wehub-resource-sync
555 lines
19 KiB
Python
555 lines
19 KiB
Python
# Copyright (c) 2019 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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import logging
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import os
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import paddle
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import paddle.distributed.transpiler.distribute_transpiler as dist_transpiler
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from paddle import base
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from paddle.base.compiler import CompiledProgram
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from paddle.base.executor import Executor
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from paddle.base.framework import Program
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from paddle.base.incubate.checkpoint.checkpoint_saver import (
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CheckpointSaver,
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PaddleModel,
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)
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from paddle.distributed.fleet.meta_optimizers import RawProgramOptimizer
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from paddle.incubate.distributed.fleet.base import (
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DistributedOptimizer,
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Fleet,
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Mode,
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)
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from paddle.static import io
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class Collective(Fleet):
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def __init__(self):
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super().__init__(Mode.COLLECTIVE)
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self._local_ip = 0
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self.startup_program = None
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self._origin_program = None
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self._transpiled_program = None
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self.main_program = None
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self._checkpoint_prefix = "__paddle_fleet_checkpoint__"
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self._param_file_name = "_paddle_fleet_param__"
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def init_worker(self):
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logging.warning(
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"You should not call 'init_worker' method for collective mode."
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)
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def run_worker(self, main_programs=None, scopes=None):
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logging.warning(
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"You should not call 'run_worker' method for collective mode."
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)
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def init_server(self, model_dir=None):
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logging.warning(
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"You should not call 'init_server' method for collective mode."
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)
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def run_server(self):
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logging.warning(
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"You should not call 'run_server' method for collective mode."
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)
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def stop_worker(self):
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logging.warning(
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"You should not call 'stop_worker' method for collective mode."
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)
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def distributed_optimizer(self, optimizer, strategy=None):
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self._optimizer = CollectiveOptimizer(optimizer, strategy)
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return self._optimizer
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def save_inference_model(
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self,
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executor,
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path_prefix,
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feeded_vas=None,
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fetch_vars=None,
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program=None,
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legacy_format=False,
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):
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"""
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Prune the given `main_program` to build a new program especially for
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inference, and then save it and all related parameters to given
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`dirname` by the `executor`.
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"""
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assert isinstance(executor, Executor), (
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"In fleet.save_inference_model() function, executor must be as"
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" Executor type."
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)
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if program is None:
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program = self._origin_program
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assert isinstance(program, Program), (
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"In fleet.save_inference_model() function, main_program "
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"must be as Program type."
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)
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io.save_inference_model(
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path_prefix,
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feeded_vas,
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fetch_vars,
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executor,
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program=program,
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legacy_format=legacy_format,
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)
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def save_persistables(
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self, executor, dirname, main_program=None, filename=None
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):
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"""
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This function filters out all variables with `persistable==True` from
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the give `main_program` and then saves these variables to the folder
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`dirname` or file `filename`.
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The `dirname` is used to specify the folder where persistable variables
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are going to be saved. If you would like to save variables in separate
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files, set `filename` None; if you would like to save all variables in a
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single file, use `filename` to specify the file name.
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"""
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assert isinstance(executor, Executor), (
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"In fleet.save_inference_model() function, executor must be as"
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" Executor type."
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)
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if main_program is None:
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main_program = self._origin_program
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assert isinstance(main_program, Program), (
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"In fleet.save_inference_model() function, main_program "
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"must be as Program type."
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)
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paddle.distributed.io.save_persistables(
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executor, dirname, main_program, filename=filename
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)
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def save_checkpoint(
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self,
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executor,
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path,
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trainer_id,
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train_status,
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fs,
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main_program=None,
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local_cache_path=".cache",
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remain_all_checkpoint=True,
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):
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"""
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This function save persistables and current epoch num to path.
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"""
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if main_program is None:
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main_program = self._transpiled_program
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m = PaddleModel(executor, main_program)
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t = train_status
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c = CheckpointSaver(fs)
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real_path, checkpoint_no = c.save_checkpoint(
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path=path,
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slists=[m, t],
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trainer_id=trainer_id,
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local_cache_path=local_cache_path,
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)
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if not remain_all_checkpoint:
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c.clean_redundant_checkpoints(path)
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return real_path, checkpoint_no
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def load_checkpoint(
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self,
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executor,
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path,
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trainer_id,
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train_status,
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fs,
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main_program=None,
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local_cache_path=".cache",
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ignore_empty=True,
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):
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"""
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This function load persistables and current epoch num from path.
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"""
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if main_program is None:
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main_program = self._transpiled_program
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m = PaddleModel(executor, main_program)
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c = CheckpointSaver(fs)
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return c.load_checkpoint(
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path,
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[m, train_status],
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trainer_id=trainer_id,
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ignore_empty=ignore_empty,
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local_cache_path=local_cache_path,
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)
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fleet = Collective()
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class DistributedStrategy(base.BuildStrategy):
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"""
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Init function of DistributedStrategy
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"""
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def __init__(self):
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super().__init__()
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self.use_local_sgd = False
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self.use_dist_fc = False
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self.dist_fc_config = None # DistFCConfig
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self.mode = "nccl2" # or collective
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self.collective_mode = None # local_sgd or grad_allreduce
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self.nccl_comm_num = 1
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self.forward_recompute = False # use RecomputeOptimizer
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self.recompute_checkpoints = []
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self.use_amp = False # use mixed precision optimizer
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self.amp_loss_scaling = 2**15
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# configurations below are used for unit test
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self._ut4grad_allreduce = False
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class CollectiveOpBasedOptimizer(DistributedOptimizer):
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"""
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Collective Operator Base Class For Distributed Optimizer
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The class is invisible to a user
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"""
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def __init__(self, optimizer, strategy=None):
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assert isinstance(strategy, DistributedStrategy), (
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"strategy must be DistributedStrategy"
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)
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super().__init__(optimizer, strategy)
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def backward(
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self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None,
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callbacks=None,
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):
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return self._optimizer.backward(
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loss, startup_program, parameter_list, no_grad_set, callbacks
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)
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def apply_gradients(self, params_grads):
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return self._optimizer.apply_gradients(params_grads)
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class CollectiveOptimizer(DistributedOptimizer):
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"""
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DistributedOptimizer is a wrapper for paddle.base.optimizer
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A user should pass a paddle.base.optimizer to DistributedOptimizer
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minimize() function is implemented.
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DistributedOptimizer is the starting point for a user who wants to
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run distributed training. The optimized information will be stored in
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Fleet() instance who holds the global information about current distributed
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training.
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"""
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def __init__(self, optimizer, strategy=DistributedStrategy()):
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if strategy is None:
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strategy = DistributedStrategy()
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super().__init__(optimizer, strategy)
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self._forward_recompute = strategy.forward_recompute
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if not isinstance(strategy.recompute_checkpoints, list):
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raise ValueError(
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"DistStrategy.recompute_checkpoints should be a List"
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)
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self._recompute_checkpoints = strategy.recompute_checkpoints
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self._use_amp = strategy.use_amp
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self._amp_loss_scaling = strategy.amp_loss_scaling
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self.print_config = False
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def backward(
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self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None,
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callbacks=None,
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):
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return self._optimizer.backward(
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loss, startup_program, parameter_list, no_grad_set, callbacks
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)
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def apply_gradients(self, params_grads):
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return self._optimizer.apply_gradients(params_grads)
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def _check_condition(self, name, **kwargs):
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for k, v in kwargs.items():
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if v is True:
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raise AssertionError(f"you can't use {name} and {k} together")
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def _check_collective_mode(self, main_program, optimizer, strategy):
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"""
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Check the conflict conditions.
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"""
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if strategy.use_local_sgd:
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strategy.mode = "collective"
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strategy.collective_mode = "local_sgd"
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self._check_condition(
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"use_local_sgd",
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use_dgc=main_program._enable_dgc,
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use_dist_fc=strategy.use_dist_fc,
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use_lamb=main_program._use_lamb,
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)
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if strategy.use_dist_fc:
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self._check_condition(
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"use_dist_fc",
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use_dgc=main_program._enable_dgc,
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use_local_sgd=strategy.use_local_sgd,
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use_lamb=main_program._use_lamb,
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)
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assert strategy.dist_fc_config is not None, (
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"DistributedStrategy.dist_fc_config should be set"
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)
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if strategy._ut4grad_allreduce:
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strategy.mode = "collective"
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strategy.collective_mode = "grad_allreduce"
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self._check_condition(
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"_ut4grad_allreduce",
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use_dgc=main_program._enable_dgc,
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use_lamb=main_program._use_lamb,
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)
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if (
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self._strategy.collective_mode == "local_sgd"
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or self._strategy.collective_mode == "grad_allreduce"
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):
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assert self._strategy.mode == "collective", (
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"local_sgd and grad_allreduce can be used under collective mode"
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)
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def _transpile(self, startup_program, main_program):
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"""
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Transpile the programs to distributed programs. And add the variables.
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"""
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worker_endpoints = fleet.worker_endpoints()
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trainer_id = fleet.worker_index()
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current_endpoint = fleet.worker_endpoints()[trainer_id]
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worker_endpoints_env = ','.join(worker_endpoints)
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trainers_num = fleet.worker_num()
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if self.print_config:
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print(
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f"worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} \
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trainer_id:{trainer_id}"
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)
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# call transpiler
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config = dist_transpiler.DistributeTranspilerConfig()
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config.mode = self._strategy.mode
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config.collective_mode = self._strategy.collective_mode
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config.nccl_comm_num = self._strategy.nccl_comm_num
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config.use_hierarchical_allreduce = (
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self._strategy.use_hierarchical_allreduce
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)
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config.hierarchical_allreduce_inter_nranks = (
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self._strategy.hierarchical_allreduce_inter_nranks
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)
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t = dist_transpiler.DistributeTranspiler(config=config)
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t.transpile(
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trainer_id=trainer_id,
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trainers=worker_endpoints_env,
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startup_program=startup_program,
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program=main_program,
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current_endpoint=current_endpoint,
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)
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def _get_node_ips_from_endpoints(self, endpoints):
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ss = set()
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ips = []
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for ep in endpoints:
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ip = ep.split(":")[0].strip()
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if ip not in ss:
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ss.add(ip)
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ips.append(ip)
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else:
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continue
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return ips
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def _node_num(self):
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worker_endpoints = fleet.worker_endpoints()
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current_endpoint = fleet.worker_endpoints()[fleet.worker_index()]
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worker_endpoints_env = ','.join(worker_endpoints)
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node_ips = self._get_node_ips_from_endpoints(worker_endpoints)
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node_ip = current_endpoint.split(":")[0].strip()
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node_num = len(node_ips)
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return node_num
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def _try_to_compile(self, startup_program, main_program):
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node_num = self._node_num()
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assert node_num >= 1, f"nccl2 node_num must >= 1, now:{node_num}"
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if node_num <= 1:
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if self._strategy.nccl_comm_num > 1:
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logging.warning(
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"set nccl_comm_num=1 since you only have 1 node."
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)
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self._strategy.nccl_comm_num = 1
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if self._strategy.use_hierarchical_allreduce:
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logging.warning(
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"set use_hierarchical_allreduce=False since you only have 1 node."
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)
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self._strategy.use_hierarchical_allreduce = False
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sync_allreduce = os.getenv("FLAGS_sync_nccl_allreduce")
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# NOTE. open sync_batch_norm will hang when use multi num_threads
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sync_batch_norm = self._strategy.sync_batch_norm
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if sync_batch_norm is not None and sync_batch_norm is True:
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self._strategy.nccl_comm_num = 1
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self._strategy.use_hierarchical_allreduce = False
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logging.warning(
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"use sync_batch_norm will hang when set num_threads > 1, so "
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"set num_threads=1, nccl_comm_num=1, use_hierarchical_allreduce=False."
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)
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if self.print_config:
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print(
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"node_num:",
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node_num,
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"use_hierarchical_allreduce:",
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self._strategy.use_hierarchical_allreduce,
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"nccl_comm_num:",
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self._strategy.nccl_comm_num,
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"FLAGS_sync_nccl_allreduce:",
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sync_allreduce,
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)
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self._transpile(startup_program, main_program)
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if self._strategy.mode == "collective":
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return main_program
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self._strategy.num_trainers = fleet.worker_num()
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self._strategy.trainer_id = fleet.worker_index()
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self._strategy.trainers_endpoints = fleet.worker_endpoints()
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self._strategy.enable_backward_optimizer_op_deps = True
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comm_opt = RawProgramOptimizer(self._optimizer)
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comm_opt.fuse_all_reduce_ops = True
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comm_opt.fuse_grad_size_in_num = True
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comm_opt.endpoints = self._strategy.trainers_endpoints
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comm_opt.current_endpoint = comm_opt.endpoints[fleet.worker_index()]
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comm_opt.rank = fleet.worker_index()
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comm_opt.nranks = fleet.worker_num()
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comm_opt.main_program = main_program
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if comm_opt.nranks > 1:
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comm_opt._transpile_main_program(self._loss)
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self._compiled_program = CompiledProgram(
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comm_opt.main_program, build_strategy=self._strategy
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)
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return self._compiled_program
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def raiseOptimizeError(self, strategy_name, optimize_name):
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raise ValueError(
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f"can not use {optimize_name} when you set DistStrategy.{strategy_name} "
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"as True"
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)
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def minimize(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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"""
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minimize a program through loss
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Args:
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loss (Variable|Variable List): loss variable or loss variable list to run optimization.
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startup_program (Program): startup_program for initializing parameters
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in `parameter_list`.
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parameter_list (list): list of Variables to update.
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no_grad_set (set|None): set of Variables should be ignored.
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Returns:
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tuple: (optimize_ops, params_grads) which are, list of operators appended;
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and list of (param, grad) Variables pair for optimization.
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Note that in parameter server mode, a worker will not get anything about optimize_os
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Because optimizer algorithms run on pserver side. We will make this usable in pserver
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process, but currently the optimization part is written into Fleet(). A user does not
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need to care about how to startup a pserver node.
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"""
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# check optimizer conflicts
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if self._forward_recompute:
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if self._recompute_checkpoints == []:
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raise ValueError(
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"please set strategy.recompute_checkpoints"
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"when set strategy.forward_recompute as True"
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)
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if self._optimizer.__class__.__name__ in [
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"RecomputeOptimizer",
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"OptimizerWithMixedPrecision",
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]:
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self.raiseOptimizeError(
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"forward_recompute", self._optimizer.__class__.__name__
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)
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self._optimizer = paddle.incubate.optimizer.RecomputeOptimizer(
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self._optimizer
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)
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self._optimizer._set_checkpoints(self._recompute_checkpoints)
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if self._use_amp:
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if self._optimizer.__class__.__name__ in [
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"OptimizerWithMixedPrecision",
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"DGCMomentumOptimizer",
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]:
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self.raiseOptimizeError(
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"mixed_precision", self._optimizer.__class__.__name__
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)
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self._optimizer = paddle.static.amp.decorate(
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self._optimizer,
|
|
init_loss_scaling=self._amp_loss_scaling,
|
|
use_dynamic_loss_scaling=True,
|
|
)
|
|
|
|
main_program = loss.block.program
|
|
if startup_program is None:
|
|
startup_program = base.default_startup_program()
|
|
fleet.startup_program = startup_program
|
|
|
|
self._loss = loss
|
|
|
|
self._check_collective_mode(
|
|
main_program, self._optimizer, self._strategy
|
|
)
|
|
|
|
optimize_ops, param_grads = self._optimizer.minimize(
|
|
loss, startup_program, parameter_list, no_grad_set=no_grad_set
|
|
)
|
|
|
|
fleet._origin_program = main_program.clone(for_test=False)
|
|
fleet._transpiled_program = main_program
|
|
fleet.main_program = self._try_to_compile(startup_program, main_program)
|
|
|
|
return optimize_ops, param_grads
|