def get_all_inters_from_yaml(file, filters):
_envs = envs.load_yaml(file)
all_flattens = {}
def fatten_env_namespace(namespace_nests, local_envs):
for k, v in local_envs.items():
if isinstance(v, dict):
nests = copy.deepcopy(namespace_nests)
nests.append(k)
fatten_env_namespace(nests, v)
elif (k == "dataset" or k == "phase"
or k == "runner") and isinstance(v, list):
for i in v:
if i.get("name") is None:
raise ValueError("name must be in dataset list. ", v)
nests = copy.deepcopy(namespace_nests)
nests.append(k)
nests.append(i["name"])
fatten_env_namespace(nests, i)
else:
global_k = ".".join(namespace_nests + [k])
all_flattens[global_k] = v
fatten_env_namespace([], _envs)
ret = {}
for k, v in all_flattens.items():
for f in filters:
if k.startswith(f):
ret[k] = v
return ret
def master():
from paddlerec.core.engine.cluster.cluster import ClusterEngine
# Get fleet_mode & device
run_extras = get_all_inters_from_yaml(args.model, ["runner."])
mode = envs.get_runtime_environ("mode")
fleet_class = ".".join(["runner", mode, "fleet_mode"])
device_class = ".".join(["runner", mode, "device"])
fleet_mode = run_extras.get(fleet_class, "ps")
device = run_extras.get(device_class, "cpu")
device = device.upper()
fleet_mode = fleet_mode.upper()
if fleet_mode == "COLLECTIVE" and device != "GPU":
raise ValueError("COLLECTIVE can not be used without GPU")
# Get Thread nums
model_envs = envs.load_yaml(args.model)
phases_class = ".".join(["runner", mode, "phases"])
phase_names = run_extras.get(phases_class)
phases = []
all_phases = model_envs.get("phase")
if phase_names is None:
phases = all_phases
else:
for phase in all_phases:
if phase["name"] in phase_names:
phases.append(phase)
thread_num = []
for phase in phases:
thread_num.append(int(phase["thread_num"]))
max_thread_num = max(thread_num)
backend_envs = envs.load_yaml(args.backend)
flattens = envs.flatten_environs(backend_envs, "_")
flattens["engine_role"] = "MASTER"
flattens["engine_mode"] = envs.get_runtime_environ("mode")
flattens["engine_run_config"] = args.model
flattens["max_thread_num"] = max_thread_num
flattens["fleet_mode"] = fleet_mode
flattens["device"] = device
flattens["backend_yaml"] = args.backend
envs.set_runtime_environs(flattens)
launch = ClusterEngine(None, args.model)
return launch
def __init__(self, config=None):
self._status_processor = {}
self._place = fluid.CPUPlace()
self._exe = fluid.Executor(self._place)
self._exector_context = {}
self._context = {'status': 'uninit', 'is_exit': False}
self._config_yaml = config
self._config = envs.load_yaml(config)
def get_inters_from_yaml(file, filters):
_envs = envs.load_yaml(file)
flattens = envs.flatten_environs(_envs)
inters = {}
for k, v in flattens.items():
for f in filters:
if k.startswith(f):
inters[k] = v
return inters
def user_define_engine(engine_yaml):
_config = envs.load_yaml(engine_yaml)
envs.set_runtime_environs(_config)
train_location = envs.get_global_env("engine.file")
train_dirname = os.path.dirname(train_location)
base_name = os.path.splitext(os.path.basename(train_location))[0]
sys.path.append(train_dirname)
trainer_class = envs.lazy_instance_by_fliename(base_name,
"UserDefineTraining")
return trainer_class
def master():
role = "MASTER"
from paddlerec.core.engine.cluster.cluster import ClusterEngine
_envs = envs.load_yaml(args.backend)
flattens = envs.flatten_environs(_envs, "_")
flattens["engine_role"] = role
flattens["engine_run_config"] = args.model
flattens["engine_temp_path"] = tempfile.mkdtemp()
envs.set_runtime_environs(flattens)
print(envs.pretty_print_envs(flattens, ("Submit Envs", "Value")))
launch = ClusterEngine(None, args.model)
return launch
def local_cluster_engine(args):
from paddlerec.core.engine.local_cluster import LocalClusterEngine
_envs = envs.load_yaml(args.model)
run_extras = get_all_inters_from_yaml(args.model, ["train.", "runner."])
trainer_class = run_extras.get("runner." + _envs["mode"] + ".runner_class",
None)
if trainer_class:
trainer = trainer_class
else:
trainer = "GeneralTrainer"
executor_mode = "train"
distributed_strategy = run_extras.get(
"runner." + _envs["mode"] + ".distribute_strategy", "async")
worker_num = run_extras.get("runner." + _envs["mode"] + ".worker_num", 1)
server_num = run_extras.get("runner." + _envs["mode"] + ".server_num", 1)
selected_gpus = run_extras.get(
"runner." + _envs["mode"] + ".selected_gpus", "0")
fleet_mode = run_extras.get("runner." + _envs["mode"] + ".fleet_mode", "")
if fleet_mode == "":
device = run_extras.get("runner." + _envs["mode"] + ".device", "cpu")
if len(selected_gpus.split(",")) > 1 and device.upper() == "GPU":
fleet_mode = "COLLECTIVE"
else:
fleet_mode = "PS"
cluster_envs = {}
cluster_envs["server_num"] = server_num
cluster_envs["worker_num"] = worker_num
cluster_envs["selected_gpus"] = selected_gpus
cluster_envs["start_port"] = envs.find_free_port()
cluster_envs["fleet_mode"] = fleet_mode
cluster_envs["log_dir"] = "logs"
cluster_envs["train.trainer.trainer"] = trainer
cluster_envs["train.trainer.executor_mode"] = executor_mode
cluster_envs["train.trainer.strategy"] = distributed_strategy
cluster_envs["train.trainer.threads"] = "2"
cluster_envs["train.trainer.engine"] = "local_cluster"
cluster_envs["train.trainer.platform"] = envs.get_platform()
cluster_envs["CPU_NUM"] = "2"
print("launch {} engine with cluster to run model: {}".format(
trainer, args.model))
set_runtime_envs(cluster_envs, args.model)
launch = LocalClusterEngine(cluster_envs, args.model)
return launch
def get_worker_num(run_extras, workers):
_envs = envs.load_yaml(args.model)
mode = envs.get_runtime_environ("mode")
workspace = envs.get_runtime_environ("workspace")
phases_class = ".".join(["runner", mode, "phases"])
phase_names = run_extras.get(phases_class)
phases = []
all_phases = _envs.get("phase")
if phase_names is None:
phases = all_phases
else:
for phase in all_phases:
if phase["name"] in phase_names:
phases.append(phase)
dataset_names = []
for phase in phases:
dataset_names.append(phase["dataset_name"])
datapaths = []
for dataset in _envs.get("dataset"):
if dataset["name"] in dataset_names:
datapaths.append(dataset["data_path"])
if not datapaths:
raise ValueError("data path must exist for training/inference")
datapaths = [
envs.workspace_adapter_by_specific(path, workspace)
for path in datapaths
]
all_workers = [len(os.listdir(path)) for path in datapaths]
all_workers.append(workers)
max_worker_num = min(all_workers)
if max_worker_num >= workers:
return workers
print(
"phases do not have enough datas for training, set worker/gpu cards num from {} to {}"
.format(workers, max_worker_num))
return max_worker_num
def local_mpi_engine(args):
print("launch cluster engine with cluster to run model: {}".format(
args.model))
from paddlerec.core.engine.local_mpi import LocalMPIEngine
print("use 1X1 MPI ClusterTraining at localhost to run model: {}".format(
args.model))
mpi = util.run_which("mpirun")
if not mpi:
raise RuntimeError("can not find mpirun, please check environment")
_envs = envs.load_yaml(args.model)
run_extras = get_all_inters_from_yaml(args.model, ["train.", "runner."])
trainer_class = run_extras.get("runner." + _envs["mode"] + ".runner_class",
None)
executor_mode = "train"
distributed_strategy = run_extras.get(
"runner." + _envs["mode"] + ".distribute_strategy", "async")
fleet_mode = run_extras.get("runner." + _envs["mode"] + ".fleet_mode",
"ps")
if trainer_class:
trainer = trainer_class
else:
trainer = "GeneralTrainer"
cluster_envs = {}
cluster_envs["mpirun"] = mpi
cluster_envs["train.trainer.trainer"] = trainer
cluster_envs["log_dir"] = "logs"
cluster_envs["train.trainer.engine"] = "local_cluster"
cluster_envs["train.trainer.executor_mode"] = executor_mode
cluster_envs["fleet_mode"] = fleet_mode
cluster_envs["train.trainer.strategy"] = distributed_strategy
cluster_envs["train.trainer.threads"] = "2"
cluster_envs["train.trainer.engine"] = "local_cluster"
cluster_envs["train.trainer.platform"] = envs.get_platform()
set_runtime_envs(cluster_envs, args.model)
launch = LocalMPIEngine(cluster_envs, args.model)
return launch
def worker():
role = "WORKER"
_envs = envs.load_yaml(args.model)
run_extras = get_all_inters_from_yaml(args.model,
["train.", "runner."])
trainer_class = run_extras.get(
"runner." + _envs["mode"] + ".trainer_class", None)
if trainer_class:
trainer = trainer_class
else:
trainer = "GeneralTrainer"
executor_mode = "train"
distributed_strategy = run_extras.get(
"runner." + _envs["mode"] + ".distribute_strategy", "async")
selected_gpus = run_extras.get(
"runner." + _envs["mode"] + ".selected_gpus", "0")
fleet_mode = run_extras.get("runner." + _envs["mode"] + ".fleet_mode",
"ps")
cluster_envs = {}
cluster_envs["selected_gpus"] = selected_gpus
cluster_envs["fleet_mode"] = fleet_mode
cluster_envs["train.trainer.trainer"] = trainer
cluster_envs["train.trainer.executor_mode"] = executor_mode
cluster_envs["train.trainer.engine"] = "cluster"
cluster_envs["train.trainer.strategy"] = distributed_strategy
cluster_envs["train.trainer.threads"] = envs.get_runtime_environ(
"CPU_NUM")
cluster_envs["train.trainer.platform"] = envs.get_platform()
print("launch {} engine with cluster to with model: {}".format(
trainer, args.model))
set_runtime_envs(cluster_envs, args.model)
trainer = TrainerFactory.create(args.model)
return trainer
def single_infer_engine(args):
_envs = envs.load_yaml(args.model)
run_extras = get_all_inters_from_yaml(args.model, ["runner."])
mode = envs.get_runtime_environ("mode")
trainer_class = ".".join(["runner", mode, "trainer_class"])
fleet_class = ".".join(["runner", mode, "fleet_mode"])
device_class = ".".join(["runner", mode, "device"])
selected_gpus_class = ".".join(["runner", mode, "selected_gpus"])
trainer = run_extras.get(trainer_class, "GeneralTrainer")
fleet_mode = run_extras.get(fleet_class, "ps")
device = run_extras.get(device_class, "cpu")
selected_gpus = run_extras.get(selected_gpus_class, "0")
executor_mode = "infer"
single_envs = {}
if device.upper() == "GPU":
selected_gpus_num = len(selected_gpus.split(","))
if selected_gpus_num != 1:
raise ValueError(
"Single Mode Only Support One GPU, Set Local Cluster Mode to use Multi-GPUS"
)
single_envs["selsected_gpus"] = selected_gpus
single_envs["FLAGS_selected_gpus"] = selected_gpus
single_envs["train.trainer.trainer"] = trainer
single_envs["train.trainer.executor_mode"] = executor_mode
single_envs["fleet_mode"] = fleet_mode
single_envs["train.trainer.threads"] = "2"
single_envs["train.trainer.platform"] = envs.get_platform()
single_envs["train.trainer.engine"] = "single"
set_runtime_envs(single_envs, args.model)
trainer = TrainerFactory.create(args.model)
return trainer
def __init__(self, config=None):
self._status_processor = {}
self.model = None
self.inference_models = []
self.increment_models = []
self._exector_context = {}
self._context = {'status': 'uninit', 'is_exit': False}
self._context["config_yaml"] = config
self._model = {}
self._dataset = {}
self._runner_name = envs.get_runtime_environ("mode")
self._context["runner_name"] = self._runner_name
phase_names = envs.get_global_env(
"runner." + self._runner_name + ".phases", None)
_config = envs.load_yaml(config)
self._context["env"] = _config
self._context["dataset"] = _config.get("dataset")
phases = []
if phase_names is None:
phases = _config.get("phase")
else:
for phase in _config.get("phase"):
if phase["name"] in phase_names:
phases.append(phase)
self._context["phases"] = phases
print("PaddleRec: Runner {} Begin".format(self._runner_name))
self.which_engine()
self.which_device()
self.which_fleet_mode()
self.which_executor_mode()
self.legality_check()
def yaml_validation(config):
all_checkers, require_checkers = register()
_config = envs.load_yaml(config)
flattens = envs.flatten_environs(_config)
for required in require_checkers:
if required not in flattens.keys():
print("\ncan not find {} in yaml, which is required\n".format(
required))
return False
for name, flatten in flattens.items():
checker = all_checkers.get(name, None)
if not checker:
continue
ret = checker.is_valid(name, flattens)
if not ret:
return False
return True
def yaml_validation(config):
all_checkers = register()
require_checkers = []
for name, checker in all_checkers.items():
if checker.required:
require_checkers.append(name)
_config = envs.load_yaml(config)
for required in require_checkers:
if required not in _config.keys():
print("\ncan not find {} in yaml, which is required\n".format(
required))
return False
for name, value in _config.items():
checker = all_checkers.get(name, None)
if checker:
ret = checker.is_valid(name, value)
if not ret:
return False
return True
def create(config):
_config = envs.load_yaml(config)
envs.set_global_envs(_config)
envs.update_workspace()
trainer = TrainerFactory._build_trainer(config)
return trainer
def __init__(self):
# get backend env
backend_yaml = envs.get_runtime_environ("backend_yaml")
_env = envs.load_yaml(backend_yaml)
self.backend_env = envs.flatten_environs(_env, ".")
self.cluster_env = {}
def __init__(self, config):
dg.MultiSlotDataGenerator.__init__(self)
_config = envs.load_yaml(config)
def __init__(self, config):
dg.MultiSlotDataGenerator.__init__(self)
_config = envs.load_yaml(config)
envs.set_global_envs(_config)
envs.update_workspace()
