forked from mindspore-Ecosystem/mindspore
182 lines
7.2 KiB
Python
182 lines
7.2 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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BGCF training script.
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"""
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import os
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import time
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from mindspore import Tensor
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import mindspore.context as context
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from mindspore.common import dtype as mstype
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from mindspore.train.serialization import save_checkpoint
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from mindspore.common import set_seed
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from src.bgcf import BGCF
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from src.utils import convert_item_id
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from src.callback import TrainBGCF
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from src.dataset import load_graph, create_dataset
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from model_utils.config import config
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from model_utils.moxing_adapter import moxing_wrapper
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from model_utils.device_adapter import get_device_id, get_device_num
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set_seed(1)
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def modelarts_pre_process():
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'''modelarts pre process function.'''
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def unzip(zip_file, save_dir):
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import zipfile
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s_time = time.time()
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if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
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zip_isexist = zipfile.is_zipfile(zip_file)
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if zip_isexist:
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fz = zipfile.ZipFile(zip_file, 'r')
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data_num = len(fz.namelist())
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print("Extract Start...")
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print("unzip file num: {}".format(data_num))
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data_print = int(data_num / 100) if data_num > 100 else 1
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i = 0
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for file in fz.namelist():
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if i % data_print == 0:
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print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
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i += 1
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fz.extract(file, save_dir)
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print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
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int(int(time.time() - s_time) % 60)))
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print("Extract Done.")
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else:
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print("This is not zip.")
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else:
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print("Zip has been extracted.")
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if config.need_modelarts_dataset_unzip:
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zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
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save_dir_1 = os.path.join(config.data_path)
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sync_lock = "/tmp/unzip_sync.lock"
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# Each server contains 8 devices as most.
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if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
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print("Zip file path: ", zip_file_1)
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print("Unzip file save dir: ", save_dir_1)
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unzip(zip_file_1, save_dir_1)
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print("===Finish extract data synchronization===")
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try:
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os.mknod(sync_lock)
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except IOError:
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pass
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while True:
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if os.path.exists(sync_lock):
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break
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time.sleep(1)
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print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
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config.ckptpath = os.path.join(config.output_path, config.ckptpath)
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if not os.path.isdir(config.ckptpath):
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os.makedirs(config.ckptpath)
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def run_train():
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"""Train"""
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context.set_context(mode=context.GRAPH_MODE,
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device_target=config.device_target,
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save_graphs=False)
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if config.device_target == "Ascend":
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context.set_context(device_id=get_device_id())
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if config.device_target == "GPU":
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context.set_context(enable_graph_kernel=True)
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train_graph, _, sampled_graph_list = load_graph(config.datapath)
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train_ds = create_dataset(train_graph, sampled_graph_list, config.workers, batch_size=config.batch_pairs,
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num_samples=config.raw_neighs, num_bgcn_neigh=config.gnew_neighs, num_neg=config.num_neg)
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num_user = train_graph.graph_info()["node_num"][0]
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num_item = train_graph.graph_info()["node_num"][1]
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num_pairs = train_graph.graph_info()['edge_num'][0]
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bgcfnet = BGCF([config.input_dim, num_user, num_item],
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config.embedded_dimension,
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config.activation,
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config.neighbor_dropout,
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num_user,
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num_item,
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config.input_dim)
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train_net = TrainBGCF(bgcfnet, config.num_neg, config.l2, config.learning_rate,
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config.epsilon, config.dist_reg)
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train_net.set_train(True)
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itr = train_ds.create_dict_iterator(config.num_epoch, output_numpy=True)
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num_iter = int(num_pairs / config.batch_pairs)
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for _epoch in range(1, config.num_epoch + 1):
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epoch_start = time.time()
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iter_num = 1
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for data in itr:
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u_id = Tensor(data["users"], mstype.int32)
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pos_item_id = Tensor(convert_item_id(data["items"], num_user), mstype.int32)
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neg_item_id = Tensor(convert_item_id(data["neg_item_id"], num_user), mstype.int32)
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pos_users = Tensor(data["pos_users"], mstype.int32)
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pos_items = Tensor(convert_item_id(data["pos_items"], num_user), mstype.int32)
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u_group_nodes = Tensor(data["u_group_nodes"], mstype.int32)
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u_neighs = Tensor(convert_item_id(data["u_neighs"], num_user), mstype.int32)
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u_gnew_neighs = Tensor(convert_item_id(data["u_gnew_neighs"], num_user), mstype.int32)
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i_group_nodes = Tensor(convert_item_id(data["i_group_nodes"], num_user), mstype.int32)
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i_neighs = Tensor(data["i_neighs"], mstype.int32)
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i_gnew_neighs = Tensor(data["i_gnew_neighs"], mstype.int32)
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neg_group_nodes = Tensor(convert_item_id(data["neg_group_nodes"], num_user), mstype.int32)
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neg_neighs = Tensor(data["neg_neighs"], mstype.int32)
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neg_gnew_neighs = Tensor(data["neg_gnew_neighs"], mstype.int32)
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train_loss = train_net(u_id,
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pos_item_id,
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neg_item_id,
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pos_users,
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pos_items,
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u_group_nodes,
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u_neighs,
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u_gnew_neighs,
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i_group_nodes,
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i_neighs,
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i_gnew_neighs,
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neg_group_nodes,
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neg_neighs,
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neg_gnew_neighs)
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if iter_num == num_iter:
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print('Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num,
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'loss',
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'{}, cost:{:.4f}'.format(train_loss, time.time() - epoch_start))
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iter_num += 1
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if _epoch % config.eval_interval == 0:
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save_checkpoint(bgcfnet, config.ckptpath + "/bgcf_epoch{}.ckpt".format(_epoch))
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if __name__ == "__main__":
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run_train()
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