model.py 20.4 KB
Newer Older
C
chengmo 已提交
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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
# -*- 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 paddle.fluid as fluid
import math

from fleetrec.core.utils import envs
from fleetrec.core.model import Model as ModelBase


class Model(ModelBase):
    def __init__(self, config):
        ModelBase.__init__(self, config)
        # tree meta hyper parameters
        self.max_layers = envs.get_global_env(
            "tree_parameters.max_layers", 4, self._namespace)
        self.node_nums = envs.get_global_env(
            "tree_parameters.node_nums", 26, self._namespace)
        self.leaf_node_nums = envs.get_global_env(
            "tree_parameters.leaf_node_nums", 13, self._namespace)
        self.output_positive = envs.get_global_env(
            "tree_parameters.output_positive", True, self._namespace)
        self.layer_node_num_list = envs.get_global_env(
            "tree_parameters.layer_node_num_list", [
                2, 4, 7, 12], self._namespace)
        self.child_nums = envs.get_global_env(
            "tree_parameters.node_nums", 2, self._namespace)
        self.tree_layer_init_path = envs.get_global_env(
            "tree_parameters.tree_layer_init_path", None, self._namespace)

        # model training hyper parameter
        self.node_emb_size = envs.get_global_env(
            "hyper_parameters.node_emb_size", 64, self._namespace)
        self.input_emb_size = envs.get_global_env(
C
chengmo 已提交
48
            "hyper_parameters.input_emb_size", 768, self._namespace)
C
chengmo 已提交
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63
        self.act = envs.get_global_env(
            "hyper_parameters.act", "tanh", self._namespace)
        self.neg_sampling_list = envs.get_global_env(
            "hyper_parameters.neg_sampling_list", [
                1, 2, 3, 4], self._namespace)

        # model infer hyper parameter
        self.topK = envs.get_global_env(
            "hyper_parameters.node_nums", 1, self._namespace)
        self.batch_size = envs.get_global_env(
            "batch_size", 32, "train.reader")

    def train_net(self):
        self.train_input()
        self.tdm_net()
C
chengmo 已提交
64
        self.create_info()
C
chengmo 已提交
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177
        self.avg_loss()
        self.metrics()

    def infer_net(self):
        self.infer_input()
        self.create_first_layer()
        self.tdm_infer_net()

    """ -------- Train network detail ------- """

    def train_input(self):
        input_emb = fluid.data(
            name="input_emb",
            shape=[None, self.input_emb_size],
            dtype="float32",
        )
        self._data_var.append(input_emb)

        item_label = fluid.data(
            name="item_label",
            shape=[None, 1],
            dtype="int64",
        )

        self._data_var.append(item_label)

        if self._platform != "LINUX":
            self._data_loader = fluid.io.DataLoader.from_generator(
                feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)

    def tdm_net(self):
        """
        tdm训练网络的主要流程部分
        """
        is_distributed = True if envs.get_trainer() == "CtrTrainer" else False

        input_emb = self._data_var[0]
        item_label = self._data_var[1]

        # 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_nums,
            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)
        ]

        # 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_emb)

        # 分类器的主体网络,分别训练不同层次的分类器
        layer_classifier_res = self.classifier_layer(
            input_trans_emb, sample_nodes_emb)

        # 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"))

        # 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)
        labels_reshape = fluid.layers.reshape(sample_label, [-1, 1])
        labels_reshape.stop_gradient = True

        # 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)
        mask_reshape = fluid.layers.reshape(sample_mask, [-1, 1])
        mask_index = fluid.layers.where(mask_reshape != 0)
        mask_index.stop_gradient = True

        self.mask_cost = fluid.layers.gather_nd(cost, mask_index)
C
chengmo 已提交
178 179

        softmax_prob = fluid.layers.unsqueeze(input=softmax_prob, axes=[1])
C
chengmo 已提交
180 181 182 183 184
        self.mask_prob = fluid.layers.gather_nd(softmax_prob, mask_index)
        self.mask_label = fluid.layers.gather_nd(labels_reshape, mask_index)

        self._predict = self.mask_prob

C
chengmo 已提交
185 186 187 188 189 190 191 192 193 194 195 196 197
    def create_info(self):
        fluid.default_startup_program().global_block().create_var(
            name="TDM_Tree_Info",
            dtype=fluid.core.VarDesc.VarType.INT32,
            shape=[self.node_nums, 3 + self.child_nums],
            persistable=True,
            initializer=fluid.initializer.ConstantInitializer(0))
        fluid.default_main_program().global_block().create_var(
            name="TDM_Tree_Info",
            dtype=fluid.core.VarDesc.VarType.INT32,
            shape=[self.node_nums, 3 + self.child_nums],
            persistable=True)

C
chengmo 已提交
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487
    def avg_loss(self):
        avg_cost = fluid.layers.reduce_mean(self.mask_cost)
        self._cost = avg_cost

    def metrics(self):
        auc, batch_auc, _ = fluid.layers.auc(input=self._predict,
                                             label=self.mask_label,
                                             num_thresholds=2 ** 12,
                                             slide_steps=20)
        self._metrics["AUC"] = auc
        self._metrics["BATCH_AUC"] = batch_auc
        self._metrics["BATCH_LOSS"] = self._cost

    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=self.act,
                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 _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 not isinstance(node, list):
            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=self.act,
                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

    """ -------- Infer network detail ------- """

    def infer_input(self):
        input_emb = fluid.layers.data(
            name="input_emb",
            shape=[self.input_emb_size],
            dtype="float32",
        )
        self._data_var.append(input_emb)

        if self._platform != "LINUX":
            self._data_loader = fluid.io.DataLoader.from_generator(
                feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)

    def get_layer_list(self):
        """get layer list from layer_list.txt"""
        layer_list = []
        with open(self.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):
        """decide which layer to start infer"""
        self.get_layer_list()
        first_layer_id = 0
        for idx, layer_node in enumerate(self.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
        node_list = []
        mask_list = []
        for id in node_list:
            node_list.append(fluid.layers.fill_constant(
                [self.batch_size, 1], value=id, dtype='int64'))
            mask_list.append(fluid.layers.fill_constant(
                [self.batch_size, 1], value=0, dtype='int64'))

        self.first_layer_node = fluid.layers.concat(node_list, axis=1)
        self.first_layer_node_mask = fluid.layers.concat(mask_list, axis=1)

    def tdm_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 = self._data_var[0]

        node_score = []
        node_list = []

        current_layer_node = self.first_layer_node
        current_layer_node_mask = self.first_layer_node_mask
        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 = self.first_layer_node.shape[1]
            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_node_mask = fluid.layers.reshape(
                current_layer_node_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.layer_fc_infer(
                input_trans_emb, layer_idx)

            # 过每一层的分类器
            layer_classifier_res = self.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_node_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_node_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])
        self.res_item_re = fluid.layers.reshape(res_item, [-1, self.topK])

    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=self.act,
            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

    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=self.act,
            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