From 507f821d7ab0a51a4f01b8557fe38e0bcf0d14f6 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 26 三月 2024 16:45:10 +0800
Subject: [PATCH] update
---
funasr/train_utils/trainer.py | 52 ++++++++++++++++++++++++++--------------------------
1 files changed, 26 insertions(+), 26 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index cf23483..56ec604 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -190,7 +190,7 @@
if self.resume:
ckpt = os.path.join(self.output_dir, "model.pt")
if os.path.isfile(ckpt):
- checkpoint = torch.load(ckpt)
+ checkpoint = torch.load(ckpt, map_location="cpu")
self.start_epoch = checkpoint['epoch'] + 1
# self.model.load_state_dict(checkpoint['state_dict'])
src_state = checkpoint['state_dict']
@@ -215,7 +215,7 @@
self.val_acc_list = checkpoint["acc"]
self.step_or_epoch = checkpoint["step_or_epoch"]
-
+ model.to(self.device)
print(f"Checkpoint loaded successfully from '{ckpt}'")
else:
print(f"No checkpoint found at '{ckpt}', does not resume status!")
@@ -250,14 +250,14 @@
optim.zero_grad()
speed_stats = {}
time5 = time.perf_counter()
- # iterator_stop = torch.tensor(0).to(self.device)
+ iterator_stop = torch.tensor(0).to(self.device)
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
+ 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}"
@@ -340,7 +340,7 @@
speed_stats["total_time"] = total_time
lr = scheduler.get_last_lr()[0]
- batch_num_epoch = -1
+ batch_num_epoch = 1
if hasattr(dataloader_train, "__len__"):
batch_num_epoch = len(dataloader_train)
self.log(epoch, batch_idx,
@@ -364,13 +364,14 @@
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)
+ 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()
+ iterator_stop = torch.tensor(0).to(self.device)
@@ -397,15 +398,13 @@
speed_stats = {}
time5 = time.perf_counter()
- # iterator_stop = torch.tensor(0).to(self.device)
-
+ iterator_stop = torch.tensor(0).to(self.device)
+ dataloader_val.batch_sampler.set_epoch(epoch)
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
+ if self.use_ddp or self.use_fsdp:
+ dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+ if iterator_stop > 0:
+ break
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1 - time5:0.3f}"
batch = to_device(batch, self.device)
@@ -441,8 +440,8 @@
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
+ time5 = time.perf_counter()
+ batch_num_epoch = 1
if hasattr(dataloader_val, "__len__"):
batch_num_epoch = len(dataloader_val)
self.log(epoch, batch_idx,
@@ -455,16 +454,17 @@
tag="val",
)
- # else:
- # if self.use_ddp or self.use_fsdp:
- # iterator_stop.fill_(1)
- # dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+ 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()
+ iterator_stop = torch.tensor(0).to(self.device)
def log(self,
--
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