add tdm_demo (#4546)

* add tdm_demo

PaddleRec/tdm/README.md

+# Paddle-TDM
+本代码库提供了基于PaddlePaddle实现的TDM召回算法，主要包含以下组成：
+
+- 基于fake数据集，适用于快速调试的paddle-tdm-demo模型。主要用于理解paddle-tdm的设计原理，高效上手设计适合您的使用场景的模型。
+- 基于User-Behavier数据集，复现TDM原始论文的paddle-tdm-dnn模型。
+- 基于User-Behavier数据集，复现TDM原始论文的paddle-tdm-attention模型。
+- 基于User-Behavier数据集，实现JTM原始论文设计思想的paddle-jtm模型。
+- Paddle-TDM-Serving，快速部署paddle-tdm模型，实现高效检索。
+
+以上内容将随paddle版本迭代不断更新，欢迎您关注该代码库。
+
+#
+## 设计思路
+
+### 基本概念
+TDM是为大规模推荐系统设计的、能承载任意先进模型来高效检索用户兴趣的推荐算法解决方案。TDM基于树结构，提出了一套对用户兴趣度量进行层次化建模与检索的方法论，使得系统能直接利高级深度学习模型在全库范围内检索用户兴趣。其基本原理是使用树结构对全库item进行索引，然后训练深度模型以支持树上的逐层检索，从而将大规模推荐中全库检索的复杂度由O(n)（n为所有item的量级）下降至O(log n)。
+
+### 核心问题
+
+1. 如何构建树结构？
+2. 如何基于树结构做深度学习模型的训练？
+3. 如何基于树及模型进行高效检索？
+
+### PaddlePaddle的TDM方案
+
+1. 树结构的数据，来源于各个业务的实际场景，构造方式各有不同，paddle-TDM一期暂不提供统一的树的构造流程，但会统一树构造好之后，输入paddle网络的数据组织形式。业务方可以使用任意工具构造自己的树，生成指定的数据格式，参与tdm网络训练。
+2. 网络训练中，有三个核心问题:
+   
+   - 如何组网？答：paddle封装了大量的深度学习OP，用户可以根据需求设计自己的网络结构。
+   - 训练数据如何组织？答：tdm的训练数据主要为：`user/query emb` 加 `item`的正样本，`item`需要映射到树的某个叶子节点。用户只需准备符合该构成的数据即可。负样本的生成，会基于用户提供的树结构，以及paddle提供的`tdm-sampler op`完成高效的负采样，并自动添加相应的label，参与tdm中深度学习模型的训练。
+   - 大规模的数据与模型训练如何实现？答：基于paddle优秀的大规模参数服务器分布式能力，可以实现高效的分布式训练。基于paddle-fleet api，学习门槛极低，且可以灵活的支持增量训练，流式训练等业务需求。
+3. 训练好模型后，可以基于paddle，将检索与打分等流程都融入paddle的组网中，生成inference_model与参数文件，基于PaddlePaddle的预测库或者PaddleLite进行快速部署与高效检索。
+
+#
\ No newline at end of file

PaddleRec/tdm/tdm_demo/README.md

+# Paddle-TDM-DEMO
+
+本代码仅作tdm组网示例，使用fake数据集，用于快速调研paddle-tdm。
+
+* [运行环境要求](#运行环境要求)
+* [树结构的准备](#树结构的准备)
+   * [名词概念](#名词概念)
+   * [树的准备流程](#树的准备流程)
+      * [Layer_list](#layer_list)
+      * [Travel_list](#travel_list)
+      * [Tree_Info](#tree_info)
+      * [Tree_Embedding](#tree_embedding)
+* [数据准备](#数据准备)
+* [TDM网络设计](#tdm网络设计)
+* [TDM组网细节](#tdm组网细节)
+   * [训练组网](#训练组网)
+      * [输入的定义](#输入的定义)
+      * [输入侧的组网](#输入侧的组网)
+      * [node的负采样组网](#node的负采样组网)
+      * [input与node的交互网络](#input与node的交互网络)
+      * [判别及loss计算组网](#判别及loss计算组网)
+   * [预测组网](#预测组网)
+      * [网络输入](#网络输入)
+      * [通过各层分类器的流程](#通过各层分类器的流程)
+* [TDM-Demo运行细节](#tdm-demo运行细节)
+   * [运行流程](#运行流程)
+   * [模型的加载与保存](#模型的加载与保存)
+   * [demo的训练运行方法](#demo的训练运行方法)
+   * [demo的预测运行方法](#demo的预测运行方法)
+   * [demo分布式运行方法](#demo分布式运行方法)
+   * [demo的部署及推理](#demo的部署及推理)
+
+
+## 运行环境要求
+- 目前仅支持Linux，如：`unbuntu`及`CentOS`
+- 目前仅支持python版本`2.7`
+- 请确保您的paddle版本高于`1.7.2`，可以利用pip升级您的paddle版本
+
+
+## 树结构的准备
+### 名词概念
+
+为防止概念混淆，让我们明确tdm中名词的概念：
+
+<p align="center">
+<img align="center" src="img/demo_tree.png">
+<p>
+
+- **item**：具有实际物理含义，是我们希望通过tdm检索最后得到的结果，如一个商品，一篇文章，一个关键词等等，在tdm模型中，item位于树的叶子节点。item有其自身的ID，我们姑且称之为 `item_id`。
+- **节点(node)**：tdm树的任意节点。我们完成聚类后，会生成一颗树，树的叶子节点对应着item。而非叶子节点，则是一种类别上的概括，从大方向的兴趣，不断细分到小方向的兴趣。我们希望这棵树的结构满足最大堆树的性质。同样，节点也有其自身的ID，我们称之为node_id。如上图，最左下方的节点，它的node_id是14，而对应的item_id是0.
+- **Node-Embedding**：注意，此处的Embedding，并非我们已有的item-embedding，而是构建完成的树的节点对应的Embedding，由item-embedding通过规则生成，是我们的网络主要训练的目标。ID范围为所有0->节点数-1。我们同时也需准备一个映射表，来告诉模型，item_id到node_id的映射关系。
+- **Travel**：是指叶子节点从root开始直到其自身的遍历路径，如上图，14号节点的Travel：0->1->3->7->14
+- **Layer**：指树的层，如上图，共有4层。
+  
+> Paddle-TDM在训练时，不会改动树的结构，只会改动Node-Embedding。
+
+
+### 树的准备流程
+
+让我们以上图给出的简单树结构为例，来介绍TDM的模型准备流程。假设我们已经完成了树的聚类，并得到了如上图所示的树结构：
+
+- 问题一：叶子节点的Embedding值是多少？答：叶子节点的node-embedding与对应的item的embedding值一致
+- 问题二：非叶子节点的Embedding值是多少？答：非叶子节点的Embedding值初始化目前有两种方案：1、随机初始化。2、使用其子节点的emb均值。
+- 问题三：树一定要求二叉树吗？答：没有这样的硬性要求。
+- 问题四：若有item不在最后一层，树不平衡怎么办？答：树尽量平衡，不在最后一层也没有关系，我们可以通过其他方式让网络正常训练。
+- 问题五：是每个用户都有一棵树，还是所有用户共享一颗树？答：只有一棵树，通过每一层的模型牵引节点的emb，使其尽量满足最大堆性质。
+
+完成以上步骤，我们已经得到了树的结构，与每个节点的全部信息，现在让我们将其转换为Paddle-TDM训练所需的格式。我们需要产出四个数据：
+#### Layer_list
+
+记录了每一层都有哪些节点。训练用
+```bash
+# Layer list
+1,2
+3,4,5,6
+7,8,9,10,11,12,13
+14,15,16,17,18,19,20,21,22,23,24,25
+```
+#### Travel_list
+
+记录每个叶子节点的Travel路径。训练用
+```bash
+# Travel list
+1,3,7,14
+1,3,7,15
+1,3,8,16
+...
+2,5,12,25
+2,6,13,0
+```
+
+#### Tree_Info
+
+记录了每个节点的信息，主要为：是否是item/item_id，所在层级，父节点，子节点。检索用
+```bash
+# Tree info
+0,0,0,1,2
+0,1,0,3,4
+0,1,0,5,6
+0,2,1,7,8
+...
+10,4,12,0,0
+11,4,12,0,0
+```
+
+#### Tree_Embedding
+
+记录所有节点的Embedding表，格式如正常表。训练及检索用
+
+以上数据设计的初衷是为了高效，在paddle网络中以Tensor的形式参与训练，运行时，不再需要进行频繁的树的结构的遍历，直接根据已有信息进行快速查找与训练。以上数据可以明文保存，但最终都需要转成ndarray，参与网络的初始化。
+结合示例树，数据可以组织如右，下面介绍一些细节：
+
+- Layer list从第2（index=1）层开始即可，因为0号节点不参与训练也不参与检索；
+- Travel list的按照item_id的顺序组织，如第一行对应着item_id=0的遍历信息，同样，也不需要包含0号节点；
+- Travel_list每行的长度必须相等，遇到不在最后一层的item，需要padding 0 直至长度和其他item一致；
+- Tree_info包含了0号节点的信息，主要考量是，当我们拿到node_id查找其信息时，可以根据id在该数据中寻找第id行；
+- Tree_info各列的含义是：itme_id（若无则为0），层级Layer，父节点node_id（无则为0），子节点node_id（若无则为0，若子节点数量不满，则需要paddding 0）
+
+## 数据准备
+如前所述，若我们关心的是输入一个user emb，得到他所感兴趣的item id，那我们就准备user_emb + 正样本item的格式的数据，负采样会通过paddle的tdm_sampler op得到。数据的准备不涉及树的结构，因而可以快速复用其他任务的训练数据来验证TDM效果。
+
+```bash
+# emb(float) \t item_id (int)
+-0.9480544328689575 0.8702829480171204 -0.5691063404083252 ...... -0.04391402751207352 -0.5352795124053955 -0.9972627758979797 0.9397293329238892   4690780
+```
+
+## TDM网络设计
+假设输入数据是 Emb + item_id，下面让我们开始介绍一个最简单的网络设计。
+
+<p align="center">
+<img align="center" src="img/demo_network.png">
+<p>
+
+上图给出了一个非常简单的TDM示例网络，没有添加任何复杂的逻辑，纯用DNN实现。
+TDM的组网，宏观上，可以概括为三个部分
+- 第一部分，输入侧的组网，如果想要对user/query进行一些预处理，或者添加Attention结构，通常都是在这一层次实现。
+- 第二部分，每层的输入与节点信息交互的组网，这一部分是将user/query的信息与node信息结合，在树的不同层下，进行不同粒度兴趣的学习。通常而言，第一部分与第二部分具有紧密的联系，可以统一为一个部分。
+- 第三部分，最终的判别组网，将每层交互得到的信息进行最终的概率判决。但这一层也不是必须的，并不要求所有层的信息都经过一个统一的分类器，可以各层拥有独立的概率判决器。为了逻辑划分更加清晰，我们在示例中添加了这个层次的组网，方便您更加直观的理解tdm网络。
+再次强调，该示例组网仅为展示tdm的基本运行逻辑，请基于这个框架，升级改进您自己的网络。
+
+## TDM组网细节
+
+### 训练组网
+
+训练组网中需要重点关注五个部分：
+1. 网络输入的定义
+2. 网络中输入侧的处理逻辑
+3. node的负采样组网
+4. input与node的交互网络
+5. 判别及loss计算组网
+
+
+#### 输入的定义
+
+首先简要介绍输入的定义：
+
+**demo模型，假设输入为两个元素：**
+> 一、user/query的emb表示，该emb应该来源于特征的组合在某个空间的映射（比如若干特征取emb后concat到一起），或其他预训练模型的处理结果（比如将明文query通过nlp预处理得到emb表示）
+    
+> 二、item的正样本，是发生了实际点击/购买/浏览等行为的item_id，与输入的user/query emb强相关，是我们之后通过预测想得到的结果。
+
+在paddle组网中，我们这样定义上面两个变量：
+```python
+def input_data(self):
+    """
+    指定tdm训练网络的输入变量
+    """
+    input_emb = fluid.data(
+        name="input_emb",
+        shape=[None, self.input_embed_size],
+        dtype="float32",
+    )
+
+    item_label = fluid.data(
+        name="item_label",
+        shape=[None, 1],
+        dtype="int64",
+    )
+
+    inputs = [input_emb] + [item_label]
+    return inputs
+```
+
+#### 输入侧的组网
+
+**输入侧组网由FC层组成**
+> 一、`input_fc`，主要功能是input_emb维度的压缩，只需一个fc即可。
+
+> 二、`layer_fc`，主要功能是将input_emb映射到不同的兴趣层空间，和当层的node学习兴趣关系。有多少层，就添加多少个fc。
+
+<p align="center">
+<img align="center" src="img/input-net.png">
+<p>
+
+在paddle组网中，我们这样快速实现输入侧组网：
+```python
+def input_trans_layer(self, input_emb):
+    """
+    输入侧训练组网
+    """
+    # 将input压缩到与node相同的维度
+    input_fc_out = fluid.layers.fc(
+        input=input_emb,
+        size=self.node_emb_size,
+        act=None,
+        param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
+        bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
+    )
+
+    # 将input_emb映射到各个不同层次的向量表示空间
+    input_layer_fc_out = [
+        fluid.layers.fc(
+            input=input_fc_out,
+            size=self.node_emb_size,
+            act="tanh",
+            param_attr=fluid.ParamAttr(
+                name="trans.layer_fc.weight." + str(i)),
+            bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias."+str(i)),
+        ) for i in range(self.max_layers)
+    ]
+
+    return input_layer_fc_out
+```
+
+#### node的负采样组网
+**tdm 负采样的核心是tdm_sampler OP**
+
+tdm_sampler的运行逻辑如下：
+
+1. 输入item_id，读travel_list，查表，得到该item_id对应的遍历路径（从靠近根节点的第一层一直往下直到存放该item的node）
+2. 读layer_list，查表，得到每层都有哪些node
+3. 循环：i = 0, 从第i层开始进行负采样
+    - 在item遍历路径上的node视为正样本，`positive_node_id`由`travel_list[item_id][i]`给出，其他同层的兄弟节点视为负样本，该层节点列表由`layer_list[i]`给出，如果`positive_node_id`不在`layer_list[i]`中，会提示错误。
+
+    - 在兄弟节点中进行随机采样，采样N个node，N由`neg_sampling_list[i]`的值决定，如果该值大于兄弟节点的数量，会提示错误。 采样结果不会重复，且不会采样到正样本。
+    
+    - 如果`output_positive=True`，则会同时输出正负样本，否则只输出负采样的结果
+    
+    - 生成该层`label`，shape与采样结果一致，正样本对应的label=1，负样本的label=0
+
+    - 生成该层`mask`，如果树是不平衡的，则有些item不会位于树的最后一层，所以遍历路径的实际长度会比其他item少，为了tensor维度一致，travel_list中padding了0。当遇到了padding的0时，tdm_sampler也会输出正常维度的采样结果，采样结果与label都为0。为了区分这部分虚拟的采样结果与真实采样结果，会给虚拟采样结果额外设置mask=0，如果是真实采样结果mask=1
+    - i += 1, 若i > layer_nums, break
+4. 对输出的采样结果、label、mask进行整理：
+   - 如果`output_list=False`，则会输出三个tensor(samping_result, label, mask)，shape形如`[batch_size, all_layer_sampling_nums, 1]`；
+   - 若`output_list=True`，则会输出三个`list[tensor,...,tensor]`，`sampling_result_list/label_list/mask_list`，`len(list)`等于层数，将采样结果按照分属哪一层进行拆分，每个tensor的shape形如`[batch_size, layer_i_sampling_nums,1]`
+
+```python
+# 根据输入的item的正样本在给定的树上进行负采样
+# sample_nodes 是采样的node_id的结果，包含正负样本
+# sample_label 是采样的node_id对应的正负标签
+# sample_mask 是为了保持tensor维度一致，padding部分的标签，若为0，则是padding的虚拟node_id
+sample_nodes, sample_label, sample_mask = fluid.contrib.layers.tdm_sampler(
+    x=item_label,
+    neg_samples_num_list=self.neg_sampling_list,
+    layer_node_num_list=self.layer_node_num_list,
+    leaf_node_num=self.leaf_node_num,
+    tree_travel_attr=fluid.ParamAttr(name="TDM_Tree_Travel"),
+    tree_layer_attr=fluid.ParamAttr(name="TDM_Tree_Layer"),
+    output_positive=self.output_positive,
+    output_list=True,
+    seed=0,
+    tree_dtype='int64',
+    dtype='int64'
+)
+
+# 查表得到每个节点的Embedding
+sample_nodes_emb = [
+    fluid.embedding(
+        input=sample_nodes[i],
+        is_sparse=True,
+        size=[self.node_nums, self.node_emb_size],
+        param_attr=fluid.ParamAttr(
+            name="TDM_Tree_Emb")
+    ) for i in range(self.max_layers)
+]
+
+# 此处进行reshape是为了之后层次化的分类器训练
+sample_nodes_emb = [
+    fluid.layers.reshape(sample_nodes_emb[i],
+                            [-1, self.neg_sampling_list[i] +
+                                self.output_positive, self.node_emb_size]
+                            ) for i in range(self.max_layers)
+]
+
+```
+
+#### input与node的交互网络
+**交互网络由FC层组成**
+
+主要包含两个流程：
+> 一、将输入进行维度上的`expand`，与采样得到的noed数量一致（当然也可以使用其他`broadcast`的网络结构）
+
+> 二、input_emb与node_emb进行`concat`，过FC，计算兴趣上的匹配关系
+
+<p align="center">
+<img align="center" src="img/dnn-net.png">
+<p>
+
+在paddle的组网中，我们这样实现这一部分的逻辑：
+
+```python
+def _expand_layer(self, input_layer, node, layer_idx):
+    # 扩展input的输入，使数量与node一致，
+    # 也可以以其他broadcast的操作进行代替
+    # 同时兼容了训练组网与预测组网
+    input_layer_unsequeeze = fluid.layers.unsqueeze(
+        input=input_layer, axes=[1])
+    if self.is_test:
+        input_layer_expand = fluid.layers.expand(
+            input_layer_unsequeeze, expand_times=[1, node.shape[1], 1])
+    else:
+        input_layer_expand = fluid.layers.expand(
+            input_layer_unsequeeze, expand_times=[1, node[layer_idx].shape[1], 1])
+    return input_layer_expand
+
+def classifier_layer(self, input, node):
+    # 扩展input，使维度与node匹配
+    input_expand = [
+        self._expand_layer(input[i], node, i) for i in range(self.max_layers)
+    ]
+
+    # 将input_emb与node_emb concat到一起过分类器FC
+    input_node_concat = [
+        fluid.layers.concat(
+            input=[input_expand[i], node[i]],
+            axis=2) for i in range(self.max_layers)
+    ]
+
+    hidden_states_fc = [
+        fluid.layers.fc(
+            input=input_node_concat[i],
+            size=self.node_emb_size,
+            num_flatten_dims=2,
+            act="tanh",
+            param_attr=fluid.ParamAttr(
+                name="cls.concat_fc.weight."+str(i)),
+            bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(i))
+        ) for i in range(self.max_layers)
+    ]
+
+    # 如果将所有层次的node放到一起计算loss，则需要在此处concat
+    # 将分类器结果以batch为准绳concat到一起，而不是layer
+    # 维度形如[batch_size, total_node_num, node_emb_size]
+    hidden_states_concat = fluid.layers.concat(hidden_states_fc, axis=1)
+    return hidden_states_concat
+```
+
+#### 判别及loss计算组网
+
+最终的判别组网会将所有层的输出打平放到一起，过`tdm.cls_fc`，再过`softmax_with_cross_entropy`层，计算cost，同时得到softmax的中间结果，计算acc或者auc。
+
+```python
+tdm_fc = fluid.layers.fc(input=layer_classifier_res,
+                            size=self.label_nums,
+                            act=None,
+                            num_flatten_dims=2,
+                            param_attr=fluid.ParamAttr(
+                                name="tdm.cls_fc.weight"),
+                            bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+
+# 将loss打平，放到一起计算整体网络的loss
+tdm_fc_re = fluid.layers.reshape(tdm_fc, [-1, 2])
+
+# 若想对各个层次的loss辅以不同的权重，则在此处无需concat
+# 支持各个层次分别计算loss，再乘相应的权重
+sample_label = fluid.layers.concat(sample_label, axis=1)
+sample_label.stop_gradient = True
+labels_reshape = fluid.layers.reshape(sample_label, [-1, 1])
+
+# 计算整体的loss并得到softmax的输出
+cost, softmax_prob = fluid.layers.softmax_with_cross_entropy(
+    logits=tdm_fc_re, label=labels_reshape, return_softmax=True)
+
+# 通过mask过滤掉虚拟节点的loss
+sample_mask = fluid.layers.concat(sample_mask, axis=1)
+sample_mask.stop_gradient = True
+mask_reshape = fluid.layers.reshape(sample_mask, [-1, 1])
+
+mask_index = fluid.layers.where(mask_reshape != 0)
+mask_cost = fluid.layers.gather_nd(cost, mask_index)
+
+# 计算该batch的均值loss，同时计算acc, 亦可在这里计算auc
+avg_cost = fluid.layers.reduce_mean(mask_cost)
+acc = fluid.layers.accuracy(input=softmax_prob, label=labels_reshape)
+```
+
+### 预测组网
+
+预测的整体流程类似于beamsearch。预测组网中需要重点关注三个部分：
+1. 网络输入的定义，及初始检索层的确定
+2. 串行的通过各层分类器的流程
+3. 每层选出topK及最终选出topK的方法
+
+#### 网络输入
+
+首先考虑这样一个问题，假设树是一颗十层的完全二叉树，我们要取topK=1024的召回结果，那么我们需不需要从树的第一层开始逐层计算fc与topK？答案显然是否定的，我们只需要计算最后一层的1024个节点即可。在开发的实验中，我们得到的结论是，tdm检索时，计算复杂度并不高，时间耗费中OP的调度占据了主要矛盾，通过模型裁剪，树剪枝，模型量化等都可以加快预测速度。
+
+因此infer组网中首先考虑了跳层与剪枝的实现：我们定义了三个变量，`input_emb`,`first_layer_node`,`first_layer_node_mask`作为网络的输入。
+- `input_emb`：预测时输入的user/query向量。
+- `first_layer_node`：检索时的起始node_id，是变长类型。这样设置的好处是：1、可以输入某一层所有节点node_id，从而在某一层开始进行检索。2、也可以设置为特定node_id，从指定的树分枝开始检索。输入的node_id应该位于同一层。
+- `first_layer_node_mask`：维度与`first_layer_node`相同，值一一对应。若node是叶子节点，则mask=1，否则设置mask=0。
+
+在demo网络中，我们设置为从某一层的所有节点开始进行检索。paddle组网对输入定义的实现如下：
+```python
+def input_data(self):
+    input_emb = fluid.layers.data(
+        name="input_emb",
+        shape=[self.input_embed_size],
+        dtype="float32",
+    )
+
+    # first_layer 与 first_layer_mask 对应着infer起始的节点
+    first_layer = fluid.layers.data(
+        name="first_layer_node",
+        shape=[1],
+        dtype="int64",
+        lod_level=1, #支持变长
+    )
+
+    first_layer_mask = fluid.layers.data(
+        name="first_layer_node_mask",
+        shape=[1],
+        dtype="int64",
+        lod_level=1,
+    )
+
+    inputs = [input_emb] + [first_layer] + [first_layer_mask]
+    return inputs
+```
+
+确定起始层的方式比较简单，比较topK的大小与当层节点数，选取第一个节点数大于等于topK的层作为起始层，取它的节点作为起始节点。代码如下：
+```python
+def create_first_layer(self, args):
+    """decide which layer to start infer"""
+    first_layer_id = 0
+    for idx, layer_node in args.layer_node_num_list:
+        if layer_node >= self.topK:
+            first_layer_id = idx
+            break
+    first_layer_node = self.layer_list[first_layer_id]
+    self.first_layer_idx = first_layer_id
+    return first_layer_node
+```
+
+#### 通过各层分类器的流程
+
+tdm的检索逻辑类似beamsearch，简单来说：在每一层计算打分，得到topK的节点，将这些节点的孩子节点作为下一层的输入，如此循环，得到最终的topK。但仍然有一些需要注意的细节，下面将详细介绍。
+
+- 问题一：怎么处理`input_emb`？
+  
+  - input_emb过`input_fc`，检索中，只需过一次即可:
+  ```python
+  nput_trans_emb = self.input_trans_net.input_fc_infer(input_emb)
+  ```
+  - 在通过每一层的分类器之前，过`layer_fc`，指定`layer_idx`，加载对应层的分类器，将输入映射到不同的兴趣粒度空间
+  ```python
+  input_fc_out = self.input_trans_net.layer_fc_infer(
+                input_trans_emb, layer_idx)
+  ```
+
+- 问题二：怎样实现beamsearch？
+
+    我们通过在每一层计算打分，计算topK并拿到对应的孩子节点，for循环这个过程实现beamsearch。
+    ```python
+    for layer_idx in range(self.first_layer_idx, self.max_layers):
+        # 确定当前层的需要计算的节点数
+        if layer_idx == self.first_layer_idx:
+            current_layer_node_num = len(self.first_layer_node)
+        else:
+            current_layer_node_num = current_layer_node.shape[1] * \
+                current_layer_node.shape[2]
+
+        current_layer_node = fluid.layers.reshape(
+            current_layer_node, [self.batch_size, current_layer_node_num])
+        current_layer_child_mask = fluid.layers.reshape(
+            current_layer_child_mask, [self.batch_size, current_layer_node_num])
+
+        # 查当前层node的emb
+        node_emb = fluid.embedding(
+            input=current_layer_node,
+            size=[self.node_nums, self.node_embed_size],
+            param_attr=fluid.ParamAttr(name="TDM_Tree_Emb"))
+
+        input_fc_out = self.input_trans_net.layer_fc_infer(
+            input_trans_emb, layer_idx)
+
+        # 过每一层的分类器
+        layer_classifier_res = self.layer_classifier.classifier_layer_infer(input_fc_out, node_emb, layer_idx)
+
+        # 过最终的判别分类器
+        tdm_fc = fluid.layers.fc(input=layer_classifier_res,
+                                    size=self.label_nums,
+                                    act=None,
+                                    num_flatten_dims=2,
+                                    param_attr=fluid.ParamAttr(
+                                        name="tdm.cls_fc.weight"),
+                                    bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+
+        prob = fluid.layers.softmax(tdm_fc)
+        positive_prob = fluid.layers.slice(
+            prob, axes=[2], starts=[1], ends=[2])
+        prob_re = fluid.layers.reshape(
+            positive_prob, [self.batch_size, current_layer_node_num])
+
+        # 过滤掉padding产生的无效节点（node_id=0）
+        node_zero_mask = fluid.layers.cast(current_layer_node, 'bool')
+        node_zero_mask = fluid.layers.cast(node_zero_mask, 'float')
+        prob_re = prob_re * node_zero_mask
+
+        # 在当前层的分类结果中取topK，并将对应的score及node_id保存下来
+        k = self.topK
+        if current_layer_node_num < self.topK:
+            k = current_layer_node_num
+        _, topk_i = fluid.layers.topk(prob_re, k)
+
+        # index_sample op根据下标索引tensor对应位置的值
+        # 若paddle版本>2.0，调用方式为paddle.index_sample
+        top_node = fluid.contrib.layers.index_sample(
+            current_layer_node, topk_i)
+        prob_re_mask = prob_re * current_layer_child_mask  # 过滤掉非叶子节点
+        topk_value = fluid.contrib.layers.index_sample(
+            prob_re_mask, topk_i)
+        node_score.append(topk_value)
+        node_list.append(top_node)
+
+        # 取当前层topK结果的孩子节点，作为下一层的输入
+        if layer_idx < self.max_layers - 1:
+            # tdm_child op 根据输入返回其 child 及 child_mask
+            # 若child是叶子节点，则child_mask=1，否则为0
+            current_layer_node, current_layer_child_mask = \
+                fluid.contrib.layers.tdm_child(x=top_node,
+                                            node_nums=self.node_nums,
+                                            child_nums=self.child_nums,
+                                            param_attr=fluid.ParamAttr(
+                                                    name="TDM_Tree_Info"),
+                                            dtype='int64')
+    ```
+
+- 问题三：怎样得到最终的topK个叶子节点？
+
+    在过每层分类器的过程中，我们保存了每层的topk节点，并将非叶子节点的打分置为了0，保存在`node_score`与`node_list`中。显然，我们需要召回的是topk个叶子节点，对所有层的叶子节点打分再计算一次topk，拿到结果。
+
+    ```python
+    total_node_score = fluid.layers.concat(node_score, axis=1)
+    total_node = fluid.layers.concat(node_list, axis=1)
+
+    # 考虑到树可能是不平衡的，计算所有层的叶子节点的topK
+    res_score, res_i = fluid.layers.topk(total_node_score, self.topK)
+    res_layer_node = fluid.contrib.layers.index_sample(total_node, res_i)
+    res_node = fluid.layers.reshape(res_layer_node, [-1, self.topK, 1])
+    ```
+
+- 问题四：现在拿到的是node_id，怎么转成item_id？
+
+    如果有额外的映射表，可以将node_id转为item_id，但也有更方便的方法。在生成`tree_info`时，我们保存了每个node_id相应的item_id信息，直接使用它，将node_id对应的tree_info行的数据的第零维的item_id切出来。
+
+    ```python
+    # 利用Tree_info信息，将node_id转换为item_id
+    tree_info = fluid.default_main_program().global_block().var("TDM_Tree_Info")
+    res_node_emb = fluid.layers.gather_nd(tree_info, res_node)
+
+    res_item = fluid.layers.slice(
+        res_node_emb, axes=[2], starts=[0], ends=[1])
+    res_item_re = fluid.layers.reshape(res_item, [-1, self.topK])
+    ```
+以上，我们便完成了训练及预测组网的全部部分。
+
+## TDM-Demo运行细节
+
+### 运行流程
+
+tdm-demo是使用paddle组网实现的，运行流程与普通的paddle运行流程一致。分为以下步骤：
+
+1. 组网
+2. 定义数据读取方式
+3. 设置optimizer
+4. 设置执行器
+5. 初始化模型参数/加载保存的参数
+6. 运行训练网络
+7. 保存训练的结果
+
+```python
+# 组网
+tdm_model = TdmTrainNet(args)
+inputs = tdm_model.input_data()
+avg_cost, acc = tdm_model.tdm(inputs)
+
+# 定义数据读取方式
+dataset = get_dataset(inputs, args)
+
+# 设置optimizer
+optimizer = fluid.optimizer.AdamOptimizer(
+        learning_rate=args.learning_rate,
+        lazy_mode=True)
+optimizer.minimize(avg_cost)
+
+# 设置执行器
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+
+# 初始化模型参数/加载保存的参数
+exe.run(fluid.default_startup_program())
+# args.load_model:
+# ......
+
+# 运行训练网络
+for epoch in range(args.epoch_num):
+    exe.train_from_dataset(
+        program=fluid.default_main_program(),
+        dataset=dataset,
+        fetch_list=[acc, avg_cost],
+        fetch_info=["Epoch {} acc".format(
+            epoch), "Epoch {} loss".format(epoch)],
+        print_period=10,
+        debug=False,
+    )
+
+# 保存训练结果
+model_path = os.path.join(args.model_files_path, "epoch_" + str(epoch))
+fluid.io.save_persistables(executor=exe, dirname=model_path)
+
+```
+
+
+### 模型的加载与保存
+
+我们在建树时，生成了树结构相应的四个文件。它们以明文或者npy的格式保存，在每次debug或训练时，读取的成本极高，可不可以只读取一次？答案是可以的，在第一次运行时，将他们保存为paddle的二进制模型文件，之后直接加载这个初始化的paddle模型即可。开发测试大数据模型时，往往可以将训练启动时间从10分钟以上压缩到1分钟以内。
+
+通过启动命令的`args.load_model`控制是否使用paddle的二进制模型启动：
+```python
+if args.load_model:
+    # 从paddle二进制模型加载参数
+    path = args.init_model_files_path
+    fluid.io.load_persistables(
+        executor=exe,
+        dirname=path,
+        main_program=fluid.default_main_program())
+    lr = fluid.global_scope().find_var("learning_rate_0").get_tensor()
+    lr.set(np.array(args.learning_rate).a    stype('float32'), place)
+    logger.info("Load persistables from \"{}\"".format(path))
+else:
+    # 将明文树结构及数据，set到组网中的Variale中
+    # 不使用NumpyInitialize方法是考虑到树结构相关数据size过大，有性能风险
+    Numpy_model = {}
+    Numpy_model['TDM_Tree_Travel'] = tdm_model.tdm_param_prepare_dict['travel_array']
+    Numpy_model['TDM_Tree_Layer'] = tdm_model.tdm_param_prepare_dict['layer_array']
+    Numpy_model['TDM_Tree_Info'] = tdm_model.tdm_param_prepare_dict['info_array']
+    Numpy_model['TDM_Tree_Emb'] = tdm_model.tdm_param_prepare_dict['emb_array']
+    for param_name in Numpy_model:
+        param_t = fluid.global_scope().find_var(param_name).get_tensor()
+        if param_name == 'TDM_Tree_Emb':
+            param_t.set(Numpy_model[str(param_name)
+                                    ].astype('float32'), place)
+        else:
+            param_t.set(Numpy_model[str(param_name)
+                                    ].astype('int32'), place)
+
+if args.save_init_model or not args.load_model:
+    logger.info("Begin Save Init model.")
+    model_path = os.path.join(args.model_files_path, "init_model")
+    fluid.io.save_persistables(executor=exe, dirname=model_path)
+    logger.info("End Save Init model.")
+```
+
+> 为什么每次加载模型手动Set `learning rate`？
+> 
+> 学习率在paddle的组网中，是以一个`persistable=Ture`的长期变量储存在模型的Variable scope里的。每次使用load_persistables加载模型时，也会使用加载的模型的学习率覆盖本地模型的默认学习率，换言之，加载init_model以后，学习率也是保存init_model时的学习率。对模型的调试会产生不必要的影响，为了保证网络训练如预期，需要这样的手动set步骤。
+
+### demo的训练运行方法
+- 运行脚本为run_train.sh
+
+    ```bash
+    sh run_train.sh
+    ```
+- 若模型是ndarray(刚完成树的构建)，则将启动命令中的save_init_model设置为1
+- 若模型事paddle模型（之前保存过初始化模型），则启动命令load_model应设置为1
+- 运行前，需仔细检查：`thirdparty_path`, `train_data_path`,以及模型启动地址是否正确
+- 运行前，需仔细检查：模型超参是否匹配，如学习率，线程，batch_size等
+
+### demo的预测运行方法
+- 运行脚本为run_infer.sh
+    ```bash
+    sh run_infer.sh
+    ```
+- 运行前，需仔细检查：`thirdparty_path`, `test_data_path`,以及模型启动地址是否正确
+
+### demo分布式运行方法
+
+- demo代码中给出了本地模拟分布式的运行方式：
+    ```bash
+    sh local_cluster.sh
+    ```
+
+- 首先需要运行本地训练，产出paddle的二进制模型文件`init_model`，使参数服务器可以加载该模型
+- 分布式运行的超参在`async_train.sh`中调整
+- paddle的参数服务器运行原理可以参考文档：[PaddlePaddle Fluid CPU分布式训练(Transplier)](https://github.com/PaddlePaddle/Fleet/tree/develop/markdown_doc/transpiler)
+- 单机运行流程与分布式运行流程的区别，及入门使用文档：[基于DNN模型的点击率预估模型](https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/ctr/dnn)
+
+### demo的部署及推理
+
+Demo代码中给出了基于paddle预测库加载tdm模型，输入emb产出item的端到端的推理示例，参考此示例，可以在服务器端快速部署tdm服务。
+
+- 运行方式如下
+    ```bash
+    sh run_predict.sh
+    ```
+- 首先需要运行`run_infer.sh`，打开`save_init_model`开关，使用`save_inference_model`产出paddle的推理模型，`predict.py`会加载`infer_model`，进行高速推理。
+- 欲想进一步高速推理，需使用含预测库的paddle预测库，可以使用`mkl`及`mkl_dnn`等计算库加速op的计算。相关文档可以参考：[服务器端部署](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/index_cn.html)
+- tdm相关op目前仅支持在cpu设备上运行，后续会支持GPU，欢迎关注。
\ No newline at end of file

PaddleRec/tdm/tdm_demo/args.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import six
+import argparse
+
+
+def str2bool(v):
+    """
+    str2bool
+    """
+    # because argparse does not support to parse "true, False" as python
+    # boolean directly
+    return v.lower() in ("true", "t", "1")
+
+
+class ArgumentGroup(object):
+    """
+    ArgumentGroup
+    """
+
+    def __init__(self, parser, title, des):
+        """
+        init
+        """
+        self._group = parser.add_argument_group(title=title, description=des)
+
+    def add_arg(self, name, type, default, help, **kwargs):
+        """
+        add_arg
+        """
+        type = str2bool if type == bool else type
+        # if type == list: # by dwk
+        #     self._group.add_argument("--" + name, nargs='+', type=int)
+        # else:
+        self._group.add_argument(
+            "--" + name,
+            default=default,
+            type=type,
+            help=help + ' Default: %(default)s.',
+            **kwargs)
+
+
+def parse_args():
+    """
+    parse_args
+    """
+    # global
+    parser = argparse.ArgumentParser("main")
+    main_g = ArgumentGroup(parser, "main", "global conf")
+    main_g.add_arg("random_seed", int, 0, "random_seed")
+    main_g.add_arg("cpu_num", int, 1, "cpu_num")
+    main_g.add_arg("is_local", bool, False,
+                   "whether to perform local training")
+    main_g.add_arg("is_cloud", bool, False, "")
+    main_g.add_arg("is_test", bool, False, "")
+    main_g.add_arg("sync_mode", str, "async", "distributed traing mode")
+    main_g.add_arg("need_trace", bool, False, "")
+    main_g.add_arg("need_detail", bool, False, "")
+
+    # model
+    model_g = ArgumentGroup(
+        parser, "model", "options to init, resume and save model.")
+    model_g.add_arg("epoch_num", int, 3, "number of epochs for train")
+    model_g.add_arg("batch_size", int, 16, "batch size for train")
+    model_g.add_arg("learning_rate", float, 5e-5,
+                    "learning rate for global training")
+
+    model_g.add_arg("layer_size", int, 4, "layer size")
+    model_g.add_arg("node_nums", int, 26, "tree node nums")
+    model_g.add_arg("node_emb_size", int, 64, "node embedding size")
+    model_g.add_arg("query_emb_size", int, 768, "input query embedding size")
+    model_g.add_arg("neg_sampling_list", list, [
+                    1, 2, 3, 4], "nce sample nums at every layer")
+    model_g.add_arg("layer_node_num_list", list, [
+                    2, 4, 7, 12], "node nums at every layer")
+    model_g.add_arg("leaf_node_num", int, 13, "leaf node nums")
+
+    # for infer
+    model_g.add_arg("child_nums", int, 2, "child node of ancestor node")
+    model_g.add_arg("topK", int, 1, "best recall result nums")
+
+    model_g = ArgumentGroup(
+        parser, "path", "files path of data & model.")
+    model_g.add_arg("train_files_path", str, "./data/train", "train data path")
+    model_g.add_arg("test_files_path", str, "./data/test", "test data path")
+    model_g.add_arg("model_files_path", str, "./models", "model data path")
+
+    # build tree and warm up
+    model_g.add_arg("build_tree_init_path", str,
+                    "./data/gen_tree/demo_fake_input.txt", "build tree embedding path")
+    model_g.add_arg("warm-up", bool, False,
+                    "warm up, builing new tree.")
+    model_g.add_arg("rebuild_tree_per_epochs", int, -1,
+                    "re-build tree per epochs, -1 means don't re-building")
+
+    model_g.add_arg("tree_info_init_path", str,
+                    "./thirdparty/tree_info.txt", "embedding file path")
+    model_g.add_arg("tree_travel_init_path", str,
+                    "./thirdparty/travel_list.txt", "TDM tree travel file path")
+    model_g.add_arg("tree_layer_init_path", str,
+                    "./thirdparty/layer_list.txt", "TDM tree layer file path")
+    model_g.add_arg("tree_emb_init_path", str,
+                    "./thirdparty/tree_emb.txt", "TDM tree emb file path")
+
+    model_g.add_arg("load_model", bool, False,
+                    "whether load model(paddle persistables model)")
+    model_g.add_arg("save_init_model", bool, False,
+                    "whether save init model(paddle persistables model)")
+    model_g.add_arg("init_model_files_path", str, "./models/init_model",
+                    "init model params by paddle model files for training")
+    model_g.add_arg("infer_model_files_path", str, "./models/init_model",
+                    "model files path for infer")
+
+    args = parser.parse_args()
+    return args
+
+
+def print_arguments(args):
+    """
+    print arguments
+    """
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')

PaddleRec/tdm/tdm_demo/async_train.sh

+DIRS=`pwd`
+data_path="${DIRS}/data"
+train_files_path="${data_path}/train"
+test_files_path="${data_path}/test"
+
+model_files_path="${DIRS}/model"
+# init_model will download in thirdparty folder when do paddlecloud training
+init_model_files_path="${model_files_path}/init_model"
+
+thirdparty_path="${DIRS}/thirdparty"
+tree_travel_init_path="${thirdparty_path}/travel_list.txt"
+tree_layer_init_path="${thirdparty_path}/layer_list.txt"
+tree_info_init_path="${thirdparty_path}/tree_info.txt"
+
+python_bin="python"
+echo `pwd`
+
+function main() {
+    cmd="${python_bin} distributed_train.py \
+        --is_cloud=0 \
+        --sync_mode=async \
+        --cpu_num=1 \
+        --random_seed=0 \
+        --epoch_num=1 \
+        --batch_size=32 \
+        --learning_rate=3e-4 \
+        --train_files_path=${train_files_path} \
+        --test_files_path=${test_files_path} \
+        --model_files_path=${model_files_path} \
+        --init_model_files_path=${init_model_files_path} \
+        --tree_travel_init_path=${tree_travel_init_path} \
+        --tree_info_init_path=${tree_info_init_path} \
+        --tree_layer_init_path=${tree_layer_init_path} "
+    echo ${cmd}
+    ${cmd}
+}
+
+main "$@"

PaddleRec/tdm/tdm_demo/data/test/demo_fake_test_data.txt

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PaddleRec/tdm/tdm_demo/data/train/demo_fake_train_data.txt

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0.32300743 -0.9999719 -0.9760314 -0.9992168 0.9289512 0.95043844 0.9481704 0.98556197 -0.55351686 -0.47454906 0.99999976 0.66813236 -0.7003366 0.66086745 1.0 -0.9233755 0.25717148 -0.99991375 0.99991876 0.8661739 -0.80518407 0.96468526 0.97266036 -0.9917282 0.9922263 -0.64286435 0.8628788 0.1353293 0.994207 -0.7711558 -0.120794766 0.80352 -1.0 0.96855307 -0.731797 -0.7547492 -0.20845844 0.9999607 -0.59162617 0.47989732 0.9896476 -0.98236346 0.9882714 0.99999964 0.08750912 0.47451445 -0.9875244 0.29316688 0.9999501 0.99692875 0.93303627 0.6522245 -0.5521537 -0.36325985 -0.99013996 0.91541886 -0.79438996 -0.96436757 0.14376909 0.79922646 -0.6978308 -0.94706684 0.3037744 -0.9227594 0.89376473 -0.98733544 -0.9542534 0.9423359 -0.9223392 -0.8170252 0.9974132 0.99964577 -0.9599287 0.5561011 0.99930817 -0.031173818 0.9409823 0.93238556 -0.9960135 0.99944484 -0.96108025 -0.914049 -0.6105318 -0.64838517 0.96406263 0.9996509 -0.9981671 0.9999276 0.8979043 -0.9233479 0.983884 -0.99703944 -0.99999905 0.8993983 0.41637322 0.25362557 0.3663862 0.98621625 -0.9999902 0.09194317 0.998134 0.75452584 0.94763565 -0.78139144 0.9251551 -0.16449915 0.7558825 -0.61916995 0.99407 0.98770005 -0.7486478 0.68050325 0.99341464 0.20712593 0.121428765 -0.99832517 0.99999994 0.546869 0.7774558 0.78346616 0.948524 -0.24188636 0.9996661 -0.99988973 -0.73184645 -0.99986756 -0.9991098	8
\ No newline at end of file

PaddleRec/tdm/tdm_demo/dataset_generator.py

+# -*- coding=utf8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+
+import json
+import pickle
+import time
+import random
+import os
+import numpy as np
+import sys
+import paddle.fluid.incubate.data_generator as dg
+
+
+class TDMDataset(dg.MultiSlotStringDataGenerator):
+    """
+    DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
+    Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
+    """
+
+    def infer_reader(self, infer_file_list, batch):
+        """
+            Read test_data line by line & yield batch
+            """
+        def local_iter():
+            """Read file line by line"""
+            for fname in infer_file_list:
+                with open(fname, "r") as fin:
+                    for line in fin:
+                        one_data = (line.strip('\n')).split('\t')
+                        input_emb = one_data[0].split(' ')
+
+                        yield [input_emb]
+
+        import paddle
+        batch_iter = paddle.batch(local_iter, batch)
+        return batch_iter
+
+    def generate_sample(self, line):
+        """
+        Read the data line by line and process it as a dictionary
+        """
+        def iterator():
+            """
+            This function needs to be implemented by the user, based on data format
+            """
+            features = (line.strip('\n')).split('\t')
+            input_emb = features[0].split(' ')
+            item_label = [features[1]]
+
+            feature_name = ["input_emb", "item_label"]
+            yield zip(feature_name, [input_emb] + [item_label])
+
+        return iterator
+
+
+if __name__ == "__main__":
+    d = TDMDataset()
+    d.run_from_stdin()

PaddleRec/tdm/tdm_demo/distributed_train.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+from __future__ import print_function
+import os
+import time
+import numpy as np
+import logging
+import random
+from shutil import copyfile
+import paddle.fluid as fluid
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
+from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
+
+from args import print_arguments, parse_args
+from utils import tdm_sampler_prepare, tdm_child_prepare, tdm_emb_prepare
+
+from train_network import TdmTrainNet
+
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+hadoop_home = os.getenv("HADOOP_HOME")
+configs = {
+    "fs.default.name": os.getenv("FS_NAME"),
+    "hadoop.job.ugi": os.getenv("FS_UGI")
+}
+client = HDFSClient(hadoop_home, configs)
+
+
+def get_dataset(inputs, args):
+    """get dataset"""
+    dataset = fluid.DatasetFactory().create_dataset()
+    dataset.set_use_var(inputs)
+    dataset.set_pipe_command("python ./dataset_generator.py")
+    dataset.set_batch_size(args.batch_size)
+    dataset.set_thread(int(args.cpu_num))
+    file_list = [
+        str(args.train_files_path) + "/%s" % x
+        for x in os.listdir(args.train_files_path)
+    ]
+
+    # 请确保每一个训练节点都持有不同的训练文件
+    # 当我们用本地多进程模拟分布式时，每个进程需要拿到不同的文件
+    # 使用 fleet.split_files 可以便捷的以文件为单位分配训练样本
+    if not int(args.is_cloud):
+        file_list = fleet.split_files(file_list)
+    logger.info("file list: {}".format(file_list))
+    total_example_num = get_example_num(file_list)
+    return dataset, file_list, total_example_num
+
+
+def train(args):
+    """run train"""
+    # set random
+    program = fluid.default_main_program()
+    program.random_seed = args.random_seed
+
+    # 根据环境变量确定当前机器/进程在分布式训练中扮演的角色
+    # 然后使用 fleet api的 init()方法初始化这个节点
+    role = role_maker.PaddleCloudRoleMaker()
+    fleet.init(role)
+
+    # 我们还可以进一步指定分布式的运行模式，通过 DistributeTranspilerConfig进行配置
+    # 如下，我们设置分布式运行模式为异步(async)，同时将参数进行切分，以分配到不同的节点
+    if args.sync_mode == "sync":
+        strategy = StrategyFactory.create_sync_strategy()
+    elif args.sync_mode == "half_async":
+        strategy = StrategyFactory.create_half_async_strategy()
+    elif args.sync_mode == "async":
+        strategy = StrategyFactory.create_async_strategy()
+
+    # set model
+    logger.info("TDM Begin build network.")
+    tdm_model = TdmTrainNet(args)
+    inputs = tdm_model.input_data()
+
+    logger.info("TDM Begin load tree travel & layer.")
+    avg_cost, acc = tdm_model.tdm(inputs)
+    logger.info("TDM End build network.")
+    # 配置分布式的optimizer，传入我们指定的strategy，构建program
+    optimizer = fluid.optimizer.AdamOptimizer(
+        learning_rate=args.learning_rate, lazy_mode=True)
+
+    optimizer = fleet.distributed_optimizer(optimizer, strategy)
+    optimizer.minimize(avg_cost)
+    logger.info("TDM End append backward.")
+
+    # 根据节点角色，分别运行不同的逻辑
+    if fleet.is_server():
+        logger.info("TDM Run server ...")
+        # 初始化及运行参数服务器节点
+        logger.info("TDM init model path: {}".format(
+            args.init_model_files_path))
+        # 模型中除了tdm树结构相关的变量都应该在此处初始化
+        fleet.init_server(args.init_model_files_path)
+        lr = fluid.global_scope().find_var("learning_rate_0")
+        if lr:
+            lr.get_tensor().set(np.array(args.learning_rate).astype('float32'),
+                                fluid.CPUPlace())
+            logger.info("TDM Set learning rate {}".format(args.learning_rate))
+        else:
+            logger.info("TDM Didn't find learning_rate_0 param")
+        logger.info("TDM load End")
+
+        fleet.run_server()
+        logger.info("TDM Run server success!")
+    elif fleet.is_worker():
+        logger.info("TDM Run worker ...")
+        # 初始化工作节点
+        fleet.init_worker()
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        logger.info("TDM Run Startup Begin")
+        # 初始化含有分布式流程的fleet.startup_program
+        exe.run(fleet.startup_program)
+
+        # Set Learning Rate
+        lr = fluid.global_scope().find_var("learning_rate_0")
+        if lr:
+            lr.get_tensor().set(np.array(args.learning_rate).astype('float32'),
+                                place)
+            logger.info("TDM Set learning rate {}".format(args.learning_rate))
+
+        # Set TDM Variable
+        logger.info("TDM Begin load parameter.")
+        # Set TDM_Tree_Info
+        # 树结构相关的变量不参与网络更新，不存储于参数服务器，因此需要在本地手动Set
+        tdm_param_prepare_dict = tdm_sampler_prepare(args)
+        tdm_param_prepare_dict['info_array'] = tdm_child_prepare(args)
+        Numpy_model = {}
+        Numpy_model['TDM_Tree_Travel'] = tdm_param_prepare_dict['travel_array']
+        Numpy_model['TDM_Tree_Layer'] = tdm_param_prepare_dict['layer_array']
+        Numpy_model['TDM_Tree_Info'] = tdm_param_prepare_dict['info_array']
+        # Numpy_model['TDM_Tree_Emb'] = tdm_emb_prepare(args)
+        # 分布式训练中，Emb存储与参数服务器，无需在本地set
+        for param_name in Numpy_model:
+            param_t = fluid.global_scope().find_var(param_name).get_tensor()
+            param_t.set(Numpy_model[str(param_name)].astype('int32'), place)
+
+        logger.info("TDM Run Startup End")
+
+        # Train loop
+        dataset, file_list, example_num = get_dataset(inputs, args)
+        logger.info("TDM Distributed training begin ...")
+        for epoch in range(args.epoch_num):
+            # local shuffle
+            random.shuffle(file_list)
+            dataset.set_filelist(file_list)
+
+            # 训练节点运行的是经过分布式裁剪的fleet.mian_program
+            start_time = time.time()
+            exe.train_from_dataset(program=fleet.main_program,
+                                   dataset=dataset,
+                                   fetch_list=[acc, avg_cost],
+                                   fetch_info=["Epoch {} acc ".format(
+                                       epoch), "Epoch {} loss ".format(epoch)],
+                                   print_period=1,
+                                   debug=False)
+            end_time = time.time()
+            logger.info("Epoch {} finished, use time {} second, speed {} example/s".format(
+                epoch, end_time - start_time, example_num * 1.0 / (end_time - start_time)))
+
+            # 默认使用0号节点保存模型
+            if fleet.is_first_worker():
+                model_path = os.path.join(
+                    args.model_files_path, "epoch_" + str(epoch))
+                fleet.save_persistables(executor=exe, dirname=model_path)
+                logger.info("Begin upload files")
+                # upload_files(model_path, warm_up=False)
+                # 在分布式环境下时，支持上传模型到hdfs
+        logger.info("TDM Before stop worker")
+        fleet.stop_worker()
+        logger.info("TDM Distributed training success!")
+
+
+def upload_files(local_path, warm_up=False):
+    """
+    upload files to hdfs
+    """
+    remote = os.getenv("OUTPUT_PATH")
+    job_id = os.getenv("SYS_JOB_ID")
+    local = local_path.split('/')[-1]
+    remote_path = "{}/{}/{}/{}".format(remote, job_id, "model", local)
+    client.makedirs(remote_path)
+    hadoop_path = "{}/".format(remote_path)
+
+    def is_adam_param(name):
+        adam_name = ['bias_beta', 'bias_moment',
+                     'moment1_0', 'moment2_0', 'pow_acc']
+        for i in adam_name:
+            if i in name:
+                return True
+        return False
+
+    if not warm_up:
+        infer_model_path = os.path.join(os.getcwd(), 'infer_model_'+local)
+        if not os.path.exists(infer_model_path):
+            os.makedirs(infer_model_path)
+        for root, _, files in os.walk(local_path):
+            for f in files:
+                if not is_adam_param(f):
+                    copyfile(os.path.join(root, f),
+                             os.path.join(infer_model_path, f))
+        local_path = infer_model_path
+
+    client.upload(hdfs_path=hadoop_path, local_path=local_path,
+                  multi_processes=5, overwrite=False,
+                  retry_times=3)
+
+
+def get_example_num(file_list):
+    """
+    Count the number of samples in the file
+    """
+    count = 0
+    for f in file_list:
+        last_count = count
+        for index, line in enumerate(open(f, 'r')):
+            count += 1
+        logger.info("file : %s has %s example" % (f, count - last_count))
+    logger.info("Total example : %s" % count)
+    return count
+
+
+if __name__ == "__main__":
+    print(os.getcwd())
+    args = parse_args()
+    print_arguments(args)
+    train(args)

PaddleRec/tdm/tdm_demo/infer_network.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+
+import math
+import argparse
+import numpy as np
+import paddle.fluid as fluid
+from utils import tdm_sampler_prepare, tdm_child_prepare, trace_var
+from train_network import DnnLayerClassifierNet, InputTransNet
+
+
+class TdmInferNet(object):
+    def __init__(self, args):
+        self.input_embed_size = args.query_emb_size
+        self.node_embed_size = args.node_emb_size
+        self.label_nums = 2  # label为正负两类
+        self.node_nums = args.node_nums
+        self.max_layers = args.layer_size
+        self.batch_size = args.batch_size
+        self.topK = args.topK  # 最终召回多少个item
+        self.child_nums = args.child_nums  # 若树为二叉树，则child_nums=2
+
+        self.layer_list = self.get_layer_list(args)
+        self.first_layer_idx = 0
+        self.first_layer_node = self.create_first_layer(args)
+        self.layer_classifier = DnnLayerClassifierNet(args)
+        self.input_trans_net = InputTransNet(args)
+
+    def input_data(self):
+        input_emb = fluid.layers.data(
+            name="input_emb",
+            shape=[self.input_embed_size],
+            dtype="float32",
+        )
+
+        # first_layer 与 first_layer_mask 对应着infer起始层的节点
+        first_layer = fluid.layers.data(
+            name="first_layer_node",
+            shape=[1],
+            dtype="int64",
+            lod_level=1,
+        )
+
+        first_layer_mask = fluid.layers.data(
+            name="first_layer_node_mask",
+            shape=[1],
+            dtype="int64",
+            lod_level=1,
+        )
+
+        inputs = [input_emb] + [first_layer] + [first_layer_mask]
+        return inputs
+
+    def get_layer_list(self, args):
+        """get layer list from layer_list.txt"""
+        layer_list = []
+        with open(args.tree_layer_init_path, 'r') as fin:
+            for line in fin.readlines():
+                l = []
+                layer = (line.split('\n'))[0].split(',')
+                for node in layer:
+                    if node:
+                        l.append(node)
+                layer_list.append(l)
+        return layer_list
+
+    def create_first_layer(self, args):
+        """decide which layer to start infer"""
+        first_layer_id = 0
+        for idx, layer_node in enumerate(args.layer_node_num_list):
+            if layer_node >= self.topK:
+                first_layer_id = idx
+                break
+        first_layer_node = self.layer_list[first_layer_id]
+        self.first_layer_idx = first_layer_id
+        return first_layer_node
+
+    def infer_net(self, inputs):
+        """
+        infer的主要流程
+        infer的基本逻辑是：从上层开始（具体层idx由树结构及TopK值决定）
+        1、依次通过每一层分类器，得到当前层输入的指定节点的prob
+        2、根据prob值大小，取topK的节点，取这些节点的孩子节点作为下一层的输入
+        3、循环1、2步骤，遍历完所有层，得到每一层筛选结果的集合
+        4、将筛选结果集合中的叶子节点，拿出来再做一次topK，得到最终的召回输出
+        """
+        input_emb = inputs[0]
+        current_layer_node = inputs[1]
+        current_layer_child_mask = inputs[2]
+
+        node_score = []
+        node_list = []
+
+        input_trans_emb = self.input_trans_net.input_fc_infer(input_emb)
+
+        for layer_idx in range(self.first_layer_idx, self.max_layers):
+            # 确定当前层的需要计算的节点数
+            if layer_idx == self.first_layer_idx:
+                current_layer_node_num = len(self.first_layer_node)
+            else:
+                current_layer_node_num = current_layer_node.shape[1] * \
+                    current_layer_node.shape[2]
+
+            current_layer_node = fluid.layers.reshape(
+                current_layer_node, [-1, current_layer_node_num])
+            current_layer_child_mask = fluid.layers.reshape(
+                current_layer_child_mask, [-1, current_layer_node_num])
+
+            node_emb = fluid.embedding(
+                input=current_layer_node,
+                size=[self.node_nums, self.node_embed_size],
+                param_attr=fluid.ParamAttr(name="TDM_Tree_Emb"))
+
+            input_fc_out = self.input_trans_net.layer_fc_infer(
+                input_trans_emb, layer_idx)
+
+            # 过每一层的分类器
+            layer_classifier_res = self.layer_classifier.classifier_layer_infer(input_fc_out,
+                                                                                node_emb,
+                                                                                layer_idx)
+
+            # 过最终的判别分类器
+            tdm_fc = fluid.layers.fc(input=layer_classifier_res,
+                                     size=self.label_nums,
+                                     act=None,
+                                     num_flatten_dims=2,
+                                     param_attr=fluid.ParamAttr(
+                                         name="tdm.cls_fc.weight"),
+                                     bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+
+            prob = fluid.layers.softmax(tdm_fc)
+            positive_prob = fluid.layers.slice(
+                prob, axes=[2], starts=[1], ends=[2])
+            prob_re = fluid.layers.reshape(
+                positive_prob, [-1, current_layer_node_num])
+
+            # 过滤掉padding产生的无效节点（node_id=0）
+            node_zero_mask = fluid.layers.cast(current_layer_node, 'bool')
+            node_zero_mask = fluid.layers.cast(node_zero_mask, 'float')
+            prob_re = prob_re * node_zero_mask
+
+            # 在当前层的分类结果中取topK，并将对应的score及node_id保存下来
+            k = self.topK
+            if current_layer_node_num < self.topK:
+                k = current_layer_node_num
+            _, topk_i = fluid.layers.topk(prob_re, k)
+
+            # index_sample op根据下标索引tensor对应位置的值
+            # 若paddle版本>2.0，调用方式为paddle.index_sample
+            top_node = fluid.contrib.layers.index_sample(
+                current_layer_node, topk_i)
+            prob_re_mask = prob_re * current_layer_child_mask  # 过滤掉非叶子节点
+            topk_value = fluid.contrib.layers.index_sample(
+                prob_re_mask, topk_i)
+            node_score.append(topk_value)
+            node_list.append(top_node)
+
+            # 取当前层topK结果的孩子节点，作为下一层的输入
+            if layer_idx < self.max_layers - 1:
+                # tdm_child op 根据输入返回其 child 及 child_mask
+                # 若child是叶子节点，则child_mask=1，否则为0
+                current_layer_node, current_layer_child_mask = \
+                    fluid.contrib.layers.tdm_child(x=top_node,
+                                                   node_nums=self.node_nums,
+                                                   child_nums=self.child_nums,
+                                                   param_attr=fluid.ParamAttr(
+                                                       name="TDM_Tree_Info"),
+                                                   dtype='int64')
+
+        total_node_score = fluid.layers.concat(node_score, axis=1)
+        total_node = fluid.layers.concat(node_list, axis=1)
+
+        # 考虑到树可能是不平衡的，计算所有层的叶子节点的topK
+        res_score, res_i = fluid.layers.topk(total_node_score, self.topK)
+        res_layer_node = fluid.contrib.layers.index_sample(total_node, res_i)
+        res_node = fluid.layers.reshape(res_layer_node, [-1, self.topK, 1])
+
+        # 利用Tree_info信息，将node_id转换为item_id
+        tree_info = fluid.default_main_program().global_block().var("TDM_Tree_Info")
+        res_node_emb = fluid.layers.gather_nd(tree_info, res_node)
+
+        res_item = fluid.layers.slice(
+            res_node_emb, axes=[2], starts=[0], ends=[1])
+        res_item_re = fluid.layers.reshape(res_item, [-1, self.topK])
+        return res_item_re

PaddleRec/tdm/tdm_demo/local_cluster.sh

+#!/bin/bash
+echo "WARNING: This script only for run PaddlePaddle Fluid on one node"
+
+CLUSTER_DIRS="./"
+
+if [ ! -d "${CLUSTER_DIRS}/model" ]; then
+  mkdir "${CLUSTER_DIRS}/model"
+  echo "Create model folder for store infer model"
+fi
+
+if [ ! -d "${CLUSTER_DIRS}/log" ]; then
+  mkdir "${CLUSTER_DIRS}/log"
+  echo "Create log floder for store running log"
+fi
+
+if [ ! -d "${CLUSTER_DIRS}/output" ]; then
+  mkdir "${CLUSTER_DIRS}/output"
+  echo "Create output floder"
+fi
+
+# environment variables for fleet distribute training
+export PADDLE_TRAINER_ID=0
+
+export PADDLE_TRAINERS_NUM=1
+export OUTPUT_PATH="output"
+export SYS_JOB_ID="test"
+
+export FLAGS_communicator_thread_pool_size=5
+export FLAGS_communicator_fake_rpc=0
+export FLAGS_communicator_is_sgd_optimizer=0
+
+export FLAGS_communicator_send_queue_size=1
+export FLAGS_communicator_max_merge_var_num=1
+export FLAGS_communicator_max_send_grad_num_before_recv=1
+export FLAGS_communicator_min_send_grad_num_before_recv=1
+export FLAGS_rpc_retry_times=3
+export PADDLE_PSERVERS_IP_PORT_LIST="127.0.0.1:36001"
+export PADDLE_PSERVER_PORT_ARRAY=(36001)
+export PADDLE_PSERVER_NUMS=1
+export PADDLE_TRAINERS=1
+
+export TRAINING_ROLE=PSERVER
+export GLOG_v=0
+export GLOG_logtostderr=0
+ps -ef|grep python|awk '{print $2}'|xargs kill -9
+
+train_mode=$1
+
+for((i=0;i<$PADDLE_PSERVER_NUMS;i++))
+do
+    cur_port=${PADDLE_PSERVER_PORT_ARRAY[$i]}
+    echo "PADDLE WILL START PSERVER "$cur_port
+    export PADDLE_PORT=${cur_port}
+    export POD_IP=127.0.0.1
+    sh ./async_train.sh &> ./log/pserver.$i.log &
+done
+
+export TRAINING_ROLE=TRAINER
+export GLOG_v=0
+export GLOG_logtostderr=0
+
+for((i=0;i<$PADDLE_TRAINERS;i++))
+do
+    echo "PADDLE WILL START Trainer "$i
+    PADDLE_TRAINER_ID=$i
+    sh ./async_train.sh &> ./log/trainer.$i.log &
+done
+
+echo "Training log stored in ./log/"

PaddleRec/tdm/tdm_demo/local_infer.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+from __future__ import print_function
+import os
+import time
+import numpy as np
+import logging
+import argparse
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import profiler
+
+from args import print_arguments, parse_args
+from infer_network import TdmInferNet
+from dataset_generator import TDMDataset
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def to_tensor(data, place):
+    """
+    Convert data to paddle tensor
+    """
+    flattened_data = np.concatenate(data, axis=0).astype("float32")
+    flattened_data = flattened_data.reshape([-1, 768])
+    res = fluid.Tensor()
+    res.set(flattened_data, place)
+    return res
+
+
+def data2tensor(data, place):
+    """
+    Dataset prepare
+    """
+    input_emb = to_tensor([x[0] for x in data], place)
+
+    return input_emb
+
+
+def run_infer(args, model_path):
+    """run infer"""
+    logger.info("Infer Begin")
+    file_list = [
+        str(args.test_files_path) + "/%s" % x
+        for x in os.listdir(args.test_files_path)
+    ]
+
+    tdm_model = TdmInferNet(args)
+    inputs = tdm_model.input_data()
+    res_item = tdm_model.infer_net(inputs)
+    test_reader = TDMDataset().infer_reader(file_list, args.batch_size)
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    path = os.path.join(args.model_files_path, model_path)
+    fluid.io.load_persistables(
+        executor=exe,
+        dirname=path,
+        main_program=fluid.default_main_program())
+
+    logger.info("Load persistables from \"{}\"".format(path))
+
+    if args.save_init_model:
+        logger.info("Begin Save infer model.")
+        model_path = (str(args.model_files_path) + "/" + "infer_model")
+        fluid.io.save_inference_model(executor=exe, dirname=model_path,
+                                      feeded_var_names=[
+                                          'input_emb', 'first_layer_node', 'first_layer_node_mask'],
+                                      target_vars=[res_item])
+        logger.info("End Save infer model.")
+
+    first_layer_node = tdm_model.first_layer_node
+    first_layer_nums = len(first_layer_node)
+    first_layer_node = np.array(first_layer_node)
+    first_layer_node = first_layer_node.reshape((1, -1)).astype('int64')
+    first_layer_node = first_layer_node.repeat(args.batch_size, axis=0)
+    # 在demo中，假设infer起始层的节点都不是叶子节点，mask=0
+    # 若真实的起始层含有叶子节点，则对应位置的 mask=1
+    first_layer_mask = (
+        np.zeros((args.batch_size, first_layer_nums))).astype('int64')
+
+    for batch_id, data in enumerate(test_reader()):
+        input_emb = data2tensor(data, place)
+        item_res = exe.run(fluid.default_main_program(),
+                           feed={"input_emb": input_emb,
+                                 "first_layer_node": first_layer_node,
+                                 "first_layer_node_mask": first_layer_mask},
+                           fetch_list=[res_item])
+        logger.info("TEST --> batch: {} infer_item {}".format(
+            batch_id, item_res))
+    logger.info("Inference complete!")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    print_arguments(args)
+    # 在此处指定infer模型所在的文件夹
+    path = "epoch_0"
+    run_infer(args, path)

PaddleRec/tdm/tdm_demo/local_train.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+from __future__ import print_function
+import os
+import time
+import numpy as np
+import logging
+import argparse
+
+import paddle
+import paddle.fluid as fluid
+
+from args import print_arguments, parse_args
+from utils import tdm_sampler_prepare, tdm_child_prepare
+from train_network import TdmTrainNet
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def get_dataset(inputs, args):
+    """
+    get dataset
+    """
+    dataset = fluid.DatasetFactory().create_dataset()
+    dataset.set_use_var(inputs)
+    dataset.set_pipe_command("python ./dataset_generator.py")
+    dataset.set_batch_size(args.batch_size)
+    dataset.set_thread(int(args.cpu_num))
+    file_list = [
+        str(args.train_files_path) + "/%s" % x
+        for x in os.listdir(args.train_files_path)
+    ]
+    dataset.set_filelist(file_list)
+    logger.info("file list: {}".format(file_list))
+    return dataset
+
+
+def run_train(args):
+    """
+    run train
+    """
+    logger.info("TDM Begin build network.")
+    tdm_model = TdmTrainNet(args)
+    inputs = tdm_model.input_data()
+    avg_cost, acc = tdm_model.tdm(inputs)
+    logger.info("TDM End build network.")
+
+    dataset = get_dataset(inputs, args)
+
+    optimizer = fluid.optimizer.AdamOptimizer(
+        learning_rate=args.learning_rate,
+        lazy_mode=True)
+    optimizer.minimize(avg_cost)
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    if args.load_model:
+        # 从paddle二进制模型加载参数
+        path = args.init_model_files_path
+        fluid.io.load_persistables(
+            executor=exe,
+            dirname=path,
+            main_program=fluid.default_main_program())
+        lr = fluid.global_scope().find_var("learning_rate_0").get_tensor()
+        lr.set(np.array(args.learning_rate).astype('float32'), place)
+        logger.info("Load persistables from \"{}\"".format(path))
+    else:
+        # 将明文树结构及数据，set到组网中的Variale中
+        # 不使用NumpyInitialize方法是考虑到树结构相关数据size过大，有性能风险
+        Numpy_model = {}
+        Numpy_model['TDM_Tree_Travel'] = tdm_model.tdm_param_prepare_dict['travel_array']
+        Numpy_model['TDM_Tree_Layer'] = tdm_model.tdm_param_prepare_dict['layer_array']
+        Numpy_model['TDM_Tree_Info'] = tdm_model.tdm_param_prepare_dict['info_array']
+        Numpy_model['TDM_Tree_Emb'] = tdm_model.tdm_param_prepare_dict['emb_array']
+        for param_name in Numpy_model:
+            param_t = fluid.global_scope().find_var(param_name).get_tensor()
+            if param_name == 'TDM_Tree_Emb':
+                param_t.set(Numpy_model[str(param_name)
+                                        ].astype('float32'), place)
+            else:
+                param_t.set(Numpy_model[str(param_name)
+                                        ].astype('int32'), place)
+
+    if args.save_init_model or not args.load_model:
+        logger.info("Begin Save Init model.")
+        model_path = os.path.join(args.model_files_path, "init_model")
+        fluid.io.save_persistables(executor=exe, dirname=model_path)
+        logger.info("End Save Init model.")
+
+    logger.info("TDM Local training begin ...")
+    for epoch in range(args.epoch_num):
+        start_time = time.time()
+        exe.train_from_dataset(
+            program=fluid.default_main_program(),
+            dataset=dataset,
+            fetch_list=[acc, avg_cost],
+            fetch_info=["Epoch {} acc".format(
+                epoch), "Epoch {} loss".format(epoch)],
+            print_period=1,
+            debug=False,
+        )
+        end_time = time.time()
+        logger.info("Epoch %d finished, use time=%d sec\n" %
+                    (epoch, end_time - start_time))
+
+        model_path = os.path.join(args.model_files_path, "epoch_" + str(epoch))
+        fluid.io.save_persistables(executor=exe, dirname=model_path)
+
+    logger.info("Local training success!")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    print_arguments(args)
+    run_train(args)

PaddleRec/tdm/tdm_demo/predict.py

+# -*- coding: utf-8 -*-
+"""
+# Copyright (c) 2019 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.
+"""
+
+import os
+import argparse
+import numpy as np
+import argparse
+import paddle.fluid as fluid
+from paddle.fluid.core import PaddleTensor
+from paddle.fluid.core import AnalysisConfig
+from paddle.fluid.core import create_paddle_predictor
+from dataset_generator import TDMDataset
+from infer_network import TdmInferNet
+from args import print_arguments, parse_args
+import logging
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def to_tensor(data):
+    """
+    Convert data to paddle tensor
+    """
+    flattened_data = np.concatenate(data, axis=0).astype("float32")
+    flattened_data = flattened_data.reshape([-1, 768])
+    return flattened_data
+
+
+def data2tensor(data):
+    """
+    Dataset prepare
+    """
+    input_emb = to_tensor([x[0] for x in data])
+
+    return input_emb
+
+
+def tdm_input(input_emb, first_layer_node, first_layer_mask):
+    """
+    Create input of tdm pred
+    """
+    input_emb = PaddleTensor(input_emb)
+    first_layer_node = PaddleTensor(first_layer_node)
+    first_layer_mask = PaddleTensor(first_layer_mask)
+    return [input_emb, first_layer_node, first_layer_mask]
+
+
+def main():
+    """Predictor main"""
+    args = parse_args()
+
+    config = AnalysisConfig(args.model_files_path)
+    config.disable_gpu()
+    config.enable_profile()
+    # config.enable_mkldnn()
+    config.set_cpu_math_library_num_threads(args.cpu_num)
+
+    predictor = create_paddle_predictor(config)
+    tdm_model = TdmInferNet(args)
+    first_layer_node = tdm_model.first_layer_node
+    first_layer_nums = len(first_layer_node)
+    first_layer_node = np.array(first_layer_node)
+    first_layer_node = first_layer_node.reshape((1, -1)).astype('int64')
+    first_layer_node = first_layer_node.repeat(args.batch_size, axis=0)
+    first_layer_mask = (
+        np.zeros((args.batch_size, first_layer_nums))).astype('int64')
+
+    file_list = [
+        str(args.test_files_path) + "/%s" % x
+        for x in os.listdir(args.test_files_path)
+    ]
+    test_reader = TDMDataset().infer_reader(file_list, args.batch_size)
+
+    for batch_id, data in enumerate(test_reader()):
+        input_emb = data2tensor(data)
+
+        inputs = tdm_input(input_emb, first_layer_node, first_layer_mask)
+        outputs = predictor.run(inputs)
+        output = outputs[0]
+        output_data = output.as_ndarray()
+
+        logger.info("TEST --> batch: {} infer_item {}".format(
+            batch_id, output_data))
+
+
+if __name__ == "__main__":
+    main()

PaddleRec/tdm/tdm_demo/run_infer.sh

+DIRS=`pwd`
+data_path="${DIRS}/data"
+train_files_path="${data_path}/train"
+test_files_path="${data_path}/test"
+
+model_files_path="${DIRS}/model"
+
+thirdparty_path="${DIRS}/thirdparty"
+tree_travel_init_path="${thirdparty_path}/travel_list.txt"
+tree_layer_init_path="${thirdparty_path}/layer_list.txt"
+tree_info_init_path="${thirdparty_path}/tree_info.txt"
+
+export GLOG_v=0
+
+function main() {
+    cmd="python local_infer.py \
+        --is_local=1 \
+        --cpu_num=1 \
+        --batch_size=1 \
+        --topK=1 \
+        --is_test=1 \
+        --save_init_model=1 \
+        --train_files_path=${train_files_path} \
+        --test_files_path=${test_files_path} \
+        --model_files_path=${model_files_path} \
+        --tree_travel_init_path=${tree_travel_init_path} \
+        --tree_info_init_path=${tree_info_init_path} \
+        --tree_layer_init_path=${tree_layer_init_path} " 
+    ${cmd}
+}
+
+main "$@"

PaddleRec/tdm/tdm_demo/run_predict.sh

+DIRS=`pwd`
+data_path="${DIRS}/data"
+train_files_path="${data_path}/train"
+test_files_path="${data_path}/test"
+
+model_files_path="${DIRS}/model"
+
+thirdparty_path="${DIRS}/thirdparty"
+tree_travel_init_path="${thirdparty_path}/travel_list.txt"
+tree_layer_init_path="${thirdparty_path}/layer_list.txt"
+tree_info_init_path="${thirdparty_path}/tree_info.txt"
+
+export GLOG_v=0
+
+function main() {
+    cmd="python predict.py \
+        --is_local=1 \
+        --cpu_num=1 \
+        --batch_size=1 \
+        --topK=1 \
+        --is_test=1 \
+        --save_init_model=1 \
+        --train_files_path=${train_files_path} \
+        --test_files_path=${test_files_path} \
+        --model_files_path=${model_files_path} \
+        --tree_travel_init_path=${tree_travel_init_path} \
+        --tree_info_init_path=${tree_info_init_path} \
+        --tree_layer_init_path=${tree_layer_init_path} " 
+    ${cmd}
+}
+
+main "$@"

PaddleRec/tdm/tdm_demo/run_train.sh

+DIRS=`pwd`
+data_path="${DIRS}/data"
+train_files_path="${data_path}/train"
+test_files_path="${data_path}/test"
+
+model_files_path="${DIRS}/model"
+
+thirdparty_path="${DIRS}/thirdparty"
+tree_travel_init_path="${thirdparty_path}/travel_list.txt"
+tree_layer_init_path="${thirdparty_path}/layer_list.txt"
+tree_info_init_path="${thirdparty_path}/tree_info.txt"
+
+export GLOG_v=0
+
+function main() {
+    cmd="python local_train.py \
+        --is_local=1 \
+        --save_init_model=1 \
+        --load_model=0 \
+        --cpu_num=1 \
+        --random_seed=0 \
+        --epoch_num=1 \
+        --batch_size=32 \
+        --learning_rate=3e-4 \
+        --train_files_path=${train_files_path} \
+        --model_files_path=${model_files_path} \
+        --tree_travel_init_path=${tree_travel_init_path} \
+        --tree_info_init_path=${tree_info_init_path} \
+        --tree_layer_init_path=${tree_layer_init_path} " 
+    ${cmd}
+}
+
+main "$@"

PaddleRec/tdm/tdm_demo/thirdparty/layer_list.txt

+1,2
+3,4,5,6
+7,8,9,10,11,12,13
+14,15,16,17,18,19,20,21,22,23,24,25
\ No newline at end of file

PaddleRec/tdm/tdm_demo/thirdparty/travel_list.txt

+1,3,7,14
+1,3,7,15
+1,3,8,16
+1,3,8,17
+1,4,9,18
+1,4,9,19
+1,4,10,20
+1,4,10,21
+2,5,11,22
+2,5,11,23
+2,5,12,24
+2,5,12,25
+2,6,13,0
\ No newline at end of file

PaddleRec/tdm/tdm_demo/thirdparty/tree_emb.txt

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PaddleRec/tdm/tdm_demo/thirdparty/tree_info.txt

+0,0,0,1,2
+0,1,0,3,4
+0,1,0,5,6
+0,2,1,7,8
+0,2,1,9,10
+0,2,2,11,12
+0,2,2,13,0
+0,3,3,14,15
+0,3,3,16,17
+0,3,4,18,19
+0,3,4,20,21
+0,3,5,22,23
+0,3,5,24,25
+12,3,6,0,0
+0,4,7,0,0
+1,4,7,0,0
+2,4,8,0,0
+3,4,8,0,0
+4,4,9,0,0
+5,4,9,0,0
+6,4,10,0,0
+7,4,10,0,0
+8,4,11,0,0
+9,4,11,0,0
+10,4,12,0,0
+11,4,12,0,0
\ No newline at end of file

PaddleRec/tdm/tdm_demo/train_network.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+
+import math
+import argparse
+import numpy as np
+import logging
+import paddle.fluid as fluid
+from utils import tdm_sampler_prepare, tdm_child_prepare, tdm_emb_prepare, trace_var
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+class TdmTrainNet(object):
+    """
+    TDM-Demo网络的主要流程部分
+    """
+
+    def __init__(self, args):
+        self.input_embed_size = args.query_emb_size
+        self.node_emb_size = args.node_emb_size
+        self.label_nums = 2
+        self.node_nums = args.node_nums
+        self.max_layers = args.layer_size
+        self.neg_sampling_list = args.neg_sampling_list
+        self.output_positive = True
+        self.get_tree_info(args)
+
+        # 设置是否需要进行数值的debug
+        self.need_trace = args.need_trace
+        self.need_detail = args.need_detail
+
+        if not args.load_model:
+            # 每次模型训练仅需运行一次，灌入生成的明文，拿到树结构信息
+            # 将其保存为paddle的二进制init_model
+            # 下一次调试或训练即可load init_model,快速启动且内存占用更小
+            self.tdm_param_prepare_dict = tdm_sampler_prepare(args)
+            logger.info("--Layer node num list--: {}".format(
+                self.tdm_param_prepare_dict['layer_node_num_list']))
+            self.layer_node_num_list = self.tdm_param_prepare_dict['layer_node_num_list']
+
+            logger.info("--Leaf node num--: {}".format(
+                self.tdm_param_prepare_dict['leaf_node_num']))
+            self.leaf_node_num = self.tdm_param_prepare_dict['leaf_node_num']
+
+            self.tdm_param_prepare_dict['info_array'] = tdm_child_prepare(
+                args)
+            logger.info(
+                "--Tree Info array shape {}--".format(self.tdm_param_prepare_dict['info_array'].shape))
+
+            self.tdm_param_prepare_dict['emb_array'] = tdm_emb_prepare(args)
+            logger.info(
+                "--Tree Emb array shape {}--".format(self.tdm_param_prepare_dict['emb_array'].shape))
+        else:
+            self.layer_node_num_list = args.layer_node_num_list
+            self.leaf_node_num = args.leaf_node_num
+
+        self.input_trans_layer = InputTransNet(args)
+        self.layer_classifier = DnnLayerClassifierNet(args)
+
+    def get_tree_info(self, args):
+        """
+        TDM_Tree_Info 虽然在训练过程中没有用到，但在预测网络中会使用。
+        如果希望保存的模型直接用来预测，不再有额外的生成tree_info参数的步骤，
+        则可以在训练组网中添加tree_info参数，训练保存模型时可以进行保存。
+        """
+        fluid.default_startup_program().global_block().create_var(
+            name="TDM_Tree_Info", dtype=fluid.core.VarDesc.VarType.INT32, shape=[args.node_nums, 3 + args.child_nums],
+            persistable=True,
+            initializer=fluid.initializer.ConstantInitializer(0))
+        tdm_tree_info = fluid.default_main_program().global_block().create_var(
+            name="TDM_Tree_Info", dtype=fluid.core.VarDesc.VarType.INT32, shape=[args.node_nums, 3 + args.child_nums],
+            persistable=True)
+
+    def input_data(self):
+        """
+        指定tdm训练网络的输入变量
+        """
+        input_emb = fluid.data(
+            name="input_emb",
+            shape=[None, self.input_embed_size],
+            dtype="float32",
+        )
+
+        item_label = fluid.data(
+            name="item_label",
+            shape=[None, 1],
+            dtype="int64",
+        )
+
+        inputs = [input_emb] + [item_label]
+        return inputs
+
+    def tdm(self, inputs):
+        """
+        tdm训练网络的主要流程部分
+        """
+        input_emb = inputs[0]
+        item_label = inputs[1]
+
+        # trace_var用于在静态图的调试中打印参数信息细节:
+        # 将 need_trace设置为True，可以在日志中看到参数的前向信息（数值默认前20个）
+        # 将 need_detail设置为True，可以在日志中看到参数的前向全部数值
+        trace_var(input_emb, "[TDM][inputs]", "input_emb",
+                  self.need_trace, self.need_detail)
+        trace_var(item_label, "[TDM][inputs]",
+                  "item_label", self.need_trace, self.need_detail)
+
+        # 根据输入的item的正样本在给定的树上进行负采样
+        # sample_nodes 是采样的node_id的结果，包含正负样本
+        # sample_label 是采样的node_id对应的正负标签
+        # sample_mask 是为了保持tensor维度一致，padding部分的标签，若为0，则是padding的虚拟node_id
+        sample_nodes, sample_label, sample_mask = fluid.contrib.layers.tdm_sampler(
+            x=item_label,
+            neg_samples_num_list=self.neg_sampling_list,
+            layer_node_num_list=self.layer_node_num_list,
+            leaf_node_num=self.leaf_node_num,
+            tree_travel_attr=fluid.ParamAttr(name="TDM_Tree_Travel"),
+            tree_layer_attr=fluid.ParamAttr(name="TDM_Tree_Layer"),
+            output_positive=self.output_positive,
+            output_list=True,
+            seed=0,
+            tree_dtype='int64',
+            dtype='int64'
+        )
+
+        trace_var(sample_nodes, "[TDM][tdm_sample]",
+                  "sample_nodes", self.need_trace, self.need_detail)
+        trace_var(sample_label, "[TDM][tdm_sample]",
+                  "sample_label", self.need_trace, self.need_detail)
+        trace_var(sample_mask, "[TDM][tdm_sample]",
+                  "sample_mask", self.need_trace, self.need_detail)
+
+        # 查表得到每个节点的Embedding
+        sample_nodes_emb = [
+            fluid.embedding(
+                input=sample_nodes[i],
+                is_sparse=True,
+                size=[self.node_nums, self.node_emb_size],
+                param_attr=fluid.ParamAttr(
+                    name="TDM_Tree_Emb")
+            ) for i in range(self.max_layers)
+        ]
+
+        # 此处进行Reshape是为了之后层次化的分类器训练
+        sample_nodes_emb = [
+            fluid.layers.reshape(sample_nodes_emb[i],
+                                 [-1, self.neg_sampling_list[i] +
+                                     self.output_positive, self.node_emb_size]
+                                 ) for i in range(self.max_layers)
+        ]
+        trace_var(sample_nodes_emb, "[TDM][tdm_sample]",
+                  "sample_nodes_emb", self.need_trace, self.need_detail)
+
+        # 对输入的input_emb进行转换，使其维度与node_emb维度一致
+        input_trans_emb = self.input_trans_layer.input_trans_layer(input_emb)
+        trace_var(input_trans_emb, "[TDM][input_trans]",
+                  "input_trans_emb", self.need_trace, self.need_detail)
+
+        # 分类器的主体网络，分别训练不同层次的分类器
+        layer_classifier_res = self.layer_classifier.classifier_layer(
+            input_trans_emb, sample_nodes_emb)
+        trace_var(layer_classifier_res, "[TDM][classifier_layer]",
+                  "layer_classifier_res", self.need_trace, self.need_detail)
+
+        # 最后的概率判别FC，将所有层次的node分类结果放到一起以相同的标准进行判别
+        # 考虑到树极大可能不平衡，有些item不在最后一层，所以需要这样的机制保证每个item都有机会被召回
+        tdm_fc = fluid.layers.fc(input=layer_classifier_res,
+                                 size=self.label_nums,
+                                 act=None,
+                                 num_flatten_dims=2,
+                                 param_attr=fluid.ParamAttr(
+                                     name="tdm.cls_fc.weight"),
+                                 bias_attr=fluid.ParamAttr(name="tdm.cls_fc.bias"))
+        trace_var(tdm_fc, "[TDM][cls_fc]", "tdm_fc",
+                  self.need_trace, self.need_detail)
+
+        # 将loss打平，放到一起计算整体网络的loss
+        tdm_fc_re = fluid.layers.reshape(tdm_fc, [-1, 2])
+
+        # 若想对各个层次的loss辅以不同的权重，则在此处无需concat
+        # 支持各个层次分别计算loss，再乘相应的权重
+        sample_label = fluid.layers.concat(sample_label, axis=1)
+        sample_label.stop_gradient = True
+        labels_reshape = fluid.layers.reshape(sample_label, [-1, 1])
+
+        # 计算整体的loss并得到softmax的输出
+        cost, softmax_prob = fluid.layers.softmax_with_cross_entropy(
+            logits=tdm_fc_re, label=labels_reshape, return_softmax=True)
+
+        # 通过mask过滤掉虚拟节点的loss
+        sample_mask = fluid.layers.concat(sample_mask, axis=1)
+        sample_mask.stop_gradient = True
+        mask_reshape = fluid.layers.reshape(sample_mask, [-1, 1])
+
+        mask_index = fluid.layers.where(mask_reshape != 0)
+        mask_cost = fluid.layers.gather_nd(cost, mask_index)
+
+        # 计算该batch的均值loss，同时计算acc, 亦可在这里计算auc
+        avg_cost = fluid.layers.reduce_mean(mask_cost)
+        acc = fluid.layers.accuracy(input=softmax_prob, label=labels_reshape)
+        return avg_cost, acc
+
+
+class InputTransNet(object):
+    """
+    输入侧组网的主要部分
+    """
+
+    def __init__(self, args):
+        self.node_emb_size = args.node_emb_size
+        self.max_layers = args.layer_size
+        self.is_test = args.is_test
+
+    def input_trans_layer(self, input_emb):
+        """
+        输入侧训练组网
+        """
+        # 将input映射到与node相同的维度
+        input_fc_out = fluid.layers.fc(
+            input=input_emb,
+            size=self.node_emb_size,
+            act=None,
+            param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
+            bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
+        )
+
+        # 将input_emb映射到各个不同层次的向量表示空间
+        input_layer_fc_out = [
+            fluid.layers.fc(
+                input=input_fc_out,
+                size=self.node_emb_size,
+                act="tanh",
+                param_attr=fluid.ParamAttr(
+                    name="trans.layer_fc.weight." + str(i)),
+                bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias."+str(i)),
+            ) for i in range(self.max_layers)
+        ]
+
+        return input_layer_fc_out
+
+    def input_fc_infer(self, input_emb):
+        """
+        输入侧预测组网第一部分，将input转换为node同维度
+        """
+        # 组网与训练时保持一致
+        input_fc_out = fluid.layers.fc(
+            input=input_emb,
+            size=self.node_emb_size,
+            act=None,
+            param_attr=fluid.ParamAttr(name="trans.input_fc.weight"),
+            bias_attr=fluid.ParamAttr(name="trans.input_fc.bias"),
+        )
+        return input_fc_out
+
+    def layer_fc_infer(self, input_fc_out, layer_idx):
+        """
+        输入侧预测组网第二部分，将input映射到不同层次的向量空间
+        """
+        # 组网与训练保持一致，通过layer_idx指定不同层的FC
+        input_layer_fc_out = fluid.layers.fc(
+            input=input_fc_out,
+            size=self.node_emb_size,
+            act="tanh",
+            param_attr=fluid.ParamAttr(
+                name="trans.layer_fc.weight." + str(layer_idx)),
+            bias_attr=fluid.ParamAttr(
+                name="trans.layer_fc.bias."+str(layer_idx)),
+        )
+        return input_layer_fc_out
+
+
+class DnnLayerClassifierNet(object):
+    """
+    层次分类器的主要部分
+    """
+
+    def __init__(self, args):
+        self.node_emb_size = args.node_emb_size
+        self.max_layers = args.layer_size
+        self.neg_sampling_list = args.neg_sampling_list
+        self.output_positive = True
+        self.is_test = args.is_test
+        self.child_nums = args.child_nums
+
+    def _expand_layer(self, input_layer, node, layer_idx):
+        # 扩展input的输入，使数量与node一致，
+        # 也可以以其他broadcast的操作进行代替
+        # 同时兼容了训练组网与预测组网
+        input_layer_unsequeeze = fluid.layers.unsqueeze(
+            input=input_layer, axes=[1])
+        if self.is_test:
+            input_layer_expand = fluid.layers.expand(
+                input_layer_unsequeeze, expand_times=[1, node.shape[1], 1])
+        else:
+            input_layer_expand = fluid.layers.expand(
+                input_layer_unsequeeze, expand_times=[1, node[layer_idx].shape[1], 1])
+        return input_layer_expand
+
+    def classifier_layer(self, input, node):
+        # 扩展input，使维度与node匹配
+        input_expand = [
+            self._expand_layer(input[i], node, i) for i in range(self.max_layers)
+        ]
+
+        # 将input_emb与node_emb concat到一起过分类器FC
+        input_node_concat = [
+            fluid.layers.concat(
+                input=[input_expand[i], node[i]],
+                axis=2) for i in range(self.max_layers)
+        ]
+        hidden_states_fc = [
+            fluid.layers.fc(
+                input=input_node_concat[i],
+                size=self.node_emb_size,
+                num_flatten_dims=2,
+                act="tanh",
+                param_attr=fluid.ParamAttr(
+                    name="cls.concat_fc.weight."+str(i)),
+                bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(i))
+            ) for i in range(self.max_layers)
+        ]
+
+        # 如果将所有层次的node放到一起计算loss，则需要在此处concat
+        # 将分类器结果以batch为准绳concat到一起，而不是layer
+        # 维度形如[batch_size, total_node_num, node_emb_size]
+        hidden_states_concat = fluid.layers.concat(hidden_states_fc, axis=1)
+        return hidden_states_concat
+
+    def classifier_layer_infer(self, input, node, layer_idx):
+        # 为infer组网提供的简化版classifier，通过给定layer_idx调用不同层的分类器
+
+        # 同样需要保持input与node的维度匹配
+        input_expand = self._expand_layer(input, node, layer_idx)
+
+        # 与训练网络相同的concat逻辑
+        input_node_concat = fluid.layers.concat(
+            input=[input_expand, node], axis=2)
+
+        # 根据参数名param_attr调用不同的层的FC
+        hidden_states_fc = fluid.layers.fc(
+            input=input_node_concat,
+            size=self.node_emb_size,
+            num_flatten_dims=2,
+            act="tanh",
+            param_attr=fluid.ParamAttr(
+                name="cls.concat_fc.weight."+str(layer_idx)),
+            bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(layer_idx)))
+        return hidden_states_fc

PaddleRec/tdm/tdm_demo/utils.py

+# -*- coding=utf-8 -*-
+"""
+#   Copyright (c) 2020 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.
+"""
+
+import os
+import json
+import argparse
+import logging
+import numpy as np
+import paddle.fluid as fluid
+from args import print_arguments, parse_args
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def read_list(path):
+    list = []
+    with open(path, 'r') as fin:
+        for line in fin.readlines():
+            data = (line.split('\n'))[0].split(',')
+            data = [int(i) for i in data]
+            list.append(data)
+    return list
+
+
+def read_list_float(path):
+    list = []
+    with open(path, 'r') as fin:
+        for line in fin.readlines():
+            data = (line.split('\n'))[0].split(',')
+            data = [float(i) for i in data]
+            list.append(data)
+    return list
+
+
+def read_layer_list(path):
+    layer_list = []
+    layer_list_flat = []
+    with open(path, 'r') as fin:
+        for line in fin.readlines():
+            l = []
+            layer = (line.split('\n'))[0].split(',')
+            layer = [int(i) for i in layer]
+            for node in layer:
+                if node:
+                    l.append(node)
+                    layer_list_flat.append(node)
+            layer_list.append(l)
+
+    layer_array = np.array(layer_list_flat)
+    layer_array = layer_array.reshape([-1, 1])
+    return layer_list, layer_array
+
+
+def tdm_sampler_prepare(args):
+    """load tdm tree param from list file"""
+    prepare_dict = {}
+    travel_list = read_list(args.tree_travel_init_path)
+
+    travel_array = np.array(travel_list)
+    prepare_dict['travel_array'] = travel_array
+
+    leaf_num = len(travel_list)
+    prepare_dict['leaf_node_num'] = leaf_num
+
+    layer_list, layer_array = read_layer_list(args.tree_layer_init_path)
+    prepare_dict['layer_array'] = layer_array
+
+    layer_node_num_list = [len(i) for i in layer_list]
+    prepare_dict['layer_node_num_list'] = layer_node_num_list
+
+    node_num = int(np.sum(layer_node_num_list))
+    prepare_dict['node_num'] = node_num
+
+    return prepare_dict
+
+
+def tdm_child_prepare(args):
+    """load tdm tree param from list file"""
+    info_list = read_list(args.tree_info_init_path)
+    info_array = np.array(info_list)
+    return info_array
+
+
+def tdm_emb_prepare(args):
+    """load tdm tree emb from list file"""
+    emb_list = read_list_float(args.tree_emb_init_path)
+    emb_array = np.array(emb_list)
+    return emb_array
+
+
+def trace_var(var, msg_prefix, var_name, need_trace=False, need_detail=False):
+    """trace var and its value detail"""
+    summarize_level = 20
+    if need_detail:
+        summarize_level = -1
+    if need_trace:
+        if isinstance(var, list):
+            for i, v in enumerate(var):
+                fluid.layers.Print(v,
+                                   message="{}[{}.{}]".format(
+                                       msg_prefix, var_name, i),
+                                   summarize=summarize_level)
+        else:
+            fluid.layers.Print(var, message="{}[{}]".format(
+                msg_prefix, var_name), summarize=summarize_level)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    print_arguments(args)