From 5d74e107fc5696b70e75003c278f8babd17161e8 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 24 三月 2024 00:58:56 +0800
Subject: [PATCH] update
---
funasr/train_utils/trainer.py | 65 +++++++++++++++++++++++---------
1 files changed, 47 insertions(+), 18 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index c443c6f..23c18d9 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -198,6 +198,8 @@
for k in dst_state.keys():
if not k.startswith("module.") and "module."+k in src_state.keys():
k_ddp = "module."+k
+ elif k.startswith("module.") and "module."+k not in src_state.keys():
+ k_ddp = k.replace("module.", "", 1)
else:
k_ddp = k
if k_ddp in src_state.keys():
@@ -246,8 +248,15 @@
optim.zero_grad()
speed_stats = {}
time5 = time.perf_counter()
-
+ iterator_stop = torch.tensor(0).to(self.device)
+ dist.barrier()
+ print(f"before iter, iterator_stop: {iterator_stop}\n")
+ dataloader_train.batch_sampler.set_epoch(epoch)
for batch_idx, batch in enumerate(dataloader_train):
+ if self.use_ddp or self.use_fsdp:
+ dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+ if iterator_stop > 0:
+ break
self.batch_total += 1
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1-time5:0.3f}"
@@ -288,13 +297,13 @@
self.train_loss_avg = (self.train_loss_avg*batch_idx + loss.detach().cpu().item())/(batch_idx+1)
if "acc" in stats:
self.train_acc_avg = (self.train_acc_avg * batch_idx + stats["acc"].detach().cpu().item()) / (batch_idx + 1)
- if self.use_ddp or self.use_fsdp:
- train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(self.device)
- train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(self.device)
- dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
- dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
- self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
- self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
+ # if self.use_ddp or self.use_fsdp:
+ # train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(self.device)
+ # train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(self.device)
+ # dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
+ # dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
+ # self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
+ # self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
# Perform an optimizer step only after accumulating enough gradients
@@ -354,7 +363,11 @@
if (batch_idx+1) % self.save_checkpoint_interval == 0:
self.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler, step=batch_idx+1)
-
+ else:
+ if self.use_ddp or self.use_fsdp:
+ iterator_stop.fill_(1)
+ dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+
if self.use_ddp or self.use_fsdp:
dist.barrier()
@@ -381,7 +394,16 @@
speed_stats = {}
time5 = time.perf_counter()
+ iterator_stop = torch.tensor(0).to(self.device)
+ dist.barrier()
+ print(f"before iter, iterator_stop: {iterator_stop}\n")
for batch_idx, batch in enumerate(dataloader_val):
+ if self.use_ddp or self.use_fsdp:
+ dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+ if epoch >= 1:
+ print(f"iterator_stop: {iterator_stop}\n")
+ if iterator_stop > 0:
+ break
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1 - time5:0.3f}"
batch = to_device(batch, self.device)
@@ -395,7 +417,7 @@
# Apply weighted averaging for loss and stats
loss = (loss * weight.type(loss.dtype)).sum()
# if distributed, this method can also apply all_reduce()
- stats, weight = recursive_average(stats, weight, distributed=True)
+ # stats, weight = recursive_average(stats, weight, distributed=True)
if self.use_ddp or self.use_fsdp:
dist.all_reduce(weight, op=dist.ReduceOp.SUM)
# Now weight is summation over all workers
@@ -410,13 +432,13 @@
self.val_loss_avg = (self.val_loss_avg*batch_idx + loss.detach().cpu().item())/(batch_idx+1)
if "acc" in stats:
self.val_acc_avg = (self.val_acc_avg * batch_idx + stats["acc"].detach().cpu().item()) / (batch_idx + 1)
- if self.use_ddp or self.use_fsdp:
- val_loss_avg = torch.tensor(self.val_loss_avg, dtype=torch.float32).to(self.device)
- val_acc_avg = torch.tensor(self.val_acc_avg, dtype=torch.float32).to(self.device)
- dist.all_reduce(val_loss_avg, op=dist.ReduceOp.SUM)
- dist.all_reduce(val_acc_avg, op=dist.ReduceOp.SUM)
- self.val_loss_avg = val_loss_avg.detach().cpu().item() / self.world_size
- self.val_acc_avg = val_acc_avg.detach().cpu().item() / self.world_size
+ # if self.use_ddp or self.use_fsdp:
+ # val_loss_avg = torch.tensor(self.val_loss_avg, dtype=torch.float32).to(self.device)
+ # val_acc_avg = torch.tensor(self.val_acc_avg, dtype=torch.float32).to(self.device)
+ # dist.all_reduce(val_loss_avg, op=dist.ReduceOp.SUM)
+ # dist.all_reduce(val_acc_avg, op=dist.ReduceOp.SUM)
+ # self.val_loss_avg = val_loss_avg.detach().cpu().item() / self.world_size
+ # self.val_acc_avg = val_acc_avg.detach().cpu().item() / self.world_size
batch_num_epoch = -1
if hasattr(dataloader_val, "__len__"):
@@ -431,9 +453,16 @@
tag="val",
)
+ else:
+ if self.use_ddp or self.use_fsdp:
+ iterator_stop.fill_(1)
+ dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+
self.val_acc_list.append(self.val_acc_avg)
model.train()
-
+
+
+
if self.use_ddp or self.use_fsdp:
dist.barrier()
--
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