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train5.py

@@ -2,6 +2,7 @@ import pdb
 from transformers import AutoTokenizer
 from torch import nn
 import os
+import time
 import torch.distributed as dist
 from tqdm import tqdm
 from torch.utils.data import DataLoader
@@ -144,7 +145,7 @@ def fsdp_main(args):
     rank = int(os.environ['RANK'])
     world_size = int(os.environ['WORLD_SIZE'])
     if args.use_wandb and rank == 0:
-        wandb.init(entity="SemiNAT", project="SemiNAT-SFT", name=args.run_name)
+        wandb.init(entity="SemiNAT", project="SemiNAT-Debug", name=args.run_name)
 
     local_rank = int(os.environ['LOCAL_RANK'])
     DEVICE = f"cuda:{local_rank}"
@@ -250,7 +251,7 @@ def fsdp_main(args):
 
         if train_sampler:
             train_sampler.set_epoch(epoch)
-
+            
         if rank == 0:
             inner_pbar = tqdm(range(len(train_dataloader)),
                               colour="blue",
@@ -278,8 +279,9 @@ def fsdp_main(args):
 
             loss.backward()
             optimizer.step()
-            mem = torch.cuda.memory_allocated() / (1024 ** 2)
-            memories.append(mem)
+            scheduler.step()
+            # mem = torch.cuda.memory_allocated() / (1024 ** 2)
+            # memories.append(mem)
             global_step += 1
 
             if global_step % args.save_steps == 0:
@@ -327,16 +329,16 @@ def fsdp_main(args):
                     })
 
 
-        avg_mem = sum(memories) / len(memories)
-        print(f"Average memory usage over {len(memories)} steps: {avg_mem:.2f} MB")
+        # avg_mem = sum(memories) / len(memories)
+        # print(f"Average memory usage over {len(memories)} steps: {avg_mem:.2f} MB")
 
 
-        dist.all_reduce(loss, op=dist.ReduceOp.SUM)
+        # dist.all_reduce(loss, op=dist.ReduceOp.SUM)
 
         if rank == 0:
             inner_pbar.close()
 
-        scheduler.step()
+        
 
 
     end_time = time.time()