From 9ec8f8519ec22cc81b35bab56a01a43d8b4984e3 Mon Sep 17 00:00:00 2001
From: lujianwei <826254726@qq.com>
Date: Thu, 19 May 2022 14:36:48 +0800
Subject: [PATCH] fix the deadlock problem when using distributed training in
 VQA fintuning

---
 oscar/run_vqa.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/oscar/run_vqa.py b/oscar/run_vqa.py
index 123ca48..a1fc155 100644
--- a/oscar/run_vqa.py
+++ b/oscar/run_vqa.py
@@ -590,7 +590,7 @@ def train(args, train_dataset, eval_dataset, model, tokenizer):
                 model.zero_grad()
                 global_step += 1
 
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:# Log metrics
+                if args.logging_steps > 0 and global_step % args.logging_steps == 0:# Log metrics
                     if args.local_rank not in [-1, 0]:
                         torch.distributed.barrier()