From ceebbd01f4672d7ba2c7272ba3e4d61d7d17678d Mon Sep 17 00:00:00 2001 From: VectorSL Date: Mon, 25 May 2020 21:18:07 +0800 Subject: [PATCH] gpu update example resnet --- example/resnet50_cifar10/README.md | 12 +++++++ example/resnet50_cifar10/dataset.py | 12 +++++-- example/resnet50_cifar10/eval.py | 28 +++++++++------ example/resnet50_cifar10/train.py | 43 +++++++++++++++--------- example/resnet50_imagenet2012/README.md | 15 +++++++++ example/resnet50_imagenet2012/dataset.py | 13 ++++--- example/resnet50_imagenet2012/eval.py | 14 ++++---- example/resnet50_imagenet2012/train.py | 41 +++++++++++++--------- 8 files changed, 122 insertions(+), 56 deletions(-) diff --git a/example/resnet50_cifar10/README.md b/example/resnet50_cifar10/README.md index bbe7688fbf..abb0ba4090 100644 --- a/example/resnet50_cifar10/README.md +++ b/example/resnet50_cifar10/README.md @@ -123,3 +123,15 @@ Inference result will be stored in the example path, whose folder name is "infer ``` result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt ``` + +### Running on GPU +``` +# distributed training example +mpirun -n 8 python train.py --dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True + +# standalone training example +python train.py --dataset_path=~/cifar-10-batches-bin --device_target="GPU" + +# infer example +python eval.py --dataset_path=~/cifar10-10-verify-bin --device_target="GPU" --checkpoint_path=resnet-90_195.ckpt +``` \ No newline at end of file diff --git a/example/resnet50_cifar10/dataset.py b/example/resnet50_cifar10/dataset.py index 0a1f6eb3fe..1d7074d733 100755 --- a/example/resnet50_cifar10/dataset.py +++ b/example/resnet50_cifar10/dataset.py @@ -20,10 +20,11 @@ import mindspore.common.dtype as mstype import mindspore.dataset.engine as de import mindspore.dataset.transforms.vision.c_transforms as C import mindspore.dataset.transforms.c_transforms as C2 +from mindspore.communication.management import get_rank, get_group_size from config import config -def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32): +def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"): """ create a train or eval dataset @@ -32,12 +33,17 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32): do_train(bool): whether dataset is used for train or eval. repeat_num(int): the repeat times of dataset. Default: 1 batch_size(int): the batch size of dataset. Default: 32 + target(str): the device target. Default: Ascend Returns: dataset """ - device_num = int(os.getenv("DEVICE_NUM")) - rank_id = int(os.getenv("RANK_ID")) + if target == "Ascend": + device_num = int(os.getenv("DEVICE_NUM")) + rank_id = int(os.getenv("RANK_ID")) + else: + rank_id = get_rank() + device_num = get_group_size() if device_num == 1: ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True) diff --git a/example/resnet50_cifar10/eval.py b/example/resnet50_cifar10/eval.py index e6f02360d8..f7d71c8d29 100755 --- a/example/resnet50_cifar10/eval.py +++ b/example/resnet50_cifar10/eval.py @@ -25,7 +25,7 @@ from mindspore.parallel._auto_parallel_context import auto_parallel_context from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits from mindspore.train.model import Model, ParallelMode from mindspore.train.serialization import load_checkpoint, load_param_into_net -from mindspore.communication.management import init +from mindspore.communication.management import init, get_group_size parser = argparse.ArgumentParser(description='Image classification') parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute') @@ -34,26 +34,32 @@ parser.add_argument('--do_train', type=bool, default=False, help='Do train or no parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.') parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') +parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') args_opt = parser.parse_args() -device_id = int(os.getenv('DEVICE_ID')) - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) -context.set_context(device_id=device_id) - if __name__ == '__main__': + target = args_opt.device_target + context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False) if not args_opt.do_eval and args_opt.run_distribute: - context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True) - auto_parallel_context().set_all_reduce_fusion_split_indices([140]) - init() + if target == "Ascend": + device_id = int(os.getenv('DEVICE_ID')) + context.set_context(device_id=device_id) + context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) + auto_parallel_context().set_all_reduce_fusion_split_indices([140]) + init() + elif target == "GPU": + init("nccl") + context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) epoch_size = config.epoch_size net = resnet50(class_num=config.class_num) loss = SoftmaxCrossEntropyWithLogits(sparse=True) if args_opt.do_eval: - dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size) + dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size, + target=target) step_size = dataset.get_dataset_size() if args_opt.checkpoint_path: diff --git a/example/resnet50_cifar10/train.py b/example/resnet50_cifar10/train.py index b37c794822..7ef4241235 100755 --- a/example/resnet50_cifar10/train.py +++ b/example/resnet50_cifar10/train.py @@ -29,7 +29,7 @@ from mindspore.train.model import Model, ParallelMode from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train.loss_scale_manager import FixedLossScaleManager -from mindspore.communication.management import init +from mindspore.communication.management import init, get_rank, get_group_size parser = argparse.ArgumentParser(description='Image classification') parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute') @@ -37,28 +37,37 @@ parser.add_argument('--device_num', type=int, default=1, help='Device num.') parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.') parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') +parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') args_opt = parser.parse_args() -device_id = int(os.getenv('DEVICE_ID')) - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id, - enable_auto_mixed_precision=True) if __name__ == '__main__': + target = args_opt.device_target if not args_opt.do_eval and args_opt.run_distribute: - context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True) - auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160]) - init() + if target == "Ascend": + device_id = int(os.getenv('DEVICE_ID')) + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id, + enable_auto_mixed_precision=True) + init() + context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) + auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160]) + ckpt_save_dir = config.save_checkpoint_path + loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') + elif target == "GPU": + context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False) + init("nccl") + context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) + ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/" + loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean') epoch_size = config.epoch_size net = resnet50(class_num=config.class_num) - loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') - if args_opt.do_train: dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, - repeat_num=epoch_size, batch_size=config.batch_size) + repeat_num=epoch_size, batch_size=config.batch_size, target=target) step_size = dataset.get_dataset_size() loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) @@ -67,9 +76,11 @@ if __name__ == '__main__': lr_decay_mode='poly')) opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) - - model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2", - keep_batchnorm_fp32=False) + if target == 'GPU': + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) + else: + model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, + amp_level="O2", keep_batchnorm_fp32=True) time_cb = TimeMonitor(data_size=step_size) loss_cb = LossMonitor() @@ -77,6 +88,6 @@ if __name__ == '__main__': if config.save_checkpoint: config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps, keep_checkpoint_max=config.keep_checkpoint_max) - ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck) + ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck) cb += [ckpt_cb] model.train(epoch_size, dataset, callbacks=cb) diff --git a/example/resnet50_imagenet2012/README.md b/example/resnet50_imagenet2012/README.md index 05b39daaca..6baf863544 100644 --- a/example/resnet50_imagenet2012/README.md +++ b/example/resnet50_imagenet2012/README.md @@ -133,3 +133,18 @@ Inference result will be stored in the example path, whose folder name is "infer ``` result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt ``` + +### Running on GPU +``` +# distributed training example +mpirun -n 8 python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --run_distribute=True + +# standalone training example +python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" + +# standalone training example with pretrained checkpoint +python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --pre_trained=pretrained.ckpt + +# infer example +python eval.py --dataset_path=dataset/ilsvrc/val --device_target="GPU" --checkpoint_path=resnet-90_5004ss.ckpt +``` \ No newline at end of file diff --git a/example/resnet50_imagenet2012/dataset.py b/example/resnet50_imagenet2012/dataset.py index 6f4b1a11b5..400a4dc4fa 100755 --- a/example/resnet50_imagenet2012/dataset.py +++ b/example/resnet50_imagenet2012/dataset.py @@ -20,9 +20,9 @@ import mindspore.common.dtype as mstype import mindspore.dataset.engine as de import mindspore.dataset.transforms.vision.c_transforms as C import mindspore.dataset.transforms.c_transforms as C2 +from mindspore.communication.management import get_rank, get_group_size - -def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32): +def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"): """ create a train or eval dataset @@ -31,12 +31,17 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32): do_train(bool): whether dataset is used for train or eval. repeat_num(int): the repeat times of dataset. Default: 1 batch_size(int): the batch size of dataset. Default: 32 + target(str): the device target. Default: Ascend Returns: dataset """ - device_num = int(os.getenv("DEVICE_NUM")) - rank_id = int(os.getenv("RANK_ID")) + if target == "Ascend": + device_num = int(os.getenv("DEVICE_NUM")) + rank_id = int(os.getenv("RANK_ID")) + else: + rank_id = get_rank() + device_num = get_group_size() if device_num == 1: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True) diff --git a/example/resnet50_imagenet2012/eval.py b/example/resnet50_imagenet2012/eval.py index a19807ee9c..3f7961e786 100755 --- a/example/resnet50_imagenet2012/eval.py +++ b/example/resnet50_imagenet2012/eval.py @@ -32,12 +32,13 @@ parser.add_argument('--do_train', type=bool, default=False, help='Do train or no parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.') parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') +parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') args_opt = parser.parse_args() - -device_id = int(os.getenv('DEVICE_ID')) - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) -context.set_context(device_id=device_id) +target = args_opt.device_target +context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False) +if target == "Ascend": + device_id = int(os.getenv('DEVICE_ID')) + context.set_context(device_id=device_id) if __name__ == '__main__': @@ -47,7 +48,8 @@ if __name__ == '__main__': loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num) if args_opt.do_eval: - dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size) + dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size, + target=target) step_size = dataset.get_dataset_size() if args_opt.checkpoint_path: diff --git a/example/resnet50_imagenet2012/train.py b/example/resnet50_imagenet2012/train.py index 42f19b4d64..6301480429 100755 --- a/example/resnet50_imagenet2012/train.py +++ b/example/resnet50_imagenet2012/train.py @@ -29,7 +29,7 @@ from mindspore.train.model import Model, ParallelMode from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.serialization import load_checkpoint, load_param_into_net -from mindspore.communication.management import init +from mindspore.communication.management import init, get_rank, get_group_size import mindspore.nn as nn import mindspore.common.initializer as weight_init from crossentropy import CrossEntropy @@ -40,21 +40,28 @@ parser.add_argument('--device_num', type=int, default=1, help='Device num.') parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.') parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') +parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path') args_opt = parser.parse_args() -device_id = int(os.getenv('DEVICE_ID')) - - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id, - enable_auto_mixed_precision=True) - if __name__ == '__main__': + target = args_opt.device_target if not args_opt.do_eval and args_opt.run_distribute: - context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True, parameter_broadcast=True) - auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160]) - init() + if target == "Ascend": + device_id = int(os.getenv('DEVICE_ID')) + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id, + enable_auto_mixed_precision=True) + init() + context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) + auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160]) + ckpt_save_dir = config.save_checkpoint_path + elif target == "GPU": + context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False) + init("nccl") + context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) + ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/" epoch_size = config.epoch_size net = resnet50(class_num=config.class_num) @@ -81,7 +88,7 @@ if __name__ == '__main__': if args_opt.do_train: dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, - repeat_num=epoch_size, batch_size=config.batch_size) + repeat_num=epoch_size, batch_size=config.batch_size, target=target) step_size = dataset.get_dataset_size() loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) @@ -93,9 +100,11 @@ if __name__ == '__main__': opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) - - model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2", - keep_batchnorm_fp32=False) + if target == "Ascend": + model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, + amp_level="O2", keep_batchnorm_fp32=False) + elif target == "GPU": + model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}) time_cb = TimeMonitor(data_size=step_size) @@ -104,6 +113,6 @@ if __name__ == '__main__': if config.save_checkpoint: config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size, keep_checkpoint_max=config.keep_checkpoint_max) - ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck) + ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck) cb += [ckpt_cb] model.train(epoch_size, dataset, callbacks=cb)