From 443bc09c11f3cf89ffc573aab2021f0c933aa5b3 Mon Sep 17 00:00:00 2001
From: kmn1024 <kienman@gmail.com>
Date: 星期三, 25 六月 2025 16:34:30 +0800
Subject: [PATCH] Bugfix: Only allow rank==0 to clean up old checkpoints (#2558)
---
funasr/train_utils/trainer_ds.py | 114 +++++++++++++++++++++++++++++----------------------------
1 files changed, 58 insertions(+), 56 deletions(-)
diff --git a/funasr/train_utils/trainer_ds.py b/funasr/train_utils/trainer_ds.py
index 8a0679c..a1430db 100644
--- a/funasr/train_utils/trainer_ds.py
+++ b/funasr/train_utils/trainer_ds.py
@@ -30,8 +30,9 @@
yield
else:
if dtype == torch.float16 or dtype == torch.bfloat16:
- with autocast(enabled=True, dtype=dtype):
- yield
+ yield
+ # with autocast(enabled=True, dtype=dtype):
+ # yield
else:
yield
@@ -121,8 +122,8 @@
self.saved_ckpts = {}
self.step_or_epoch = -1
self.best_step_or_epoch = ""
- self.val_acc_step_or_eoch = {}
- self.val_loss_step_or_eoch = {}
+ self.val_acc_step_or_epoch = {}
+ self.val_loss_step_or_epoch = {}
self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
self.start_data_split_i = 0
@@ -194,8 +195,8 @@
# "optimizer": optim.state_dict(),
# "scheduler": scheduler.state_dict(),
"saved_ckpts": self.saved_ckpts,
- "val_acc_step_or_eoch": self.val_acc_step_or_eoch,
- "val_loss_step_or_eoch": self.val_loss_step_or_eoch,
+ "val_acc_step_or_epoch": self.val_acc_step_or_epoch,
+ "val_loss_step_or_epoch": self.val_loss_step_or_epoch,
"best_step_or_epoch": self.best_step_or_epoch,
"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
"step": step,
@@ -233,8 +234,8 @@
if self.avg_keep_nbest_models_type == "acc":
if (
- self.val_acc_step_or_eoch[ckpt_name]
- >= self.val_acc_step_or_eoch[self.best_step_or_epoch]
+ self.val_acc_step_or_epoch[ckpt_name]
+ >= self.val_acc_step_or_epoch[self.best_step_or_epoch]
):
self.best_step_or_epoch = ckpt_name
best_ckpt = Path(os.path.join(self.output_dir, f"model.pt.best"))
@@ -244,16 +245,16 @@
save_dir=self.output_dir, tag=f"model.pt.best", client_state=state
)
logging.info(
- f"Update best acc: {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
+ f"Update best acc: {self.val_acc_step_or_epoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
)
else:
logging.info(
- f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
+ f"No improvement in acc: {self.val_acc_step_or_epoch[ckpt_name]:.4f} < {self.val_acc_step_or_epoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
elif self.avg_keep_nbest_models_type == "loss":
if (
- self.val_loss_step_or_eoch[ckpt_name]
- <= self.val_loss_step_or_eoch[self.best_step_or_epoch]
+ self.val_loss_step_or_epoch[ckpt_name]
+ <= self.val_loss_step_or_epoch[self.best_step_or_epoch]
):
self.best_step_or_epoch = ckpt_name
best_ckpt = Path(os.path.join(self.output_dir, f"model.pt.best"))
@@ -263,30 +264,31 @@
save_dir=self.output_dir, tag=f"model.pt.best", client_state=state
)
logging.info(
- f"Update best loss: {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
+ f"Update best loss: {self.val_loss_step_or_epoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
)
else:
logging.info(
- f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
+ f"No improvement in loss: {self.val_loss_step_or_epoch[ckpt_name]:.4f} > {self.val_loss_step_or_epoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
else:
print("Undo")
- self.saved_ckpts[ckpt_name] = getattr(
- self, f"val_{self.avg_keep_nbest_models_type}_step_or_eoch"
- )[ckpt_name]
- if self.keep_nbest_models > 0:
- if len(self.saved_ckpts) > self.keep_nbest_models:
- if self.avg_keep_nbest_models_type == "acc":
- key = min(self.saved_ckpts, key=self.saved_ckpts.get)
- else:
- key = max(self.saved_ckpts, key=self.saved_ckpts.get)
- if key in self.saved_ckpts:
- del self.saved_ckpts[key]
- filename = os.path.join(self.output_dir, key)
- logging.info(f"Delete: {filename}")
- if os.path.exists(filename):
- # os.remove(filename)
- misc_utils.smart_remove(filename)
+ if self.rank == 0:
+ self.saved_ckpts[ckpt_name] = getattr(
+ self, f"val_{self.avg_keep_nbest_models_type}_step_or_epoch"
+ )[ckpt_name]
+ if self.keep_nbest_models > 0:
+ if len(self.saved_ckpts) > self.keep_nbest_models:
+ if self.avg_keep_nbest_models_type == "acc":
+ key = min(self.saved_ckpts, key=self.saved_ckpts.get)
+ else:
+ key = max(self.saved_ckpts, key=self.saved_ckpts.get)
+ if key in self.saved_ckpts:
+ del self.saved_ckpts[key]
+ filename = os.path.join(self.output_dir, key)
+ logging.info(f"Delete: {filename}")
+ if os.path.exists(filename):
+ # os.remove(filename)
+ misc_utils.smart_remove(filename)
elif self.use_fsdp:
pass
@@ -300,8 +302,8 @@
"optimizer": optim.state_dict(),
"scheduler": scheduler.state_dict(),
"saved_ckpts": self.saved_ckpts,
- "val_acc_step_or_eoch": self.val_acc_step_or_eoch,
- "val_loss_step_or_eoch": self.val_loss_step_or_eoch,
+ "val_acc_step_or_epoch": self.val_acc_step_or_epoch,
+ "val_loss_step_or_epoch": self.val_loss_step_or_epoch,
"best_step_or_epoch": self.best_step_or_epoch,
"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
"step": step,
@@ -352,38 +354,38 @@
if self.avg_keep_nbest_models_type == "acc":
if (
- self.val_acc_step_or_eoch[ckpt_name]
- >= self.val_acc_step_or_eoch[self.best_step_or_epoch]
+ self.val_acc_step_or_epoch[ckpt_name]
+ >= self.val_acc_step_or_epoch[self.best_step_or_epoch]
):
self.best_step_or_epoch = ckpt_name
best_ckpt = Path(os.path.join(self.output_dir, f"model.pt.best"))
torch.save(state, best_ckpt)
logging.info(
- f"Update best acc: {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
+ f"Update best acc: {self.val_acc_step_or_epoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
)
else:
logging.info(
- f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
+ f"No improvement in acc: {self.val_acc_step_or_epoch[ckpt_name]:.4f} < {self.val_acc_step_or_epoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
elif self.avg_keep_nbest_models_type == "loss":
if (
- self.val_loss_step_or_eoch[ckpt_name]
- <= self.val_loss_step_or_eoch[self.best_step_or_epoch]
+ self.val_loss_step_or_epoch[ckpt_name]
+ <= self.val_loss_step_or_epoch[self.best_step_or_epoch]
):
self.best_step_or_epoch = ckpt_name
best_ckpt = Path(os.path.join(self.output_dir, f"model.pt.best"))
torch.save(state, best_ckpt)
logging.info(
- f"Update best loss: {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
+ f"Update best loss: {self.val_loss_step_or_epoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
)
else:
logging.info(
- f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
+ f"No improvement in loss: {self.val_loss_step_or_epoch[ckpt_name]:.4f} > {self.val_loss_step_or_epoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
else:
print("Undo")
self.saved_ckpts[ckpt_name] = getattr(
- self, f"val_{self.avg_keep_nbest_models_type}_step_or_eoch"
+ self, f"val_{self.avg_keep_nbest_models_type}_step_or_epoch"
)[ckpt_name]
if self.keep_nbest_models > 0:
if len(self.saved_ckpts) > self.keep_nbest_models:
@@ -424,14 +426,14 @@
_, checkpoint = model.load_checkpoint(self.output_dir, "model.pt")
self.start_epoch = checkpoint["epoch"]
self.saved_ckpts = checkpoint["saved_ckpts"]
- self.val_acc_step_or_eoch = (
- checkpoint["val_acc_step_or_eoch"]
- if "val_acc_step_or_eoch" in checkpoint
+ self.val_acc_step_or_epoch = (
+ checkpoint["val_acc_step_or_epoch"]
+ if "val_acc_step_or_epoch" in checkpoint
else {}
)
- self.val_loss_step_or_eoch = (
- checkpoint["val_loss_step_or_eoch"]
- if "val_loss_step_or_eoch" in checkpoint
+ self.val_loss_step_or_epoch = (
+ checkpoint["val_loss_step_or_epoch"]
+ if "val_loss_step_or_epoch" in checkpoint
else {}
)
self.best_step_or_epoch = (
@@ -477,7 +479,7 @@
for k_ex in self.excludes:
k_tmp = k.replace("module.", "")
if k_tmp.startswith(k_ex):
- logging.info(f"key: {{k}} matching: {k_ex}, excluded")
+ logging.info(f"key: {k} matching: {k_ex}, excluded")
excludes_flag = True
break
if excludes_flag:
@@ -500,14 +502,14 @@
scaler.load_state_dict(checkpoint["scaler_state"])
self.saved_ckpts = checkpoint["saved_ckpts"]
- self.val_acc_step_or_eoch = (
- checkpoint["val_acc_step_or_eoch"]
- if "val_acc_step_or_eoch" in checkpoint
+ self.val_acc_step_or_epoch = (
+ checkpoint["val_acc_step_or_epoch"]
+ if "val_acc_step_or_epoch" in checkpoint
else {}
)
- self.val_loss_step_or_eoch = (
- checkpoint["val_loss_step_or_eoch"]
- if "val_loss_step_or_eoch" in checkpoint
+ self.val_loss_step_or_epoch = (
+ checkpoint["val_loss_step_or_epoch"]
+ if "val_loss_step_or_epoch" in checkpoint
else {}
)
self.best_step_or_epoch = (
@@ -802,8 +804,8 @@
ckpt_name = f"model.pt.ep{epoch}"
else:
ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_in_epoch")}'
- self.val_acc_step_or_eoch[ckpt_name] = self.val_acc_avg
- self.val_loss_step_or_eoch[ckpt_name] = self.val_loss_avg
+ self.val_acc_step_or_epoch[ckpt_name] = self.val_acc_avg
+ self.val_loss_step_or_epoch[ckpt_name] = self.val_loss_avg
if self.use_ddp or self.use_fsdp or self.use_deepspeed:
dist.barrier()
--
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