wehub-resource-sync
601 lines
21 KiB
Python
Executable File
601 lines
21 KiB
Python
Executable File
# Copyright (c) 2022 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 argparse
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import ast
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import copy
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import os
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import struct
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import sys
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import numpy as np
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import yaml
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from ps_dnn_model import StaticModel
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import paddle
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from paddle.distributed import fleet
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from paddle.distributed.fleet.base import role_maker
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from paddle.distributed.ps.utils.ps_program_builder import (
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debug_program,
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logger,
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new_pass,
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ps_log_root_dir,
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)
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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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def is_distributed_env():
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node_role = os.getenv("TRAINING_ROLE")
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print(f"-- Role: {node_role} --")
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if node_role is None:
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return False
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else:
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return True
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class YamlHelper:
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def load_yaml(self, yaml_file, other_part=None):
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part_list = ["runner", "hyper_parameters"]
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if other_part:
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part_list += other_part
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running_config = self.get_all_inters_from_yaml(yaml_file, part_list)
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running_config = self.workspace_adapter(running_config)
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return running_config
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def print_yaml(self, config):
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print(self.pretty_print_envs(config))
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def parse_yaml(self, config):
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vs = [int(i) for i in yaml.__version__.split(".")]
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if vs[0] < 5:
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use_full_loader = False
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elif vs[0] > 5:
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use_full_loader = True
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else:
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if vs[1] >= 1:
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use_full_loader = True
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else:
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use_full_loader = False
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if os.path.isfile(config):
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with open(config, 'r', encoding="utf-8") as rb:
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if use_full_loader:
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_config = yaml.load(rb.read(), Loader=yaml.FullLoader)
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else:
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_config = yaml.load(rb.read())
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return _config
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else:
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raise ValueError(f"config {config} can not be supported")
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def get_all_inters_from_yaml(self, file, filters):
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_envs = self.parse_yaml(file)
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all_flattens = {}
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def fatten_env_namespace(namespace_nests, local_envs):
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for k, v in local_envs.items():
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if isinstance(v, dict):
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nests = copy.deepcopy(namespace_nests)
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nests.append(k)
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fatten_env_namespace(nests, v)
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else:
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global_k = ".".join([*namespace_nests, k])
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all_flattens[global_k] = v
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fatten_env_namespace([], _envs)
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ret = {}
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for k, v in all_flattens.items():
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for f in filters:
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if k.startswith(f):
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ret[k] = v
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return ret
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def workspace_adapter(self, config):
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workspace = config.get("workspace")
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for k, v in config.items():
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if isinstance(v, str) and "{workspace}" in v:
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config[k] = v.replace("{workspace}", workspace)
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return config
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def pretty_print_envs(self, envs, header=None):
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spacing = 2
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max_k = 40
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max_v = 45
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for k, v in envs.items():
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max_k = max(max_k, len(k))
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h_format = " " + "|{{:>{}s}}{}{{:^{}s}}|\n".format(
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max_k, " " * spacing, max_v
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)
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l_format = " " + f"|{{:>{max_k}s}}{{}}{{:^{max_v}s}}|\n"
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length = max_k + max_v + spacing
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border = " +" + "".join(["="] * length) + "+"
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line = " +" + "".join(["-"] * length) + "+"
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draws = ""
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draws += border + "\n"
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if header:
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draws += h_format.format(header[0], header[1])
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else:
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draws += h_format.format("Ps Benchmark Envs", "Value")
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draws += line + "\n"
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for k, v in sorted(envs.items()):
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if isinstance(v, str) and len(v) >= max_v:
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str_v = "... " + v[-41:]
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else:
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str_v = v
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draws += l_format.format(k, " " * spacing, str(str_v))
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draws += border
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_str = f"\n{draws}\n"
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return _str
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def get_user_defined_strategy(config):
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if not is_distributed_env():
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logger.warning(
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"Not Find Distributed env, Change To local train mode. If you want train with fleet, please use [fleetrun] command."
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)
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# return None
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sync_mode = config.get("runner.sync_mode")
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assert sync_mode in ["async", "sync", "geo", "heter", "gpubox"]
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if sync_mode == "sync":
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = False
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elif sync_mode == "async":
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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strategy.is_fl_ps_mode = (
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True if config.get("runner.is_fl_ps_mode") == 1 else False
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)
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if strategy.is_fl_ps_mode:
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strategy.pipeline = False
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micro_num = 1
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strategy.pipeline_configs = {
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"accumulate_steps": micro_num
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} # num_microbatches
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elif sync_mode == "geo":
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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strategy.a_sync_configs = {"k_steps": config.get("runner.geo_step")}
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elif sync_mode == "heter":
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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strategy.a_sync_configs = {"heter_worker_device_guard": "gpu"}
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strategy.pipeline = True
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strategy.pipeline_configs = {
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"accumulate_steps": config.get('runner.micro_num')
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}
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elif sync_mode == "gpubox":
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print(f"sync_mode = {sync_mode}")
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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strategy.a_sync_configs = {"use_ps_gpu": 1}
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strategy.trainer_desc_configs = {
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"dump_fields_path": config.get("runner.dump_fields_path", ""),
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"dump_fields": config.get("runner.dump_fields", []),
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"dump_param": config.get("runner.dump_param", []),
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"stat_var_names": config.get("stat_var_names", []),
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"local_sparse": config.get("runner.local_sparse", []),
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"remote_sparse": config.get("runner.remote_sparse", []),
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}
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print("strategy:", strategy.trainer_desc_configs)
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if config.get("runner.fs_client.uri") is not None:
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strategy.fs_client_param = {
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"uri": config.get("runner.fs_client.uri", ""),
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"user": config.get("runner.fs_client.user", ""),
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"passwd": config.get("runner.fs_client.passwd", ""),
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"hadoop_bin": config.get("runner.fs_client.hadoop_bin", "hadoop"),
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}
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print("strategy:", strategy.fs_client_param)
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strategy.adam_d2sum = config.get("hyper_parameters.adam_d2sum", True)
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table_config = {}
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for x in config:
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if x.startswith("table_parameters"):
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table_name = x.split('.')[1]
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if table_name not in table_config:
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table_config[table_name] = {}
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table_config[table_name][x] = config[x]
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print("table_config:", table_config)
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strategy.sparse_table_configs = table_config
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print("strategy table config:", strategy.sparse_table_configs)
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a_sync_configs = strategy.a_sync_configs
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a_sync_configs["launch_barrier"] = False
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# a_sync_configs["launch_barrier"] = True
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strategy.a_sync_configs = a_sync_configs
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print("launch_barrier: ", strategy.a_sync_configs["launch_barrier"])
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return strategy
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def get_distributed_strategy(user_defined_strategy): # pslib
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from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
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StrategyFactory,
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)
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k_steps = user_defined_strategy.a_sync_configs["k_steps"]
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strategy = None
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if not user_defined_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_sync_strategy()
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if user_defined_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_async_strategy()
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if user_defined_strategy.a_sync and k_steps > 0:
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strategy = StrategyFactory.create_geo_strategy(k_steps)
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if not strategy:
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raise ValueError("k_steps must be invalid value, please check")
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return strategy
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def get_model(config):
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abs_dir = config['config_abs_dir']
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sys.path.append(abs_dir)
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static_model = StaticModel(config)
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return static_model
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def parse_args():
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parser = argparse.ArgumentParser("PsTest train script")
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parser.add_argument(
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'-m', '--config_yaml', type=str, required=True, help='config file path'
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)
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parser.add_argument(
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'-bf16',
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'--pure_bf16',
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type=ast.literal_eval,
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default=False,
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help="whether use bf16",
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)
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parser.add_argument(
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'--run_minimize', type=int, default=0, help="test single pass"
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)
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parser.add_argument(
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'--run_single_pass', type=int, default=0, help="test single pass"
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)
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parser.add_argument(
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'--run_the_one_ps', type=int, default=0, help="test the_one_ps"
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)
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parser.add_argument(
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'--debug_new_minimize', type=int, default=0, help="test single pass"
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)
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parser.add_argument(
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'--debug_new_pass', type=int, default=0, help="test single pass"
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)
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parser.add_argument(
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'--applied_pass_name', type=str, default="", help="test single pass"
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)
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parser.add_argument(
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'--debug_the_one_ps', type=int, default=0, help="test the_one_ps"
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)
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args = parser.parse_args()
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args.abs_dir = os.path.dirname(os.path.abspath(args.config_yaml))
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yaml_helper = YamlHelper()
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config = yaml_helper.load_yaml(args.config_yaml)
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config["yaml_path"] = args.config_yaml
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config["config_abs_dir"] = args.abs_dir
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config["pure_bf16"] = args.pure_bf16
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config['run_minimize'] = args.run_minimize
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config['run_single_pass'] = args.run_single_pass
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config['run_the_one_ps'] = args.run_the_one_ps
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config['debug_new_minimize'] = args.debug_new_minimize
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config['debug_new_pass'] = args.debug_new_pass
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config['applied_pass_name'] = args.applied_pass_name
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config['debug_the_one_ps'] = args.debug_the_one_ps
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yaml_helper.print_yaml(config)
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return config
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def bf16_to_fp32(val):
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return np.float32(struct.unpack('<f', struct.pack('<I', val << 16))[0])
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class DnnTrainer:
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def __init__(self, config):
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self.metrics = {}
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self.config = config
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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.train_result_dict = {}
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self.train_result_dict["speed"] = []
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self.model = None
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self.pure_bf16 = self.config['pure_bf16']
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self.role_maker = role_maker.PaddleCloudRoleMaker()
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def init_fleet_with_gloo(self, use_gloo=False):
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if use_gloo:
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os.environ["PADDLE_WITH_GLOO"] = "1"
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fleet.init(self.role_maker)
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else:
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fleet.init()
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if fleet.is_server():
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print(f"server: {fleet.server_index()} started")
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else:
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print(f"worker: {fleet.worker_index()} started")
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def run_minimize(self):
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self.init_fleet_with_gloo()
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self.model = get_model(self.config)
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print("cpu_num: {}".format(os.getenv("CPU_NUM")))
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self.input_data = self.model.create_feeds()
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self.metrics = self.model.net(self.input_data)
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loss = self.model._cost
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user_defined_strategy = get_user_defined_strategy(self.config)
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learning_rate = self.config.get(
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"hyper_parameters.optimizer.learning_rate"
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)
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sync_mode = self.config.get("runner.sync_mode")
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inner_optimizer = paddle.optimizer.Adam(learning_rate, lazy_mode=True)
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self.role_maker._generate_role() # 必要
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if self.config['debug_new_minimize'] == 1:
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print("entering run_minimize -- new")
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from paddle.distributed.fleet.meta_optimizers.ps_optimizer import (
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ParameterServerOptimizer,
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)
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ps_optimizer = ParameterServerOptimizer(inner_optimizer)
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ps_optimizer._set_basic_info(
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loss, self.role_maker, inner_optimizer, user_defined_strategy
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)
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ps_optimizer.minimize_impl(loss)
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else:
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print("entering run_minimize -- old")
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fleet_obj = fleet.distributed_optimizer(
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inner_optimizer, user_defined_strategy
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) # Fleet object
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fleet_obj.minimize(loss)
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if fleet.is_server():
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_main_file = (
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ps_log_root_dir
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+ sync_mode
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+ '_run_minimize'
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+ '_debug:_'
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+ str(self.config['debug_new_minimize'])
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+ '_server_main.prototxt'
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)
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debug_program(_main_file, loss.block.program)
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elif fleet.is_worker():
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_main_file = (
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ps_log_root_dir
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+ sync_mode
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+ '_run_minimize'
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+ '_debug:_'
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+ str(self.config['debug_new_minimize'])
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+ '_worker_main.prototxt'
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)
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debug_program(_main_file, loss.block.program)
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elif self.role_maker._is_heter_worker():
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_main_file = (
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ps_log_root_dir
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+ sync_mode
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+ '_run_minimize'
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+ '_debug:_'
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+ str(self.config['debug_new_minimize'])
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+ '_heter_worker_main.prototxt'
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)
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debug_program(_main_file, loss.block.program)
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def run_single_pass(self):
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self.init_fleet_with_gloo()
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self.model = get_model(config)
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input_data = self.model.create_feeds()
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metrics = self.model.net(input_data)
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loss = self.model._cost
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user_defined_strategy = get_user_defined_strategy(config)
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learning_rate = config.get("hyper_parameters.optimizer.learning_rate")
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sync_mode = self.config.get("runner.sync_mode")
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inner_optimizer = paddle.optimizer.Adam(learning_rate, lazy_mode=True)
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startup_program = paddle.static.default_startup_program()
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inner_optimizer.minimize(loss, startup_program)
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if self.config['debug_new_pass'] == 1:
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print(
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"entering run {} - new".format(str(config["applied_pass_name"]))
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)
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from paddle.distributed.fleet.meta_optimizers.ps_optimizer import (
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ParameterServerOptimizer,
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)
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ps_optimizer = ParameterServerOptimizer(inner_optimizer)
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ps_optimizer._set_basic_info(
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loss, self.role_maker, inner_optimizer, user_defined_strategy
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)
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ps_optimizer._set_origin_programs([loss])
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ps_optimizer._init_ps_pass_context(loss, startup_program)
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_main = ps_optimizer.pass_ctx._attrs['cloned_main']
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append_send_ops_pass = new_pass(
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config["applied_pass_name"], ps_optimizer.pass_ctx._attrs
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)
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append_send_ops_pass.apply([_main], [None], ps_optimizer.pass_ctx)
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else:
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print(
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"entering run {} - old".format(str(config["applied_pass_name"]))
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)
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from paddle.incubate.distributed.fleet.parameter_server.ir import (
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public,
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)
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dist_strategy = get_distributed_strategy(user_defined_strategy)
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compiled_config = public.CompileTimeStrategy(
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loss.block.program,
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startup_program,
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dist_strategy,
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self.role_maker,
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)
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_main = compiled_config.origin_main_program.clone()
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_startup = compiled_config.origin_startup_program.clone()
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from paddle.incubate.distributed.fleet.parameter_server.ir import (
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trainer_pass as worker,
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)
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_main = worker.append_send_ops_pass(_main, compiled_config)
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if fleet.is_server():
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_main_file = (
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ps_log_root_dir
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+ sync_mode
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+ "_"
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+ str(config["applied_pass_name"])
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+ '_debug:_'
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+ str(self.config['debug_new_pass'])
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+ '_server_main.prototxt'
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)
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debug_program(_main_file, _main)
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elif fleet.is_worker():
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_main_file = (
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ps_log_root_dir
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+ sync_mode
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+ "_"
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+ str(config["applied_pass_name"])
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+ '_debug:_'
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+ str(self.config['debug_new_pass'])
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+ '_worker_main.prototxt'
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)
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debug_program(_main_file, _main)
|
|
|
|
def run_the_one_ps(self):
|
|
self.init_fleet_with_gloo()
|
|
self.model = get_model(self.config)
|
|
self.input_data = self.model.create_feeds()
|
|
self.metrics = self.model.net(self.input_data)
|
|
loss = self.model._cost
|
|
user_defined_strategy = get_user_defined_strategy(self.config)
|
|
learning_rate = self.config.get(
|
|
"hyper_parameters.optimizer.learning_rate"
|
|
)
|
|
sync_mode = self.config.get("runner.sync_mode")
|
|
inner_optimizer = paddle.optimizer.Adam(learning_rate, lazy_mode=True)
|
|
|
|
self.role_maker._generate_role() # 必要
|
|
if self.config['debug_the_one_ps'] == 1:
|
|
print("entering run_the_one_ps -- new")
|
|
|
|
from paddle.distributed.fleet.meta_optimizers.ps_optimizer import (
|
|
ParameterServerOptimizer,
|
|
)
|
|
|
|
ps_optimizer = ParameterServerOptimizer(inner_optimizer)
|
|
ps_optimizer._set_basic_info(
|
|
loss, self.role_maker, inner_optimizer, user_defined_strategy
|
|
)
|
|
ps_optimizer.minimize_impl(loss)
|
|
|
|
from paddle.distributed.ps.the_one_ps import TheOnePSRuntime
|
|
|
|
_runtime_handle = (
|
|
TheOnePSRuntime()
|
|
) # ps 目录下重构版的 TheOnePSRuntime
|
|
_runtime_handle._set_basic_info(ps_optimizer.pass_ctx._attrs)
|
|
if fleet.is_worker():
|
|
worker_desc = (
|
|
_runtime_handle.ps_desc_builder.build_worker_desc()
|
|
)
|
|
with open(
|
|
ps_log_root_dir + sync_mode + '_' + 'new_worker_ps_desc',
|
|
'w',
|
|
) as f:
|
|
f.write(worker_desc)
|
|
if fleet.is_server():
|
|
server_desc = (
|
|
_runtime_handle.ps_desc_builder.build_server_desc()
|
|
)
|
|
with open(
|
|
ps_log_root_dir + sync_mode + '_' + 'new_server_ps_desc',
|
|
'w',
|
|
) as f:
|
|
f.write(server_desc)
|
|
|
|
else:
|
|
pass
|
|
'''
|
|
print("entering run_the_one_ps -- old")
|
|
fleet_obj = fleet.distributed_optimizer(
|
|
inner_optimizer, user_defined_strategy)
|
|
fleet_obj.minimize(loss)
|
|
if fleet.is_worker():
|
|
worker_desc = fleet_obj._runtime_handle._get_fleet_proto(is_server=False, is_sync=False)
|
|
server_desc = fleet_obj._runtime_handle._get_fleet_proto(is_server=True, is_sync=False)
|
|
with open(ps_log_root_dir + sync_mode + '_' + 'worker_ps_desc', 'w') as f:
|
|
f.write(str(worker_desc) + str(server_desc))
|
|
if fleet.is_server():
|
|
server_desc = fleet_obj._runtime_handle._get_fleet_proto(is_server=True, is_sync=False)
|
|
with open(ps_log_root_dir + sync_mode + '_' + 'server_ps_desc', 'w') as f:
|
|
f.write(str(server_desc) + str(fleet_obj._runtime_handle._get_fs_client_desc().to_string()))
|
|
'''
|
|
if fleet.is_server():
|
|
_main_file = (
|
|
ps_log_root_dir
|
|
+ sync_mode
|
|
+ '_run_the_one_ps'
|
|
+ '_debug:_'
|
|
+ str(self.config['debug_the_one_ps'])
|
|
+ '_server_main.prototxt'
|
|
)
|
|
debug_program(_main_file, loss.block.program)
|
|
elif fleet.is_worker():
|
|
_main_file = (
|
|
ps_log_root_dir
|
|
+ sync_mode
|
|
+ '_run_the_one_ps'
|
|
+ '_debug:_'
|
|
+ str(self.config['debug_the_one_ps'])
|
|
+ '_worker_main.prototxt'
|
|
)
|
|
debug_program(_main_file, loss.block.program)
|
|
elif self.role_maker._is_heter_worker():
|
|
_main_file = (
|
|
ps_log_root_dir
|
|
+ sync_mode
|
|
+ '_run_the_one_ps'
|
|
+ '_debug:_'
|
|
+ str(self.config['debug_the_one_ps'])
|
|
+ '_heter_worker_main.prototxt'
|
|
)
|
|
debug_program(_main_file, loss.block.program)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
config = parse_args()
|
|
print(">>>>>>>>>> python process started")
|
|
os.environ["CPU_NUM"] = str(config.get("runner.thread_num"))
|
|
benchmark_main = DnnTrainer(config)
|
|
if config['run_single_pass'] == 1:
|
|
benchmark_main.run_single_pass()
|
|
elif config['run_minimize'] == 1:
|
|
benchmark_main.run_minimize()
|
|
elif config['run_the_one_ps'] == 1:
|
|
benchmark_main.run_the_one_ps()
|