Implement DDP static_graph=True (#6940)

* Implement DDP `static_graph=True`

Experimental implementation of new PyTorch 1.11.0 DDP feature.

* Add 1.11.0 check

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

for more information, see https://pre-commit.ci

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

for more information, see https://pre-commit.ci

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

Author: glenn-jocher

train.py

@@ -47,9 +47,9 @@
 from utils.datasets import create_dataloader
 from utils.downloads import attempt_download
 from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
-                           check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,
-                           intersect_dicts, is_ascii, labels_to_class_weights, labels_to_image_weights, methods,
-                           one_cycle, print_args, print_mutation, strip_optimizer)
+                           check_suffix, check_version, check_yaml, colorstr, get_latest_run, increment_path,
+                           init_seeds, intersect_dicts, is_ascii, labels_to_class_weights, labels_to_image_weights,
+                           methods, 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
@@ -269,7 +269,10 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # DDP mode
     if cuda and RANK != -1:
-        model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
+        if check_version(torch.__version__, '1.11.0'):
+            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)