# 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. import numpy as np import os import paddle.v2 as paddle import paddle.v2.fluid as fluid import paddle.v2.fluid.core as core import paddle.v2.fluid.layers as layers import paddle.v2.fluid.nets as nets from paddle.v2.fluid.optimizer import SGDOptimizer IS_SPARSE = True BATCH_SIZE = 256 PASS_NUM = 100 def get_usr_combined_features(): 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') 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') 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) 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(x=square_cost) return avg_cost def func_feed(feeding, data, place): feed_tensors = {} for (key, idx) in feeding.iteritems(): tensor = core.LoDTensor() if key != "category_id" and key != "movie_title": if key == "score": numpy_data = np.array(map(lambda x: x[idx], data)).astype( "float32") else: numpy_data = np.array(map(lambda x: x[idx], data)).astype( "int64") else: numpy_data = map(lambda x: np.array(x[idx]).astype("int64"), data) lod_info = [len(item) for item in numpy_data] offset = 0 lod = [offset] for item in lod_info: offset += item lod.append(offset) numpy_data = np.concatenate(numpy_data, axis=0) tensor.set_lod([lod]) numpy_data = numpy_data.reshape([numpy_data.shape[0], 1]) tensor.set(numpy_data, place) feed_tensors[key] = tensor return feed_tensors def main(): cost = model() optimizer = SGDOptimizer(learning_rate=0.2) optimize_ops, params_grads = optimizer.minimize(cost) train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.movielens.train(), buf_size=8192), batch_size=BATCH_SIZE) place = fluid.CPUPlace() exe = fluid.Executor(place) t = fluid.DistributeTranspiler() # all parameter server endpoints list for spliting parameters pserver_endpoints = os.getenv("PSERVERS") # server endpoint for current node current_endpoint = os.getenv("SERVER_ENDPOINT") # run as trainer or parameter server training_role = os.getenv("TRAINING_ROLE", "TRAINER") t.transpile( optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) if training_role == "PSERVER": if not current_endpoint: print("need env SERVER_ENDPOINT") exit(1) 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": exe.run(fluid.default_startup_program()) trainer_prog = t.get_trainer_program() feeding = { 'user_id': 0, 'gender_id': 1, 'age_id': 2, 'job_id': 3, 'movie_id': 4, 'category_id': 5, 'movie_title': 6, 'score': 7 } for pass_id in range(PASS_NUM): for data in train_reader(): outs = exe.run(trainer_prog, feed=func_feed(feeding, data, place), fetch_list=[cost]) out = np.array(outs[0]) print("cost=" + str(out[0])) if out[0] < 6.0: print("Training complete. Average cost is less than 6.0.") # if avg cost less than 6.0, we think our code is good. exit(0) else: print("environment var TRAINER_ROLE should be TRAINER os PSERVER") if __name__ == '__main__': main()