Resolved torchrun Bug: Fixed issue #2163

Updated torch.distributed.launch to torchrun.

recognition/arcface_torch/README.md

@@ -22,27 +22,27 @@ To train a model, execute the `train.py` script with the path to the configurati
 ### 1. To run on a machine with 8 GPUs:
 
 ```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py configs/ms1mv3_r50_lr02
+torchrun --nproc_per_node=8 train.py configs/ms1mv3_r50
 ```
 
 ### 2. To run on 2 machines with 8 GPUs each:
 
 Node 0:
 
 ```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train.py configs/webface42m_r100_lr01_pfc02_bs4k_16gpus
+torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train.py configs/wf42m_pfc02_16gpus_r100
 ```
 
 Node 1:
 
 ```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train.py configs/webface42m_r100_lr01_pfc02_bs4k_16gpus
+torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train.py configs/wf42m_pfc02_16gpus_r100
 ```
 
 ### 3. Run ViT-B on a machine with 24k batchsize:
 
 ```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12345 train_v2.py configs/wf42m_pfc03_40epoch_8gpu_vit_b.py
+torchrun --nproc_per_node=8 train_v2.py configs/wf42m_pfc03_40epoch_8gpu_vit_b
 ```
 
 

recognition/arcface_torch/run.sh

@@ -1,8 +1 @@
-
-CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 python -m torch.distributed.launch \
---nproc_per_node=7 \
---nnodes=1 \
---node_rank=0 \
---master_addr="127.0.0.1" \
---master_port=12345 train.py $@
-
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 train_v2.py $@

recognition/arcface_torch/train.py

@@ -22,12 +22,14 @@
 we have upgraded the torch to 1.9.0. torch before than 1.9.0 may not work in the future."
 
 try:
-    world_size = int(os.environ["WORLD_SIZE"])
     rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
     distributed.init_process_group("nccl")
 except KeyError:
-    world_size = 1
     rank = 0
+    local_rank = 0
+    world_size = 1
     distributed.init_process_group(
         backend="nccl",
         init_method="tcp://127.0.0.1:12584",
@@ -43,7 +45,7 @@ def main(args):
     # global control random seed
     setup_seed(seed=cfg.seed, cuda_deterministic=False)
 
-    torch.cuda.set_device(args.local_rank)
+    torch.cuda.set_device(local_rank)
 
     os.makedirs(cfg.output, exist_ok=True)
     init_logging(rank, cfg.output)
@@ -82,7 +84,7 @@ def main(args):
 
     train_loader = get_dataloader(
         cfg.rec,
-        args.local_rank,
+        local_rank,
         cfg.batch_size,
         cfg.dali,
         cfg.seed,
@@ -93,7 +95,7 @@ def main(args):
         cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).cuda()
 
     backbone = torch.nn.parallel.DistributedDataParallel(
-        module=backbone, broadcast_buffers=False, device_ids=[args.local_rank], bucket_cap_mb=16,
+        module=backbone, broadcast_buffers=False, device_ids=[local_rank], bucket_cap_mb=16,
         find_unused_parameters=True)
 
     backbone.train()
@@ -255,5 +257,4 @@ def main(args):
     parser = argparse.ArgumentParser(
         description="Distributed Arcface Training in Pytorch")
     parser.add_argument("config", type=str, help="py config file")
-    parser.add_argument("--local_rank", type=int, default=0, help="local_rank")
     main(parser.parse_args())

recognition/arcface_torch/train_v2.py

@@ -22,12 +22,14 @@
 we have upgraded the torch to 1.9.0. torch before than 1.9.0 may not work in the future."
 
 try:
-    world_size = int(os.environ["WORLD_SIZE"])
     rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
     distributed.init_process_group("nccl")
 except KeyError:
-    world_size = 1
     rank = 0
+    local_rank = 0
+    world_size = 1
     distributed.init_process_group(
         backend="nccl",
         init_method="tcp://127.0.0.1:12584",
@@ -43,7 +45,7 @@ def main(args):
     # global control random seed
     setup_seed(seed=cfg.seed, cuda_deterministic=False)
 
-    torch.cuda.set_device(args.local_rank)
+    torch.cuda.set_device(local_rank)
 
     os.makedirs(cfg.output, exist_ok=True)
     init_logging(rank, cfg.output)
@@ -55,7 +57,7 @@ def main(args):
     )
 
     wandb_logger = None
-    if using_wandb:
+    if cfg.using_wandb:
         import wandb
         # Sign in to wandb
         try:
@@ -78,11 +80,11 @@ def main(args):
                 wandb_logger.config.update(cfg)
         except Exception as e:
             print("WandB Data (Entity and Project name) must be provided in config file (base.py).")
-            rint(f"Config Error: {e}")
+            print(f"Config Error: {e}")
 
     train_loader = get_dataloader(
         cfg.rec,
-        args.local_rank,
+        local_rank,
         cfg.batch_size,
         cfg.dali,
         cfg.seed,
@@ -93,7 +95,7 @@ def main(args):
         cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).cuda()
 
     backbone = torch.nn.parallel.DistributedDataParallel(
-        module=backbone, broadcast_buffers=False, device_ids=[args.local_rank], bucket_cap_mb=16,
+        module=backbone, broadcast_buffers=False, device_ids=[local_rank], bucket_cap_mb=16,
         find_unused_parameters=True)
 
     backbone.train()
@@ -253,5 +255,4 @@ def main(args):
     parser = argparse.ArgumentParser(
         description="Distributed Arcface Training in Pytorch")
     parser.add_argument("config", type=str, help="py config file")
-    parser.add_argument("--local_rank", type=int, default=0, help="local_rank")
     main(parser.parse_args())