static_model.py

# 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 paddle

from net import DLRMLayer


class StaticModel():
    def __init__(self, config):
        self.cost = None
        self.config = config
        self._init_hyper_parameters()

    def _init_hyper_parameters(self):
        self.is_distributed = False
        self.distributed_embedding = False

        if self.config.get("hyper_parameters.distributed_embedding", 0) == 1:
            self.distributed_embedding = True

        self.dense_feature_dim = self.config.get(
            "hyper_parameters.dense_input_dim")
        self.bot_layer_sizes = self.config.get(
            "hyper_parameters.bot_layer_sizes")
        self.sparse_feature_number = self.config.get(
            "hyper_parameters.sparse_feature_number")
        self.sparse_feature_dim = self.config.get(
            "hyper_parameters.sparse_feature_dim")
        self.top_layer_sizes = self.config.get(
            "hyper_parameters.top_layer_sizes")
        self.num_field = self.config.get("hyper_parameters.num_field")

        self.sparse_inputs_slot = self.config.get(
            "hyper_parameters.sparse_inputs_slots")
        self.dense_input_dim = self.config.get(
            "hyper_parameters.dense_input_dim")
        self.learning_rate = self.config.get(
            "hyper_parameters.optimizer.learning_rate")

    def create_feeds(self, is_infer=False):
        dense_input = paddle.static.data(
            name="dense_input",
            shape=[None, self.dense_input_dim],
            dtype="float32")

        sparse_input_ids = [
            paddle.static.data(
                name="C" + str(i), shape=[None, 1], dtype="int64")
            for i in range(1, self.sparse_inputs_slot)
        ]

        label = paddle.static.data(
            name="label", shape=[None, 1], dtype="int64")

        self._sparse_data_var = [label] + sparse_input_ids
        self._dense_data_var = [dense_input]

        feeds_list = [label] + sparse_input_ids + [dense_input]
        return feeds_list

    def net(self, input, is_infer=False):
        self.sparse_inputs = self._sparse_data_var[1:]
        self.dense_input = self._dense_data_var[0]
        self.label_input = self._sparse_data_var[0]
        sparse_number = self.sparse_inputs_slot - 1
        assert sparse_number == len(self.sparse_inputs)

        dlrm_model = DLRMLayer(
            dense_feature_dim=self.dense_feature_dim,
            bot_layer_sizes=self.bot_layer_sizes,
            sparse_feature_number=self.sparse_feature_number,
            sparse_feature_dim=self.sparse_feature_dim,
            top_layer_sizes=self.top_layer_sizes,
            num_field=self.num_field,
            self_interaction=False)

        raw_predict_2d = dlrm_model.forward(self.sparse_inputs,
                                            self.dense_input)

        cost = paddle.nn.functional.cross_entropy(
            input=raw_predict_2d, label=self.label_input)
        avg_cost = paddle.mean(x=cost)

        # pred = F.sigmoid(prediction)
        predict_2d = paddle.nn.functional.softmax(raw_predict_2d)
        self.predict = predict_2d

        auc, batch_auc_var, _ = paddle.static.auc(input=predict_2d,
                                                  label=self.label_input,
                                                  slide_steps=0)

        self.inference_target_var = auc
        if is_infer:
            fetch_dict = {'auc': auc}
            return fetch_dict

        self._cost = avg_cost
        fetch_dict = {'cost': avg_cost, 'auc': auc}
        return fetch_dict

    def create_optimizer(self, strategy=None):
        optimizer = paddle.optimizer.Adam(
            learning_rate=self.learning_rate, lazy_mode=True)
        if strategy != None:
            import paddle.distributed.fleet as fleet
            optimizer = fleet.distributed_optimizer(optimizer, strategy)
        optimizer.minimize(self._cost)

    def infer_net(self, input):
        return self.net(input, is_infer=True)