assert torch!=1.12.0 for DDP training (#8621)

* assert torch!=1.12.0 for DDP training

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

requirements.txt

@@ -9,8 +9,8 @@ Pillow>=7.1.2
 PyYAML>=5.3.1
 requests>=2.23.0
 scipy>=1.4.1
-torch>=1.7.0,!=1.12.0  # https://github.com/ultralytics/yolov5/issues/8395
+torch>=1.7.0
-torchvision>=0.8.1,!=0.13.0  # https://github.com/ultralytics/yolov5/issues/8395
+torchvision>=0.8.1
 tqdm>=4.64.0
 protobuf<4.21.3  # https://github.com/ultralytics/yolov5/issues/8012
 

train.py

@@ -27,7 +27,6 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 import yaml
-from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import lr_scheduler
 from tqdm import tqdm
 
@@ -46,15 +45,15 @@ from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
 from utils.downloads import attempt_download
 from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
-                           check_requirements, check_suffix, check_version, check_yaml, colorstr, get_latest_run,
+                           check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
-                           increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
+                           init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
-                           labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer)
+                           one_cycle, print_args, print_mutation, strip_optimizer)
 from utils.loggers import Loggers
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
 from utils.plots import plot_evolve, plot_labels
-from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_optimizer,
+from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
                                torch_distributed_zero_first)
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -248,10 +247,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # DDP mode
     if cuda and RANK != -1:
-        if check_version(torch.__version__, '1.11.0'):
+        model = smart_DDP(model)
-            model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
-        else:
-            model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
 
     # Model attributes
     nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)

utils/torch_utils.py

@@ -17,8 +17,13 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
 
-from utils.general import LOGGER, colorstr, file_date, git_describe
+from utils.general import LOGGER, check_version, colorstr, file_date, git_describe
+
+LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
+WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
 
 try:
     import thop  # for FLOPs computation
@@ -29,6 +34,17 @@ except ImportError:
 warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
 
 
+def smart_DDP(model):
+    # Model DDP creation with checks
+    assert not check_version(torch.__version__, '1.12.0', pinned=True), \
+        'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
+        'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
+    if check_version(torch.__version__, '1.11.0'):
+        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
+    else:
+        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
+
+
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
     # Decorator to make all processes in distributed training wait for each local_master to do something