test_recommender_system.py

#   Copyright (c) 2018 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 math
import sys
import os
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.framework as framework
import paddle.fluid.layers as layers
import paddle.fluid.nets as nets
from paddle.fluid.executor import Executor
from paddle.fluid.optimizer import SGDOptimizer

paddle.enable_static()

IS_SPARSE = True
USE_GPU = False
BATCH_SIZE = 256


def get_usr_combined_features():
    # FIXME(dzh) : old API integer_value(10) may has range check.
    # currently we don't have user configurated check.

    USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1

    uid = layers.data(name='user_id', shape=[1], dtype='int64')

    usr_emb = layers.embedding(input=uid,
                               dtype='float32',
                               size=[USR_DICT_SIZE, 32],
                               param_attr='user_table',
                               is_sparse=IS_SPARSE)

    usr_fc = layers.fc(input=usr_emb, size=32)

    USR_GENDER_DICT_SIZE = 2

    usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')

    usr_gender_emb = layers.embedding(input=usr_gender_id,
                                      size=[USR_GENDER_DICT_SIZE, 16],
                                      param_attr='gender_table',
                                      is_sparse=IS_SPARSE)

    usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)

    USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
    usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")

    usr_age_emb = layers.embedding(input=usr_age_id,
                                   size=[USR_AGE_DICT_SIZE, 16],
                                   is_sparse=IS_SPARSE,
                                   param_attr='age_table')

    usr_age_fc = layers.fc(input=usr_age_emb, size=16)

    USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
    usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")

    usr_job_emb = layers.embedding(input=usr_job_id,
                                   size=[USR_JOB_DICT_SIZE, 16],
                                   param_attr='job_table',
                                   is_sparse=IS_SPARSE)

    usr_job_fc = layers.fc(input=usr_job_emb, size=16)

    concat_embed = layers.concat(
        input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)

    usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")

    return usr_combined_features


def get_mov_combined_features():

    MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1

    mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')

    mov_emb = layers.embedding(input=mov_id,
                               dtype='float32',
                               size=[MOV_DICT_SIZE, 32],
                               param_attr='movie_table',
                               is_sparse=IS_SPARSE)

    mov_fc = layers.fc(input=mov_emb, size=32)

    CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())

    category_id = layers.data(name='category_id',
                              shape=[1],
                              dtype='int64',
                              lod_level=1)

    mov_categories_emb = layers.embedding(input=category_id,
                                          size=[CATEGORY_DICT_SIZE, 32],
                                          is_sparse=IS_SPARSE)

    mov_categories_hidden = layers.sequence_pool(input=mov_categories_emb,
                                                 pool_type="sum")

    MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())

    mov_title_id = layers.data(name='movie_title',
                               shape=[1],
                               dtype='int64',
                               lod_level=1)

    mov_title_emb = layers.embedding(input=mov_title_id,
                                     size=[MOV_TITLE_DICT_SIZE, 32],
                                     is_sparse=IS_SPARSE)

    mov_title_conv = nets.sequence_conv_pool(input=mov_title_emb,
                                             num_filters=32,
                                             filter_size=3,
                                             act="tanh",
                                             pool_type="sum")

    concat_embed = layers.concat(
        input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)

    # FIXME(dzh) : need tanh operator
    mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")

    return mov_combined_features


def model():
    usr_combined_features = get_usr_combined_features()
    mov_combined_features = get_mov_combined_features()

    # need cos sim
    inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
    scale_infer = layers.scale(x=inference, scale=5.0)

    label = layers.data(name='score', shape=[1], dtype='float32')
    square_cost = layers.square_error_cost(input=scale_infer, label=label)
    avg_cost = layers.mean(square_cost)

    return scale_infer, avg_cost


def train(use_cuda, save_dirname, is_local=True):
    scale_infer, avg_cost = model()

    # test program
    test_program = fluid.default_main_program().clone(for_test=True)

    sgd_optimizer = SGDOptimizer(learning_rate=0.2)
    sgd_optimizer.minimize(avg_cost)

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

    exe = Executor(place)

    train_reader = paddle.batch(paddle.reader.shuffle(
        paddle.dataset.movielens.train(), buf_size=8192),
                                batch_size=BATCH_SIZE)
    test_reader = paddle.batch(paddle.dataset.movielens.test(),
                               batch_size=BATCH_SIZE)

    feed_order = [
        'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', 'category_id',
        'movie_title', 'score'
    ]

    def train_loop(main_program):
        exe.run(framework.default_startup_program())

        feed_list = [
            main_program.global_block().var(var_name) for var_name in feed_order
        ]
        feeder = fluid.DataFeeder(feed_list, place)

        PASS_NUM = 100
        for pass_id in range(PASS_NUM):
            for batch_id, data in enumerate(train_reader()):
                # train a mini-batch
                outs = exe.run(program=main_program,
                               feed=feeder.feed(data),
                               fetch_list=[avg_cost])
                out = np.array(outs[0])
                if (batch_id + 1) % 10 == 0:
                    avg_cost_set = []
                    for test_data in test_reader():
                        avg_cost_np = exe.run(program=test_program,
                                              feed=feeder.feed(test_data),
                                              fetch_list=[avg_cost])
                        avg_cost_set.append(avg_cost_np[0])
                        break  # test only 1 segment for speeding up CI

                    # get test avg_cost
                    test_avg_cost = np.array(avg_cost_set).mean()
                    if test_avg_cost < 6.0:
                        # if avg_cost less than 6.0, we think our code is good.
                        if save_dirname is not None:
                            fluid.io.save_inference_model(
                                save_dirname, [
                                    "user_id", "gender_id", "age_id", "job_id",
                                    "movie_id", "category_id", "movie_title"
                                ], [scale_infer], exe)
                        return

                if math.isnan(float(out[0])):
                    sys.exit("got NaN loss, training failed.")

    if is_local:
        train_loop(fluid.default_main_program())
    else:
        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
        eplist = []
        for ip in pserver_ips.split(","):
            eplist.append(':'.join([ip, port]))
        pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
        trainers = int(os.getenv("PADDLE_TRAINERS"))
        current_endpoint = os.getenv("POD_IP") + ":" + port
        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
        t = fluid.DistributeTranspiler()
        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
        if training_role == "PSERVER":
            pserver_prog = t.get_pserver_program(current_endpoint)
            pserver_startup = t.get_startup_program(current_endpoint,
                                                    pserver_prog)
            exe.run(pserver_startup)
            exe.run(pserver_prog)
        elif training_role == "TRAINER":
            train_loop(t.get_trainer_program())


def infer(use_cuda, save_dirname=None):
    if save_dirname is None:
        return

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)

    inference_scope = fluid.core.Scope()
    with fluid.scope_guard(inference_scope):
        # Use fluid.io.load_inference_model to obtain the inference program desc,
        # the feed_target_names (the names of variables that will be fed
        # data using feed operators), and the fetch_targets (variables that
        # we want to obtain data from using fetch operators).
        [inference_program, feed_target_names,
         fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)

        # Use the first data from paddle.dataset.movielens.test() as input
        assert feed_target_names[0] == "user_id"
        # Use create_lod_tensor(data, recursive_sequence_lengths, place) API
        # to generate LoD Tensor where `data` is a list of sequences of index
        # numbers, `recursive_sequence_lengths` is the length-based level of detail
        # (lod) info associated with `data`.
        # For example, data = [[10, 2, 3], [2, 3]] means that it contains
        # two sequences of indexes, of length 3 and 2, respectively.
        # Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
        # level of detail info, indicating that `data` consists of two sequences
        # of length 3 and 2, respectively.
        user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)

        assert feed_target_names[1] == "gender_id"
        gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)

        assert feed_target_names[2] == "age_id"
        age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)

        assert feed_target_names[3] == "job_id"
        job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)

        assert feed_target_names[4] == "movie_id"
        movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)

        assert feed_target_names[5] == "category_id"
        category_id = fluid.create_lod_tensor(
            [np.array([10, 8, 9], dtype='int64')], [[3]], place)

        assert feed_target_names[6] == "movie_title"
        movie_title = fluid.create_lod_tensor(
            [np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
            place)

        # Construct feed as a dictionary of {feed_target_name: feed_target_data}
        # and results will contain a list of data corresponding to fetch_targets.
        results = exe.run(inference_program,
                          feed={
                              feed_target_names[0]: user_id,
                              feed_target_names[1]: gender_id,
                              feed_target_names[2]: age_id,
                              feed_target_names[3]: job_id,
                              feed_target_names[4]: movie_id,
                              feed_target_names[5]: category_id,
                              feed_target_names[6]: movie_title
                          },
                          fetch_list=fetch_targets,
                          return_numpy=False)
        print("inferred score: ", np.array(results[0]))


def main(use_cuda):
    if use_cuda and not fluid.core.is_compiled_with_cuda():
        return

    # Directory for saving the inference model
    save_dirname = "recommender_system.inference.model"

    train(use_cuda, save_dirname)
    infer(use_cuda, save_dirname)


if __name__ == '__main__':
    main(USE_GPU)