From 61a7361179baa4e04bc1341a500d38182eb8b13f Mon Sep 17 00:00:00 2001 From: chengmo Date: Fri, 15 May 2020 16:19:36 +0800 Subject: [PATCH] finish design --- doc/custom_dataset_reader.md | 86 ++++++++++- doc/design.md | 282 ++++++++++++++++++++++++++++++++++- doc/imgs/design.png | Bin 0 -> 467072 bytes 3 files changed, 366 insertions(+), 2 deletions(-) create mode 100644 doc/imgs/design.png diff --git a/doc/custom_dataset_reader.md b/doc/custom_dataset_reader.md index c6dba951..e1922ff2 100644 --- a/doc/custom_dataset_reader.md +++ b/doc/custom_dataset_reader.md @@ -1,6 +1,90 @@ # PaddleRec 自定义数据集及Reader -## 数据集及reader配置简介 +用户自定义数据集及配置异步Reader,需要关注以下几个步骤: + +* [数据集整理](#数据集整理) +* [在模型组网中加入输入占位符]() +* [Reader实现及调试]() +* [在yaml文件中配置Reader](#在yaml文件中配置reader) + +我们以CTR-DNN模型为例,给出了从数据整理,变量定义,Reader写法,调试的完整历程。 + +* [PaddleRec-数据及Reader快速上手]() + + +## 数据集整理 + +PaddleRec支持模型自定义数据集。 + +关于数据的tips: +1. 数据量: + + PaddleRec面向大规模数据设计,可以轻松支持亿级的数据读取,工业级的数据读写api:`dataset`在搜索、推荐、信息流等业务得到了充分打磨。 +2. 文件类型: + + 支持任意直接可读的文本数据,`dataset`同时支持`.gz`格式的文本压缩数据,无需额外代码,可直接读取。数据样本应以`\n`为标志,按行组织。 + +3. 文件存放位置: + + 文件通常存放在训练节点本地,但同时,`dataset`支持使用`hadoop`远程读取数据,数据无需下载到本地,为dataset配置hadoop相关账户及地址即可。 +4. 数据类型 + + Reader处理的是以行为单位的`string`数据,喂入网络的数据需要转为`int`,`float`的数值数据,不支持`string`喂入网络,不建议明文保存及处理训练数据。 +5. Tips + + Dataset模式下,训练线程与数据读取线程的关系强相关,为了多线程充分利用,`强烈建议将文件合理的拆为多个小文件`,尤其是在分布式训练场景下,可以均衡各个节点的数据量,同时加快数据的下载速度。 + +## 在模型组网中加入输入占位符 + +Reader读取文件后,产出的数据喂入网络,需要有占位符进行接收。占位符在Paddle中使用`fluid.data`或`fluid.layers.data`进行定义。`data`的定义可以参考[fluid.data](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/data_cn.html#data)以及[fluid.layers.data](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/layers_cn/data_cn.html#data)。 + +加入您希望输入三个数据,分别是维度32的数据A,维度变长的稀疏数据B,以及一个一维的标签数据C,并希望梯度可以经过该变量向前传递,则示例如下: + +数据A的定义: +```python +var_a = fluid.data(name='A', shape= [-1, 32], dtype='float32') +``` + +数据B的定义,变长数据的使用可以参考[LoDTensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/basic_concept/lod_tensor.html#cn-user-guide-lod-tensor): +```python +var_b = fluid.data(name='B', shape=[-1, 1], lod_level=1, dtype='int64') +``` + +数据C的定义: +```python +var_c = fluid.data(name='C', shape=[-1, 1], dtype='int32') +var_c.stop_gradient = False +``` + +当我们完成以上三个数据的定义后,在PaddleRec的模型定义中,还需将其加入model基类成员变量`self._data_var` + +```python +self._data_var.append(var_a) +self._data_var.append(var_b) +self._data_var.append(var_c) +``` +至此,我们完成了在组网中定义输入数据的工作。 + +## Reader的实现及调试 + +### Reader的实现范式 + +Reader需要一个单独的python文件进行描述 +具体流程如下: +1. 首先我们需要引入Reader基类 + + ```python + from paddlerec.core.reader import Reader + ``` +2. 创建一个子类,继承Reader的基类,训练所需Reader命名为`TrainerReader` +3. 在`init(self)`函数中声明一些在数据读取中会用到的变量,如示例代码中的`cont_min_`、`categorical_range_`等,必要时可以在`config.yaml`文件中配置变量,通过`env.get_global_env()`拿到。 +4. 继承并实现基类中的`generate_sample(self, line)`函数,逐行读取数据。该函数应返回一个可以迭代的reader方法(带有yield的函数不再是一个普通的函数,而是一个生成器generator,成为了可以迭代的对象,等价于一个数组、链表、文件、字符串etc.) +5. 在这个可以迭代的函数中,如示例代码中的`def reader()`,我们定义数据读取的逻辑。以行为单位的数据进行截取,转换及预处理。 +6. 最后,我们需要将数据整理为特定的格式,才能够被dataset正确读取,并灌入的训练的网络中。简单来说,数据的输出顺序与我们在网络中创建的`inputs`必须是严格一一对应的,并转换为类似字典的形式。在示例代码中,我们使用`zip`的方法将参数名与数值构成的元组组成了一个list,并将其yield输出。 + + + +## 在yaml文件中配置reader 以`ctr-dnn`模型举例: diff --git a/doc/design.md b/doc/design.md index 6b16a242..90a819fe 100644 --- a/doc/design.md +++ b/doc/design.md @@ -1,2 +1,282 @@ # PaddleRec 设计 -> 占位 \ No newline at end of file + + +## PaddleRec 整体设计概览 +PaddleRec将推荐模型的训练与预测流程,整体抽象为了五个大模块: + +* [Engine 流程执行引擎](#engine) +* [Trainer 流程具体定义](#trainer) +* [Model 模型组网定义](#model) +* [Reader 数据读取定义](#reader) +* [Metric 精度指标打印](#metric) + +层级结构,以及一键启动训练时的调用关系如下图所示: + +

+ +

+ +core的文件结构如下,后续分别对各个模块进行介绍。 +``` +.core +├── engine/ 运行引擎实现 +├── metrics/ 全局指标实现 +├── modules/ 自定义op实现 +├── trainers/ 运行流程实现 +├── utils/ 辅助工具 +├── factory.py 运行流程的注册 +├── layer.py 自定义op基类定义 +├── metric.py Metric基类定义 +├── model.py Model基类定义 +├── reader.py Reader基类定义 +└── trainer.py Trainer基类定义 +``` + + +## Engine + +Engine是整体训练的执行引擎,与组网逻辑及数据无关,只与当前运行模式、运行环境及运行设备有关。 + +运行模式具体是指: +- 单机运行 +- 分布式运行 +- 本地模拟分布式 + +运行环境是指: +- Linux +- Windows +- Mac + +运行设备是指: +- CPU +- GPU +- AI芯片 + +在用户调用`python -m paddlerec.run`时,首先会根据`yaml`文件中的配置信息选择合适的执行引擎, 以下代码位于[run.py](../run.py): +```python +engine_registry() +which_engine = get_engine(args) +engine = which_engine(args) +engine.run() +``` + +我们以`single engine`为例,概览engine的行为: +```python +def single_engine(args): + trainer = get_trainer_prefix(args) + "SingleTrainer" + single_envs = {} + single_envs["train.trainer.trainer"] = trainer + single_envs["train.trainer.threads"] = "2" + single_envs["train.trainer.engine"] = "single" + single_envs["train.trainer.device"] = args.device + single_envs["train.trainer.platform"] = envs.get_platform() + print("use {} engine to run model: {}".format(trainer, args.model)) + + set_runtime_envs(single_envs, args.model) + trainer = TrainerFactory.create(args.model) + return trainer +``` +single_engine被调用后,主要进行了以下两个工作: + +1. 根据`yaml`配置文件,设置了**当前进程的环境变量**,后续的所有流程都依赖于环境变量。 +2. 根据模型及环境,指定并初始化了运行流程所用的`Trainer` + +进一步细化第一步工作 +- 本地模拟分布式引擎会在单机环境变量的基础上,额外设置本地模拟分布式的环境变量,比如:为各个进程设置不同通信端口,分配ID。最后会启动多个`Trainer`完成本地模拟分布式的工作。 +- 分布式引擎会在单机环境变量的基础上,基于运行参数`-b --backend`所指定的脚本或配置文件,完成分布式任务的文件打包,上传,提交等操作。该脚本格式与分布式任务运行的集群有关,如MPI/K8S/PaddleCloud等,用户可以自定义分布式运行逻辑。 + +Engine的自定义实现,可以参考[local_cluster.py](../core/engine/local_cluster.py) + +## Trainer + +`Trainer`是训练与预测流程的具体实现,会run模型中定义的各个流程,与model、reader、metric紧密相关。PaddleRec以有限状态机的逻辑定义了训练中的各个阶段,不同的Trainer子类会分别实现阶段中的特殊需求。有限状态机的流程在`def processor_register()`中注册。 + +我们以SingleTrainer为例,概览Trainer行为: + +```python +class SingleTrainer(TranspileTrainer): + def processor_register(self): + self.regist_context_processor('uninit', self.instance) + self.regist_context_processor('init_pass', self.init) + self.regist_context_processor('startup_pass', self.startup) + if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader": + self.regist_context_processor('train_pass', self.dataset_train) + else: + self.regist_context_processor('train_pass', self.dataloader_train) + + self.regist_context_processor('infer_pass', self.infer) + self.regist_context_processor('terminal_pass', self.terminal) +``` + +SingleTrainer首先注册了完成任务所需的步骤,各步骤首先按照注册顺序加入`Trainer`基类中名为`status_processor`的字典,运行的先后顺序,可以在每个执行步骤中改变`context['status']`的值,指定下一步运行哪个步骤。 + +SingleTrainer指定了以下6个步骤: +1. uninit:默认排在首位,通过环境变量决定model的对象 +1. init_pass:调用model_的接口,生成模型的组网,初始化fetch及metric的变量 +2. startup_pass:初始化模型组网中的各个参数,run(fluid.default_startup_program) +3. train_pass:会根据环境分别调用`dataset`与`dataloader`进行训练的流程。 +4. infer_pass:在训练结束后,会对训练保存的模型在测试集上验证效果 +5. terminal_pass:打印全局变量及预测结果等自定义的信息。 + +Trainer的自定义实现,可以参照[single_trainer.py](../core/trainers/single_trainer.py) + +## Model + +Model定义了各个模型实现的范式,模型只要继承并实现基类中的函数,并给一些成员赋值,就可以保证模型被Trainer正确调用。 + +我们首先看一下Model基类中的部分重要定义,对模型的实现流程有初步概念。 + +```python +class Model(object): + __metaclass__ = abc.ABCMeta + + def __init__(self, config): + self._cost = None + self._metrics = {} + self._data_var = [] + self._infer_data_var = [] + self._infer_results = {} + self._data_loader = None + self._infer_data_loader = None + self._fetch_interval = 20 + self._namespace = "train.model" + self._platform = envs.get_platform() + + def get_inputs(self): + return self._data_var + + @abc.abstractmethod + def train_net(self): + pass + + @abc.abstractmethod + def infer_net(self): + pass + + def get_cost_op(self): + return self._cost + +``` + +每个模型都一定需要继承`def train_net`与`def infer_net`,并且给`self._data_var`与`self._cost`成员赋值,指定模型入口,实现组网的整体逻辑。若有更多或更复杂的需求,可以参照下面的接口,分别继承各个函数,并实现需要的功能: + +```python +def get_infer_inputs(self): + return self._infer_data_var + +def get_infer_results(self): + return self._infer_results + +def get_metrics(self): + return self._metrics + +def get_fetch_period(self): + return self._fetch_interval +``` + +model的具体实现,可以参考dnn的示例[model.py](../../models/rank/dnn/../../../paddlerec/core/model.py) + + +## Reader + +PaddleRec会根据运行环境,分别指定不同的数据IO方式。在Linux下,优先使用`Dataset`,Win及Mac优先使用`Dataloader`。 + + +Dataset的使用介绍可以参考[DatasetFactory](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/dataset_cn/DatasetFactory_cn.html) + +Dataloader的使用介绍可以参考[异步数据读取](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/data_preparing/use_py_reader.html) + + +考虑到以上两种高效的数据IO方式仍然有很高的学习门槛,PaddleRec将两种数据读取方式进行了更高层次的封装,用户需要实现的仅是每行数据的处理逻辑,剩下的工作交给PaddleRec的Reader基类完成。 + +首先浏览以下Reader基类的定义,有一个初步的印象: + +```python +class Reader(dg.MultiSlotDataGenerator): + __metaclass__ = abc.ABCMeta + + def __init__(self, config): + dg.MultiSlotDataGenerator.__init__(self) + + if os.path.isfile(config): + with open(config, 'r') as rb: + _config = yaml.load(rb.read(), Loader=yaml.FullLoader) + else: + raise ValueError("reader config only support yaml") + + envs.set_global_envs(_config) + envs.update_workspace() + + @abc.abstractmethod + def init(self): + pass + + @abc.abstractmethod + def generate_sample(self, line): + pass + +``` + +用户需要关注并实现的是`def init(self)`与`def generate_sample(self,line)`函数,分别执行数据读取中预处理所需变量的初始化,以及每一行string的切分及处理逻辑。 + +当用户定义好以上两个函数,完成自己的Reader后,PaddleRec分别使用 +- [dataset_instance.py](../core/utils/dataset_instance.py) +- [dataloader_instance.py](../core/utils/dataloader_instance.py) + +完成reader的构建工作。 + +Reader数据处理的逻辑,可以参考[criteo_reader.py](../../models/rank/../../paddlerec/models/rank/criteo_reader.py) + + + +## Metric + +训练必然伴随着训练指标的打印,当单机运行时,打印相关信息比较简单。但分布式训练时,单机指标与全局指标往往有很大diff,比如`auc`以及正逆序`pn`。PaddleRec面向大规模分布式训练,将指标打印的逻辑抽象出来单独实现,以解决分布式训练时全局指标打印的问题。 + +Metric基类定义了基本的接口,如下: +```python +class Metric(object): + __metaclass__ = abc.ABCMeta + + def __init__(self, config): + """ init """ + pass + + @abc.abstractmethod + def clear(self, scope, params): + """ + clear current value + Args: + scope: value container + params: extend varilable for clear + """ + pass + + @abc.abstractmethod + def calculate(self, scope, params): + """ + calculate result + Args: + scope: value container + params: extend varilable for clear + """ + pass + + @abc.abstractmethod + def get_result(self): + """ + Return: + result(dict) : calculate result + """ + pass + + @abc.abstractmethod + def get_result_to_string(self): + """ + Return: + result(string) : calculate result with string format, for output + """ + pass +``` + +全局指标的计算及输出,需要分别继承并实现以上四个成员函数。具体实现的例子,可以参考[auc_metric.py](../core/metrics/auc_metrics.py) \ No newline at end of file diff --git a/doc/imgs/design.png b/doc/imgs/design.png new file mode 100644 index 0000000000000000000000000000000000000000..740112697e5d6ce82521446ac05336cad33d6324 GIT binary patch literal 467072 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