 3, 784)).astype('float32') + }, + fetch='fc1.w@GRAD') if __name__ == '__main__': diff --git a/python/paddle/v2/fluid/tests/test_sequence_erase_op.py b/python/paddle/v2/fluid/tests/test_sequence_erase_op.py new file mode 100644 index 0000000000000000000000000000000000000000..bf257fefea0d98c6f4d9860dbac4ccedf59bcdd9 --- /dev/null +++ b/python/paddle/v2/fluid/tests/test_sequence_erase_op.py @@ -0,0 +1,35 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def sequence_erase(in_seq, lod0, tokens): + new_lod0 = [0] + out_seq = [] + for i in range(0, len(lod0) - 1): + num_out = 0 + for dat in in_seq[lod0[i]:lod0[i + 1]]: + if dat not in tokens: + out_seq.append(dat) + num_out += 1 + new_lod0.append(new_lod0[-1] + num_out) + return np.array(out_seq).astype("int32"), new_lod0 + + +class TestSequenceEraseOp(OpTest): + def setUp(self): + self.op_type = "sequence_erase" + in_seq = np.random.randint(0, 10, (30, 1)).astype("int32") + lod = [[0, 9, 13, 24, 30]] + tokens = [2, 3, 5] + out_seq, new_lod0 = sequence_erase(in_seq, lod[0], tokens) + self.attrs = {'tokens': tokens} + self.inputs = {'X': (in_seq, lod)} + self.outputs = {'Out': (out_seq, [new_lod0])} + + def test_check_output(self): + self.check_output() + + +if __name__ == '__main__': + unittest.main() diff --git a/tools/manylinux1/Dockerfile.android b/tools/manylinux1/Dockerfile.android new file mode 100644 index 0000000000000000000000000000000000000000..b6cae228a0c45ab70ba8ecc80ae4df7e0fa5bdbc --- /dev/null +++ b/tools/manylinux1/Dockerfile.android @@ -0,0 +1,55 @@ +FROM ubuntu:16.04 +MAINTAINER PaddlePaddle Authors + +ARG UBUNTU_MIRROR +RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' + +# ENV variables +ARG ANDROID_ABI +ARG ANDROID_API + +ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"} +ENV ANDROID_API=${ANDROID_API:-21} + +ENV HOME=/root \ + ANDROID_NDK_HOME=/opt/android-ndk-linux \ + ANDROID_TOOLCHAINS_DIR=/opt/toolchains + +RUN apt-get update && \ + apt-get install -y \ + git python-dev python-pip python-numpy \ + wget curl tar unzip gcc g++ locales clang-format-3.8 swig cmake && \ + apt-get clean -y + +# Install Go and glide +RUN wget -qO- go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \ + tar -xz -C /usr/local && \ + mkdir /root/gopath && \ + mkdir /root/gopath/bin && \ + mkdir /root/gopath/src +ENV GOROOT=/usr/local/go GOPATH=/root/gopath +# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT. +ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin + +# git credential to skip password typing +RUN git config --global credential.helper store + +# Fix locales to en_US.UTF-8 +RUN localedef -i en_US -f UTF-8 en_US.UTF-8 + +RUN pip install --upgrade pip && \ + pip install -U 'protobuf==3.1.0' && \ + pip install -U wheel sphinx && \ + pip install pre-commit + +# Android NDK +RUN mkdir -p ${ANDROID_TOOLCHAINS_DIR} && \ + mkdir -p /opt/android-ndk-tmp && \ + cd /opt/android-ndk-tmp && \ + wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \ + unzip -q android-ndk-r14b-linux-x86_64.zip && \ + mv android-ndk-r14b ${ANDROID_NDK_HOME} && \ + rm -rf /opt/android-ndk-tmp + +CMD ["bash", "/paddle/paddle/scripts/docker/build_android.sh"] + diff --git a/tools/manylinux1/Dockerfile.x64 b/tools/manylinux1/Dockerfile.x64 new file mode 100644 index 0000000000000000000000000000000000000000..2c6ba650a5d7996bef212e88a16f2a159ca377e7 --- /dev/null +++ b/tools/manylinux1/Dockerfile.x64 @@ -0,0 +1,54 @@ +# NOTE The manylinux1 policy mandates CentOS-5. We replace it with CentOS-6 in +# order to satisfy the build of capnproto library (a nupic.core dependency), +# which requires some headers and symbols not present on CentOS-5 (e.g., +# signalfd.h, pipe2, O_NONBLOCK, SOCK_NONBLOCK, etc.). See +# https://github.com/sandstorm-io/capnproto/issues/350. +FROM nvidia/cuda: +MAINTAINER Numenta, based on the ManyLinux project + +ENV LC_ALL en_US.UTF-8 +ENV LANG en_US.UTF-8 +ENV LANGUAGE en_US.UTF-8 +ENV PATH /opt/rh/devtoolset-2/root/usr/bin:$PATH +ENV LD_LIBRARY_PATH /opt/rh/devtoolset-2/root/usr/lib64:/opt/rh/devtoolset-2/root/usr/lib:/usr/local/lib64:/usr/local/lib:${LD_LIBRARY_PATH} +ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig + +COPY build_scripts /build_scripts +RUN bash build_scripts/build.sh && rm -r build_scripts + +ENV SSL_CERT_FILE=/opt/_internal/certs.pem + +# for paddle +RUN wget --no-check-certificate -qO- https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \ + tar -xz -C /usr/local && \ + mkdir /root/gopath && \ + mkdir /root/gopath/bin && \ + mkdir /root/gopath/src + + +ENV GOROOT=/usr/local/go GOPATH=/root/gopath +ENV PATH=${GOROOT}/bin:${GOPATH}/bin:${PATH} + +# protobuf 3.1.0 +RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf/releases/download/v3.1.0/protobuf-cpp-3.1.0.tar.gz && \ + tar xzf protobuf-cpp-3.1.0.tar.gz && \ + cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz + + +RUN yum install -y sqlite-devel zlib-devel openssl-devel boost boost-devel pcre-devel vim tk-devel tkinter libtool + +RUN wget -O /root/requirements.txt https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt + +RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install -r /root/requirements.txt && \ + LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install -r /root/requirements.txt && \ + go get github.com/Masterminds/glide && \ + rm -rf /root/requirements.txt + +RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \ + LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install pre-commit 'ipython==5.3.0' opencv-python + +RUN wget -O /opt/swig-2.0.12.tar.gz https://sourceforge.net/projects/swig/files/swig/swig-2.0.12/swig-2.0.12.tar.gz/download && \ + cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz + +RUN mkdir -p /src && cd /src && git clone https://github.com/NVIDIA/nccl.git nccl && cd nccl &&\ + make -j `nproc` install && cd .. && rm -rf nccl diff --git a/tools/manylinux1/README.md b/tools/manylinux1/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cb0a9ac22cda6fb6f585ab8fd95179573c760f28 --- /dev/null +++ b/tools/manylinux1/README.md @@ -0,0 +1,30 @@ +# buildtools + +We release PaddlePaddle and PaddlePaddle Fluid as shared libraries, +which, we hope could be released as wheel packages on PyPI, so we need +to make sure that the build follows the +[manulinux1](https://www.python.org/dev/peps/pep-0513/) standard. + +The manylinux standard suggests building Python modules on an old +system, because that a module would anyway depend on some shared +libraries, and Linux's shared library standard states that those built +with newer version compilers cannot work with those with older +versions. The suggested building environment is as old as CentOS 5. +However, PaddlePaddle relies on CUDA, and the earlies version of +[CentOS works with CUDA is 6](https://hub.docker.com/r/nvidia/cuda/). +So, here we provide a Docker image basing on CentOS 6 and CUDA for +building PaddlePaddle and making the release supports "as-manylinux as +possible." or "sufficiently many Linux" according to [this +discussion](https://mail.python.org/pipermail/wheel-builders/2016-July/000175.html). + +The build output of our Docker image includes multiple wheel files -- +some contain the CPU-only binary, some others support CUDA; some are +compatible with the cp27m Python ABI, some others with cp27. + +To build these wheels, please run the following commands: + +```bash +git clone https://github.com/paddlepaddle/paddle +cd paddle/tools/manylinux1 +REPO=[yourrepo] ./build_all.sh +``` diff --git a/tools/manylinux1/build_all.sh b/tools/manylinux1/build_all.sh new file mode 100755 index 0000000000000000000000000000000000000000..097bedb5265d00f8aa362bb0272af633c97192ba --- /dev/null +++ b/tools/manylinux1/build_all.sh @@ -0,0 +1,26 @@ +#!/bin/bash +set -xe + +REPO="${REPO:-typhoon1986}" + +# NOTE: version matches are determined! +sed 's//7.5-cudnn5-devel-centos6/g' Dockerfile.x64 | \ +sed 's//NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52"/g'> Dockerfile.tmp +docker build -t ${REPO}/paddle_manylinux_devel:cuda7.5_cudnn5 -f Dockerfile.tmp . +docker push ${REPO}/paddle_manylinux_devel:cuda7.5_cudnn5 + +sed 's//8.0-cudnn5-devel-centos6/g' Dockerfile.x64 | \ +sed 's//NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62"/g'> Dockerfile.tmp +docker build -t ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn5 -f Dockerfile.tmp . +docker push ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn5 + +sed 's//8.0-cudnn7-devel-centos6/g' Dockerfile.x64 | \ +sed 's//NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62"/g'> Dockerfile.tmp + +docker build -t ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn7 -f Dockerfile.tmp . +docker push ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn7 + +sed 's//9.0-cudnn7-devel-centos6/g' Dockerfile.x64 | \ +sed 's//NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62 -gencode=arch=compute_70,code=sm_70"/g'> Dockerfile.tmp +docker build -t ${REPO}/paddle_manylinux_devel:cuda9.0_cudnn7 -f Dockerfile.tmp . +docker push ${REPO}/paddle_manylinux_devel:cuda9.0_cudnn7 diff --git a/tools/manylinux1/build_scripts/build.sh b/tools/manylinux1/build_scripts/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..93591fa9ddad8a78df344e1e912a5f1c7e93dfa4 --- /dev/null +++ b/tools/manylinux1/build_scripts/build.sh @@ -0,0 +1,152 @@ +#!/bin/bash +# Top-level build script called from Dockerfile + +# Stop at any error, show all commands +set -ex + +# Python versions to be installed in /opt/$VERSION_NO +# NOTE Only need python 2.7.11 for nupic.core/nupic.bindings at this time, so +# remove others to expedite build and reduce docker image size. The original +# manylinux docker image project builds many python versions. +# NOTE We added back 3.5.1, since auditwheel requires python 3.3+ +CPYTHON_VERSIONS="2.7.11 3.5.1" + +# openssl version to build, with expected sha256 hash of .tar.gz +# archive +OPENSSL_ROOT=openssl-1.0.2l +OPENSSL_HASH=ce07195b659e75f4e1db43552860070061f156a98bb37b672b101ba6e3ddf30c +EPEL_RPM_HASH=e5ed9ecf22d0c4279e92075a64c757ad2b38049bcf5c16c4f2b75d5f6860dc0d +DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc +PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb +CURL_ROOT=curl-7.49.1 +CURL_HASH=eb63cec4bef692eab9db459033f409533e6d10e20942f4b060b32819e81885f1 +AUTOCONF_ROOT=autoconf-2.69 +AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969 + +# Dependencies for compiling Python that we want to remove from +# the final image after compiling Python +PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel" + +# Libraries that are allowed as part of the manylinux1 profile +MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel mesa-libGL-devel libICE-devel libSM-devel ncurses-devel" + +# Get build utilities +MY_DIR=$(dirname "${BASH_SOURCE[0]}") +source $MY_DIR/build_utils.sh + +# EPEL support +yum -y install wget curl +curl -sLO https://dl.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm +check_sha256sum epel-release-6-8.noarch.rpm $EPEL_RPM_HASH + +# Dev toolset (for LLVM and other projects requiring C++11 support) +curl -sLO http://people.centos.org/tru/devtools-2/devtools-2.repo +check_sha256sum devtools-2.repo $DEVTOOLS_HASH +mv devtools-2.repo /etc/yum.repos.d/devtools-2.repo +rpm -Uvh --replacepkgs epel-release-6*.rpm +rm -f epel-release-6*.rpm + +# Development tools and libraries +yum -y install bzip2 make git patch unzip bison yasm diffutils \ + automake which file \ + kernel-devel-`uname -r` \ + devtoolset-2-binutils devtoolset-2-gcc \ + devtoolset-2-gcc-c++ devtoolset-2-gcc-gfortran \ + ${PYTHON_COMPILE_DEPS} + +# Install more recent version of cmake +# curl -O https://cmake.org/files/v3.8/cmake-3.8.1-Linux-x86_64.sh +# /bin/sh cmake-3.8.1-Linux-x86_64.sh --prefix=/usr/local --skip-license +# rm cmake-3.8.1-Linux-x86_64.sh + +wget -q https://cmake.org/files/v3.5/cmake-3.5.2.tar.gz && tar xzf cmake-3.5.2.tar.gz && \ +cd cmake-3.5.2 && ./bootstrap && \ +make -j4 && make install && cd .. && rm cmake-3.5.2.tar.gz + + +# Install newest autoconf +build_autoconf $AUTOCONF_ROOT $AUTOCONF_HASH +autoconf --version + +# Compile the latest Python releases. +# (In order to have a proper SSL module, Python is compiled +# against a recent openssl [see env vars above], which is linked +# statically. We delete openssl afterwards.) +build_openssl $OPENSSL_ROOT $OPENSSL_HASH +mkdir -p /opt/python +build_cpythons $CPYTHON_VERSIONS + +PY35_BIN=/opt/python/cp35-cp35m/bin +# NOTE Since our custom manylinux image builds pythons with shared +# libpython, we need to add libpython's dir to LD_LIBRARY_PATH before running +# python. +ORIGINAL_LD_LIBRARY_PATH="${LD_LIBRARY_PATH}" +LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib" + +# Our openssl doesn't know how to find the system CA trust store +# (https://github.com/pypa/manylinux/issues/53) +# And it's not clear how up-to-date that is anyway +# So let's just use the same one pip and everyone uses +LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib" $PY35_BIN/pip install certifi +ln -s $($PY35_BIN/python -c 'import certifi; print(certifi.where())') \ + /opt/_internal/certs.pem +# If you modify this line you also have to modify the versions in the +# Dockerfiles: +export SSL_CERT_FILE=/opt/_internal/certs.pem + +# Install newest curl +build_curl $CURL_ROOT $CURL_HASH +rm -rf /usr/local/include/curl /usr/local/lib/libcurl* /usr/local/lib/pkgconfig/libcurl.pc +hash -r +curl --version +curl-config --features + +# Now we can delete our built SSL +rm -rf /usr/local/ssl + +# Install patchelf (latest with unreleased bug fixes) +curl -sLO https://nipy.bic.berkeley.edu/manylinux/patchelf-0.9njs2.tar.gz +check_sha256sum patchelf-0.9njs2.tar.gz $PATCHELF_HASH +tar -xzf patchelf-0.9njs2.tar.gz +(cd patchelf-0.9njs2 && ./configure && make && make install) +rm -rf patchelf-0.9njs2.tar.gz patchelf-0.9njs2 + +# Install latest pypi release of auditwheel +LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib" $PY35_BIN/pip install auditwheel +ln -s $PY35_BIN/auditwheel /usr/local/bin/auditwheel + +# Clean up development headers and other unnecessary stuff for +# final image +yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \ + avahi freetype bitstream-vera-fonts \ + ${PYTHON_COMPILE_DEPS} > /dev/null 2>&1 +yum -y install ${MANYLINUX1_DEPS} +yum -y clean all > /dev/null 2>&1 +yum list installed +# we don't need libpython*.a, and they're many megabytes +find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f +# Strip what we can -- and ignore errors, because this just attempts to strip +# *everything*, including non-ELF files: +find /opt/_internal -type f -print0 \ + | xargs -0 -n1 strip --strip-unneeded 2>/dev/null || true +# We do not need the Python test suites, or indeed the precompiled .pyc and +# .pyo files. Partially cribbed from: +# https://github.com/docker-library/python/blob/master/3.4/slim/Dockerfile +find /opt/_internal \ + \( -type d -a -name test -o -name tests \) \ + -o \( -type f -a -name '*.pyc' -o -name '*.pyo' \) \ + -print0 | xargs -0 rm -f + +for PYTHON in /opt/python/*/bin/python; do + # Add matching directory of libpython shared library to library lookup path + LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname $(dirname ${PYTHON}))/lib" + + # Smoke test to make sure that our Pythons work, and do indeed detect as + # being manylinux compatible: + LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname $(dirname ${PYTHON}))/lib" $PYTHON $MY_DIR/manylinux1-check.py + # Make sure that SSL cert checking works + LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname $(dirname ${PYTHON}))/lib" $PYTHON $MY_DIR/ssl-check.py +done + +# Restore LD_LIBRARY_PATH +LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}" diff --git a/tools/manylinux1/build_scripts/build_utils.sh b/tools/manylinux1/build_scripts/build_utils.sh new file mode 100755 index 0000000000000000000000000000000000000000..10422ae3bd00f4e0dd059af0384f8cc17e4b7855 --- /dev/null +++ b/tools/manylinux1/build_scripts/build_utils.sh @@ -0,0 +1,173 @@ +#!/bin/bash +# Helper utilities for build + +PYTHON_DOWNLOAD_URL=https://www.python.org/ftp/python +# XXX: the official https server at www.openssl.org cannot be reached +# with the old versions of openssl and curl in Centos 5.11 hence the fallback +# to the ftp mirror: +# OPENSSL_DOWNLOAD_URL=ftp://ftp.openssl.org/source +OPENSSL_DOWNLOAD_URL=https://www.openssl.org/source +# Ditto the curl sources +CURL_DOWNLOAD_URL=http://curl.askapache.com/download + +GET_PIP_URL=https://bootstrap.pypa.io/get-pip.py + +AUTOCONF_DOWNLOAD_URL=http://ftp.gnu.org/gnu/autoconf + + +function check_var { + if [ -z "$1" ]; then + echo "required variable not defined" + exit 1 + fi +} + + +function lex_pyver { + # Echoes Python version string padded with zeros + # Thus: + # 3.2.1 -> 003002001 + # 3 -> 003000000 + echo $1 | awk -F "." '{printf "%03d%03d%03d", $1, $2, $3}' +} + + +function do_cpython_build { + local py_ver=$1 + check_var $py_ver + local ucs_setting=$2 + check_var $ucs_setting + tar -xzf Python-$py_ver.tgz + pushd Python-$py_ver + if [ "$ucs_setting" = "none" ]; then + unicode_flags="" + dir_suffix="" + else + local unicode_flags="--enable-unicode=$ucs_setting" + local dir_suffix="-$ucs_setting" + fi + local prefix="/opt/_internal/cpython-${py_ver}${dir_suffix}" + mkdir -p ${prefix}/lib + # -Wformat added for https://bugs.python.org/issue17547 on Python 2.6 + + # NOTE --enable-shared for generating libpython shared library needed for + # linking of some of the nupic.core test executables. + CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null + make -j2 > /dev/null + make install > /dev/null + popd + echo "ZZZ looking for libpython" + find / -name 'libpython*.so*' + rm -rf Python-$py_ver + # Some python's install as bin/python3. Make them available as + # bin/python. + if [ -e ${prefix}/bin/python3 ]; then + ln -s python3 ${prefix}/bin/python + fi + # NOTE Make libpython shared library visible to python calls below + LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python get-pip.py + LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/pip install wheel + local abi_tag=$(LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python ${MY_DIR}/python-tag-abi-tag.py) + ln -s ${prefix} /opt/python/${abi_tag} +} + + +function build_cpython { + local py_ver=$1 + check_var $py_ver + check_var $PYTHON_DOWNLOAD_URL + wget -q $PYTHON_DOWNLOAD_URL/$py_ver/Python-$py_ver.tgz + if [ $(lex_pyver $py_ver) -lt $(lex_pyver 3.3) ]; then + # NOTE We only need wide unicode for nupic.bindings wheel + do_cpython_build $py_ver ucs2 + do_cpython_build $py_ver ucs4 + else + do_cpython_build $py_ver none + fi + rm -f Python-$py_ver.tgz +} + + +function build_cpythons { + check_var $GET_PIP_URL + curl -sLO $GET_PIP_URL + for py_ver in $@; do + build_cpython $py_ver + done + rm get-pip.py +} + + +function do_openssl_build { + ./config no-ssl2 no-shared -fPIC --prefix=/usr/local/ssl > /dev/null + make > /dev/null + make install > /dev/null +} + + +function check_sha256sum { + local fname=$1 + check_var ${fname} + local sha256=$2 + check_var ${sha256} + + echo "${sha256} ${fname}" > ${fname}.sha256 + sha256sum -c ${fname}.sha256 + rm ${fname}.sha256 +} + + +function build_openssl { + local openssl_fname=$1 + check_var ${openssl_fname} + local openssl_sha256=$2 + check_var ${openssl_sha256} + check_var ${OPENSSL_DOWNLOAD_URL} + curl -sLO ${OPENSSL_DOWNLOAD_URL}/${openssl_fname}.tar.gz + check_sha256sum ${openssl_fname}.tar.gz ${openssl_sha256} + tar -xzf ${openssl_fname}.tar.gz + (cd ${openssl_fname} && do_openssl_build) + rm -rf ${openssl_fname} ${openssl_fname}.tar.gz +} + + +function do_curl_build { + LIBS=-ldl ./configure --with-ssl --disable-shared > /dev/null + make > /dev/null + make install > /dev/null +} + + +function build_curl { + local curl_fname=$1 + check_var ${curl_fname} + local curl_sha256=$2 + check_var ${curl_sha256} + check_var ${CURL_DOWNLOAD_URL} + curl -sLO ${CURL_DOWNLOAD_URL}/${curl_fname}.tar.bz2 + check_sha256sum ${curl_fname}.tar.bz2 ${curl_sha256} + tar -jxf ${curl_fname}.tar.bz2 + (cd ${curl_fname} && do_curl_build) + rm -rf ${curl_fname} ${curl_fname}.tar.bz2 +} + + +function do_standard_install { + ./configure > /dev/null + make > /dev/null + make install > /dev/null +} + + +function build_autoconf { + local autoconf_fname=$1 + check_var ${autoconf_fname} + local autoconf_sha256=$2 + check_var ${autoconf_sha256} + check_var ${AUTOCONF_DOWNLOAD_URL} + curl -sLO ${AUTOCONF_DOWNLOAD_URL}/${autoconf_fname}.tar.gz + check_sha256sum ${autoconf_fname}.tar.gz ${autoconf_sha256} + tar -zxf ${autoconf_fname}.tar.gz + (cd ${autoconf_fname} && do_standard_install) + rm -rf ${autoconf_fname} ${autoconf_fname}.tar.gz +} diff --git a/tools/manylinux1/build_scripts/manylinux1-check.py b/tools/manylinux1/build_scripts/manylinux1-check.py new file mode 100644 index 0000000000000000000000000000000000000000..47fd3d673be662d2229480ee650dc3799301c31e --- /dev/null +++ b/tools/manylinux1/build_scripts/manylinux1-check.py @@ -0,0 +1,56 @@ +# Logic copied from PEP 513 + + +def is_manylinux1_compatible(): + # Only Linux, and only x86-64 / i686 + from distutils.util import get_platform + if get_platform() not in ["linux-x86_64", "linux-i686"]: + return False + + # Check for presence of _manylinux module + try: + import _manylinux + return bool(_manylinux.manylinux1_compatible) + except (ImportError, AttributeError): + # Fall through to heuristic check below + pass + + # Check glibc version. CentOS 5 uses glibc 2.5. + return have_compatible_glibc(2, 5) + + +def have_compatible_glibc(major, minimum_minor): + import ctypes + + process_namespace = ctypes.CDLL(None) + try: + gnu_get_libc_version = process_namespace.gnu_get_libc_version + except AttributeError: + # Symbol doesn't exist -> therefore, we are not linked to + # glibc. + return False + + # Call gnu_get_libc_version, which returns a string like "2.5". + gnu_get_libc_version.restype = ctypes.c_char_p + version_str = gnu_get_libc_version() + # py2 / py3 compatibility: + if not isinstance(version_str, str): + version_str = version_str.decode("ascii") + + # Parse string and check against requested version. + version = [int(piece) for piece in version_str.split(".")] + assert len(version) == 2 + if major != version[0]: + return False + if minimum_minor > version[1]: + return False + return True + + +import sys +if is_manylinux1_compatible(): + print("%s is manylinux1 compatible" % (sys.executable, )) + sys.exit(0) +else: + print("%s is NOT manylinux1 compatible" % (sys.executable, )) + sys.exit(1) diff --git a/tools/manylinux1/build_scripts/python-tag-abi-tag.py b/tools/manylinux1/build_scripts/python-tag-abi-tag.py new file mode 100644 index 0000000000000000000000000000000000000000..301fbf07a47fef03c91d9dd5f49c2894a5971319 --- /dev/null +++ b/tools/manylinux1/build_scripts/python-tag-abi-tag.py @@ -0,0 +1,7 @@ +# Utility script to print the python tag + the abi tag for a Python +# See PEP 425 for exactly what these are, but an example would be: +# cp27-cp27mu + +from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag + +print("{0}{1}-{2}".format(get_abbr_impl(), get_impl_ver(), get_abi_tag())) diff --git a/tools/manylinux1/build_scripts/ssl-check.py b/tools/manylinux1/build_scripts/ssl-check.py new file mode 100644 index 0000000000000000000000000000000000000000..a85d91978c510cccd366c174c317e6a3bdb589bd --- /dev/null +++ b/tools/manylinux1/build_scripts/ssl-check.py @@ -0,0 +1,32 @@ +# cf. https://github.com/pypa/manylinux/issues/53 + +GOOD_SSL = "https://google.com" +BAD_SSL = "https://self-signed.badssl.com" + +import sys + +print("Testing SSL certificate checking for Python:", sys.version) + +if (sys.version_info[:2] < (2, 7) or sys.version_info[:2] < (3, 4)): + print("This version never checks SSL certs; skipping tests") + sys.exit(0) + +if sys.version_info[0] >= 3: + from urllib.request import urlopen + EXC = OSError +else: + from urllib import urlopen + EXC = IOError + +print("Connecting to %s should work" % (GOOD_SSL, )) +urlopen(GOOD_SSL) +print("...it did, yay.") + +print("Connecting to %s should fail" % (BAD_SSL, )) +try: + urlopen(BAD_SSL) + # If we get here then we failed: + print("...it DIDN'T!!!!!11!!1one!") + sys.exit(1) +except EXC: + print("...it did, yay.")