diff --git a/docs/tutorials/MULTI_MACHINE_TRAINING_cn.md b/docs/tutorials/MULTI_MACHINE_TRAINING_cn.md new file mode 100644 index 0000000000000000000000000000000000000000..652994bf7536ad9e680112cc03ce05ca5425160a --- /dev/null +++ b/docs/tutorials/MULTI_MACHINE_TRAINING_cn.md @@ -0,0 +1,69 @@ +# 多机训练 + +## 简介 + +* 分布式训练的高性能，是飞桨的核心优势技术之一，在分类任务上，分布式训练可以达到几乎线性的加速比。 +[Fleet](https://github.com/PaddlePaddle/Fleet) 是用于 PaddlePaddle 分布式训练的高层 API，基于这套接口用户可以很容易切换到分布式训练程序。 +为了可以同时支持单机训练和多机训练，[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/) 采用 Fleet API 接口，可以同时支持单机训练和多机训练。 + +更多的分布式训练可以参考 [Fleet API设计文档](https://github.com/PaddlePaddle/Fleet/blob/develop/README.md)。 + + +## 使用方法 + +* 使用`tools/train_multi_machine.py`可以启动基于Fleet的训练，目前同时支持单机单卡、单机多卡与多机多卡的训练过程。 + +* 可选参数列表与`tools/train.py`完全相同，可以参考[入门使用文档](./GETTING_STARTED_cn.md)。 + +### 单机训练 + +* 训练脚本如下所示。 + +```bash +# 设置PYTHONPATH路径 +export PYTHONPATH=$PYTHONPATH:. +# 设置GPU卡号信息 +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +# 启动训练 +python -m paddle.distributed.launch \ + --selected_gpus 0,1,2,3,4,5,6,7 \ + tools/train_multi_machine.py \ + -c configs/faster_rcnn_r50_fpn_1x.yml +``` + +### 多机训练 + +* 训练脚本如下所示，其中ip1和ip2分别表示不同机器的ip地址，`PADDLE_TRAINER_ID`环境变量也是根据`cluster_node_ips`提供的ip顺序依次增大。 +* 注意：在这里如果需要启动多机实验，需要保证不同的机器的运行代码是完全相同的。 + +``` +export PYTHONPATH=$PYTHONPATH:. +# 设置GPU卡号信息 +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +# 启动训练 +node_ip=`hostname -i` +python -m paddle.distributed.launch \ + --use_paddlecloud \ + --cluster_node_ips ${ip1},${ip2} \ + --node_ip ${node_ip} \ + tools/train_multi_machine.py \ + -c configs/faster_rcnn_r50_fpn_1x.yml \ +``` + +## 训练时间统计 + +* 以Faster RCNN R50_vd FPN 1x实验为例，下面给出了基于Fleet分布式训练，不同机器的训练时间对比。 + * 这里均是在V100 GPU上展开的实验。 + * 1x实验指的是8卡，单卡batch size为2时，训练的minibatch数量为90000（当训练卡数或者batch size变化时，对应的学习率和总的迭代轮数也需要变化）。 + + + +| 模型 | 训练策略 | 机器数量 | 每台机器的GPU数量 | 训练时间 | COCO bbox mAP | 加速比 | +| :----------------------: | :------------: | :------------: | :---------------: | :----------: | :-----------: | :-----------: | +| Faster RCNN R50_vd FPN | 1x | 1 | 4 | 15.1h | 38.3% | - | +| Faster RCNN R50_vd FPN | 1x | 2 | 4 | 9.8h | 38.2% | 76% | +| Faster RCNN R50_vd FPN | 1x | 1 | 8 | 8.6h | 38.2% | - | +| Faster RCNN R50_vd FPN | 1x | 2 | 8 | 5.1h | 38.0% | 84% | + +* 由上图可知，2机8卡相比于单机8卡，加速比可以达到84%，2即4卡相比于单机4卡，加速比可以达到76%，而且精度几乎没有损失。 +* 1x实验相当于COCO数据集训练了约13个epoch，因此在trainer数量很多的时候，每个trainer可能无法训练完1个epoch，这会导致精度出现一些差异，这可以通过适当增加迭代轮数实现精度的对齐，我们实验发现，在训练多尺度3x实验时(配置文件：[configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml](../../configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml))，分布式训练与单机训练的模型精度是可以对齐的。 diff --git a/ppdet/data/reader.py b/ppdet/data/reader.py index 202f331fc126008d143e836074d31a3f80b54743..276a29eb0fbbb417d1ba2bff5366c68474521c64 100644 --- a/ppdet/data/reader.py +++ b/ppdet/data/reader.py @@ -16,6 +16,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os import copy import functools import collections @@ -278,6 +279,11 @@ class Reader(object): def reset(self): """implementation of Dataset.reset """ + if self._epoch < 0: + self._epoch = 0 + else: + self._epoch += 1 + self.indexes = [i for i in range(self.size())] if self._class_aware_sampling: self.indexes = np.random.choice( @@ -287,6 +293,8 @@ class Reader(object): p=self.img_weights) if self._shuffle: + trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0)) + np.random.seed(self._epoch + trainer_id) np.random.shuffle(self.indexes) if self._mixup_epoch > 0 and len(self.indexes) < 2: @@ -298,11 +306,6 @@ class Reader(object): "less than 2 samples") self._cutmix_epoch = -1 - if self._epoch < 0: - self._epoch = 0 - else: - self._epoch += 1 - self._pos = 0 def __next__(self): diff --git a/tools/train_multi_machine.py b/tools/train_multi_machine.py new file mode 100644 index 0000000000000000000000000000000000000000..2a82f0505612291fdbecae940110fe9462505825 --- /dev/null +++ b/tools/train_multi_machine.py @@ -0,0 +1,366 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os, sys +# add python path of PadleDetection to sys.path +parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) +if parent_path not in sys.path: + sys.path.append(parent_path) + +import time +import numpy as np +import random +import datetime +import six +from collections import deque +from paddle.fluid import profiler + +from paddle import fluid +from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter +from paddle.fluid.optimizer import ExponentialMovingAverage + +from ppdet.experimental import mixed_precision_context +from ppdet.core.workspace import load_config, merge_config, create +from ppdet.data.reader import create_reader + +from ppdet.utils import dist_utils +from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results +from ppdet.utils.stats import TrainingStats +from ppdet.utils.cli import ArgsParser +from ppdet.utils.check import check_gpu, check_version, check_config +import ppdet.utils.checkpoint as checkpoint + +from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy # new line 1 +from paddle.fluid.incubate.fleet.base import role_maker # new line 2 + +import logging +FORMAT = '%(asctime)s-%(levelname)s: %(message)s' +logging.basicConfig(level=logging.INFO, format=FORMAT) +logger = logging.getLogger(__name__) + + +def main(): + role = role_maker.PaddleCloudRoleMaker(is_collective=True) # new line 3 + fleet.init(role) # new line 4 + env = os.environ + + num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 0)) + assert num_trainers != 0, "multi-machine training process must be started using distributed.launch..." + trainer_id = int(env.get("PADDLE_TRAINER_ID", 0)) + + # set different seeds for different trainers + random.seed(trainer_id) + np.random.seed(trainer_id) + + if FLAGS.enable_ce: + random.seed(0) + np.random.seed(0) + + cfg = load_config(FLAGS.config) + merge_config(FLAGS.opt) + check_config(cfg) + # check if set use_gpu=True in paddlepaddle cpu version + check_gpu(cfg.use_gpu) + # check if paddlepaddle version is satisfied + check_version() + + save_only = getattr(cfg, 'save_prediction_only', False) + if save_only: + raise NotImplementedError('The config file only support prediction,' + ' training stage is not implemented now') + main_arch = cfg.architecture + + assert cfg.use_gpu == True, "GPU must be supported for multi-machine training..." + devices_num = fluid.core.get_cuda_device_count() + + if 'FLAGS_selected_gpus' in env: + device_id = int(env['FLAGS_selected_gpus']) + else: + device_id = 0 + place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace() + exe = fluid.Executor(place) + + lr_builder = create('LearningRate') + optim_builder = create('OptimizerBuilder') + + # build program + startup_prog = fluid.Program() + train_prog = fluid.Program() + if FLAGS.enable_ce: + startup_prog.random_seed = 1000 + train_prog.random_seed = 1000 + with fluid.program_guard(train_prog, startup_prog): + with fluid.unique_name.guard(): + model = create(main_arch) + if FLAGS.fp16: + assert (getattr(model.backbone, 'norm_type', None) + != 'affine_channel'), \ + '--fp16 currently does not support affine channel, ' \ + ' please modify backbone settings to use batch norm' + + with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx: + inputs_def = cfg['TrainReader']['inputs_def'] + feed_vars, train_loader = model.build_inputs(**inputs_def) + train_fetches = model.train(feed_vars) + loss = train_fetches['loss'] + if FLAGS.fp16: + loss *= ctx.get_loss_scale_var() + lr = lr_builder() + optimizer = optim_builder(lr) + + dist_strategy = DistributedStrategy() + sync_bn = getattr(model.backbone, 'norm_type', + None) == 'sync_bn' + dist_strategy.sync_batch_norm = sync_bn + dist_strategy.nccl_comm_num = 1 + exec_strategy = fluid.ExecutionStrategy() + exec_strategy.num_threads = 3 + exec_strategy.num_iteration_per_drop_scope = 30 + dist_strategy.exec_strategy = exec_strategy + dist_strategy.fuse_all_reduce_ops = True + optimizer = fleet.distributed_optimizer( + optimizer, strategy=dist_strategy) # new line 5 + + optimizer.minimize(loss) + + if FLAGS.fp16: + loss /= ctx.get_loss_scale_var() + + if 'use_ema' in cfg and cfg['use_ema']: + global_steps = _decay_step_counter() + ema = ExponentialMovingAverage( + cfg['ema_decay'], thres_steps=global_steps) + ema.update() + + # parse train fetches + train_keys, train_values, _ = parse_fetches(train_fetches) + train_values.append(lr) + + if FLAGS.eval: + eval_prog = fluid.Program() + with fluid.program_guard(eval_prog, startup_prog): + with fluid.unique_name.guard(): + model = create(main_arch) + inputs_def = cfg['EvalReader']['inputs_def'] + feed_vars, eval_loader = model.build_inputs(**inputs_def) + fetches = model.eval(feed_vars) + eval_prog = eval_prog.clone(True) + + eval_reader = create_reader(cfg.EvalReader, devices_num=1) + eval_loader.set_sample_list_generator(eval_reader, place) + + # parse eval fetches + extra_keys = [] + if cfg.metric == 'COCO': + extra_keys = ['im_info', 'im_id', 'im_shape'] + if cfg.metric == 'VOC': + extra_keys = ['gt_bbox', 'gt_class', 'is_difficult'] + if cfg.metric == 'WIDERFACE': + extra_keys = ['im_id', 'im_shape', 'gt_bbox'] + eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog, + extra_keys) + + exe.run(startup_prog) + compiled_train_prog = fleet.main_program + + if FLAGS.eval: + compiled_eval_prog = fluid.CompiledProgram(eval_prog) + + fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel' + + ignore_params = cfg.finetune_exclude_pretrained_params \ + if 'finetune_exclude_pretrained_params' in cfg else [] + + start_iter = 0 + if FLAGS.resume_checkpoint: + checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint) + start_iter = checkpoint.global_step() + elif cfg.pretrain_weights and fuse_bn and not ignore_params: + checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights) + elif cfg.pretrain_weights: + checkpoint.load_params( + exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params) + + train_reader = create_reader( + cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num, + cfg, + devices_num=devices_num) + train_loader.set_sample_list_generator(train_reader, place) + + # whether output bbox is normalized in model output layer + is_bbox_normalized = False + if hasattr(model, 'is_bbox_normalized') and \ + callable(model.is_bbox_normalized): + is_bbox_normalized = model.is_bbox_normalized() + + # if map_type not set, use default 11point, only use in VOC eval + map_type = cfg.map_type if 'map_type' in cfg else '11point' + + train_stats = TrainingStats(cfg.log_smooth_window, train_keys) + train_loader.start() + start_time = time.time() + end_time = time.time() + + cfg_name = os.path.basename(FLAGS.config).split('.')[0] + save_dir = os.path.join(cfg.save_dir, cfg_name) + time_stat = deque(maxlen=cfg.log_smooth_window) + best_box_ap_list = [0.0, 0] #[map, iter] + + # use VisualDL to log data + if FLAGS.use_vdl: + assert six.PY3, "VisualDL requires Python >= 3.5" + from visualdl import LogWriter + vdl_writer = LogWriter(FLAGS.vdl_log_dir) + vdl_loss_step = 0 + vdl_mAP_step = 0 + + for it in range(start_iter, cfg.max_iters): + start_time = end_time + end_time = time.time() + time_stat.append(end_time - start_time) + time_cost = np.mean(time_stat) + eta_sec = (cfg.max_iters - it) * time_cost + eta = str(datetime.timedelta(seconds=int(eta_sec))) + outs = exe.run(compiled_train_prog, fetch_list=train_values) + stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])} + + # use vdl-paddle to log loss + if FLAGS.use_vdl: + if it % cfg.log_iter == 0: + for loss_name, loss_value in stats.items(): + vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step) + vdl_loss_step += 1 + + train_stats.update(stats) + logs = train_stats.log() + if it % cfg.log_iter == 0 and trainer_id == 0: + strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format( + it, np.mean(outs[-1]), logs, time_cost, eta) + logger.info(strs) + + # NOTE : profiler tools, used for benchmark + if FLAGS.is_profiler and it == 5: + profiler.start_profiler("All") + elif FLAGS.is_profiler and it == 10: + profiler.stop_profiler("total", FLAGS.profiler_path) + return + + + if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \ + and trainer_id == 0: + save_name = str(it) if it != cfg.max_iters - 1 else "model_final" + if 'use_ema' in cfg and cfg['use_ema']: + exe.run(ema.apply_program) + checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name)) + + if FLAGS.eval: + # evaluation + resolution = None + if 'Mask' in cfg.architecture: + resolution = model.mask_head.resolution + results = eval_run( + exe, + compiled_eval_prog, + eval_loader, + eval_keys, + eval_values, + eval_cls, + cfg, + resolution=resolution) + box_ap_stats = eval_results( + results, cfg.metric, cfg.num_classes, resolution, + is_bbox_normalized, FLAGS.output_eval, map_type, + cfg['EvalReader']['dataset']) + + # use vdl_paddle to log mAP + if FLAGS.use_vdl: + vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step) + vdl_mAP_step += 1 + + if box_ap_stats[0] > best_box_ap_list[0]: + best_box_ap_list[0] = box_ap_stats[0] + best_box_ap_list[1] = it + checkpoint.save(exe, train_prog, + os.path.join(save_dir, "best_model")) + logger.info("Best test box ap: {}, in iter: {}".format( + best_box_ap_list[0], best_box_ap_list[1])) + + if 'use_ema' in cfg and cfg['use_ema']: + exe.run(ema.restore_program) + + train_loader.reset() + + +if __name__ == '__main__': + parser = ArgsParser() + parser.add_argument( + "-r", + "--resume_checkpoint", + default=None, + type=str, + help="Checkpoint path for resuming training.") + parser.add_argument( + "--fp16", + action='store_true', + default=False, + help="Enable mixed precision training.") + parser.add_argument( + "--loss_scale", + default=8., + type=float, + help="Mixed precision training loss scale.") + parser.add_argument( + "--eval", + action='store_true', + default=False, + help="Whether to perform evaluation in train") + parser.add_argument( + "--output_eval", + default=None, + type=str, + help="Evaluation directory, default is current directory.") + parser.add_argument( + "--use_vdl", + type=bool, + default=False, + help="whether to record the data to VisualDL.") + parser.add_argument( + '--vdl_log_dir', + type=str, + default="vdl_log_dir/scalar", + help='VisualDL logging directory for scalar.') + parser.add_argument( + "--enable_ce", + type=bool, + default=False, + help="If set True, enable continuous evaluation job." + "This flag is only used for internal test.") + + #NOTE:args for profiler tools, used for benchmark + parser.add_argument( + '--is_profiler', + type=int, + default=0, + help='The switch of profiler tools. (used for benchmark)') + parser.add_argument( + '--profiler_path', + type=str, + default="./detection.profiler", + help='The profiler output file path. (used for benchmark)') + FLAGS = parser.parse_args() + main()