wehub-resource-sync
199 lines
6.9 KiB
Python
Executable File
199 lines
6.9 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import logging
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import os
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import sys
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import time
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import paddle
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from paddle.distributed import fleet
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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logging.basicConfig(
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format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO
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)
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logger = logging.getLogger(__name__)
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def get_dataset(inputs, config):
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dataset = paddle.distributed.InMemoryDataset()
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dataset._set_use_ps_gpu(config.get('runner.use_gpu'))
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pipe_cmd = config.get('runner.pipe_command')
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dataset.init(
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use_var=inputs,
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pipe_command=pipe_cmd,
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batch_size=32,
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thread_num=int(config.get('runner.thread_num')),
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fs_name=config.get("runner.fs_name", ""),
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fs_ugi=config.get("runner.fs_ugi", ""),
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)
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dataset.set_filelist(["train_data/sample_train.txt"])
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dataset.update_settings(
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parse_ins_id=config.get("runner.parse_ins_id", False),
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parse_content=config.get("runner.parse_content", False),
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)
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return dataset
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class Main:
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def __init__(self):
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self.metrics = {}
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self.input_data = None
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self.reader = None
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self.exe = None
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self.model = None
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self.PSGPU = None
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self.train_result_dict = {}
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self.train_result_dict["speed"] = []
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self.train_result_dict["auc"] = []
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def run(self):
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from ps_dnn_trainer import YamlHelper
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yaml_helper = YamlHelper()
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config_yaml_path = 'config_gpubox.yaml'
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self.config = yaml_helper.load_yaml(config_yaml_path)
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os.environ["CPU_NUM"] = str(self.config.get("runner.thread_num"))
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fleet.init()
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self.network()
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if fleet.is_server():
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self.run_server()
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elif fleet.is_worker():
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self.run_worker()
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fleet.stop_worker()
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logger.info("Run Success, Exit.")
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logger.info("-" * 100)
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def network(self):
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from ps_dnn_trainer import StaticModel, get_user_defined_strategy
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# self.model = get_model(self.config)
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self.model = StaticModel(self.config)
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self.input_data = self.model.create_feeds()
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self.init_reader()
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self.metrics = self.model.net(self.input_data)
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self.inference_target_var = self.model.inference_target_var
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logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
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# self.model.create_optimizer(get_strategy(self.config)
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user_defined_strategy = get_user_defined_strategy(self.config)
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optimizer = paddle.optimizer.Adam(0.01, lazy_mode=True)
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optimizer = fleet.distributed_optimizer(
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optimizer, user_defined_strategy
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)
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optimizer.minimize(self.model._cost)
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logger.info("end network.....")
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def run_server(self):
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logger.info("Run Server Begin")
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fleet.init_server(self.config.get("runner.warmup_model_path"))
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fleet.run_server()
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def run_worker(self):
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logger.info("Run Worker Begin")
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use_cuda = int(self.config.get("runner.use_gpu"))
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use_auc = self.config.get("runner.use_auc", False)
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place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
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self.exe = paddle.static.Executor(place)
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'''
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with open("./{}_worker_main_program.prototxt".format(
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fleet.worker_index()), 'w+') as f:
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f.write(str(paddle.static.default_main_program()))
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with open("./{}_worker_startup_program.prototxt".format(
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fleet.worker_index()), 'w+') as f:
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f.write(str(paddle.static.default_startup_program()))
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'''
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self.exe.run(paddle.static.default_startup_program())
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fleet.init_worker()
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'''
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save_model_path = self.config.get("runner.model_save_path")
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if save_model_path and (not os.path.exists(save_model_path)):
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os.makedirs(save_model_path)
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'''
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reader_type = self.config.get("runner.reader_type", None)
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epochs = int(self.config.get("runner.epochs"))
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sync_mode = self.config.get("runner.sync_mode")
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gpus_env = os.getenv("FLAGS_selected_gpus")
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self.PSGPU = paddle.framework.core.PSGPU()
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gpuslot = [int(i) for i in range(1, self.model.sparse_inputs_slots)]
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gpu_mf_sizes = [self.model.sparse_feature_dim - 1] * (
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self.model.sparse_inputs_slots - 1
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)
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self.PSGPU.set_slot_vector(gpuslot)
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self.PSGPU.set_slot_dim_vector(gpu_mf_sizes)
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self.PSGPU.init_gpu_ps([int(s) for s in gpus_env.split(",")])
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gpu_num = len(gpus_env.split(","))
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opt_info = paddle.static.default_main_program()._fleet_opt
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if use_auc is True:
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opt_info['stat_var_names'] = [
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self.model.stat_pos.name,
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self.model.stat_neg.name,
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]
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else:
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opt_info['stat_var_names'] = []
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for epoch in range(epochs):
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epoch_start_time = time.time()
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self.dataset_train_loop(epoch)
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epoch_time = time.time() - epoch_start_time
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self.PSGPU.end_pass()
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fleet.barrier_worker()
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self.reader.release_memory()
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logger.info(f"finish {epoch} epoch training....")
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self.PSGPU.finalize()
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def init_reader(self):
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if fleet.is_server():
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return
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# self.reader, self.file_list = get_reader(self.input_data, config)
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self.reader = get_dataset(self.input_data, self.config)
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def dataset_train_loop(self, epoch):
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start_time = time.time()
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self.reader.load_into_memory()
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print(
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f"self.reader.load_into_memory cost :{time.time() - start_time} seconds"
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)
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begin_pass_time = time.time()
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self.PSGPU.begin_pass()
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print(f"begin_pass cost:{time.time() - begin_pass_time} seconds")
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logger.info(f"Epoch: {epoch}, Running Dataset Begin.")
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fetch_info = [
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f"Epoch {epoch} Var {var_name}" for var_name in self.metrics
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]
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print_step = int(self.config.get("runner.print_interval"))
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self.exe.train_from_dataset(
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program=paddle.static.default_main_program(),
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dataset=self.reader,
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debug=self.config.get("runner.dataset_debug"),
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)
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if __name__ == "__main__":
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paddle.enable_static()
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benchmark_main = Main()
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benchmark_main.run()
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