From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/train_utils/trainer_ds.py | 120 +++++++++++++++++++++++++++++++-----------------------------
1 files changed, 62 insertions(+), 58 deletions(-)
diff --git a/funasr/train_utils/trainer_ds.py b/funasr/train_utils/trainer_ds.py
index 8a0679c..ce8809c 100644
--- a/funasr/train_utils/trainer_ds.py
+++ b/funasr/train_utils/trainer_ds.py
@@ -121,8 +121,8 @@
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 +194,8 @@
# "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 +233,8 @@
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 +244,16 @@
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 +263,31 @@
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 +301,8 @@
"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 +353,38 @@
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 +425,14 @@
_, 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 +478,7 @@
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 +501,14 @@
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 = (
@@ -682,7 +683,7 @@
scaled_loss = model.backward(loss)
else:
loss = loss / self.accum_grad
- if self.use_fp16 or self.use_bf16:
+ if scaler:
scaler.scale(loss).backward()
else:
loss.backward()
@@ -710,7 +711,7 @@
# Execute an optimization step (update model parameters)
if self.use_ddp or self.use_fsdp:
dist.barrier()
- if self.use_fp16 or self.use_bf16:
+ if scaler:
scaler.step(optim)
scaler.update()
else:
@@ -734,6 +735,9 @@
Args:
epoch (int): The current epoch number.
"""
+ self.val_loss_avg = 0.0
+ self.val_acc_avg = 0.0
+
if self.use_ddp or self.use_fsdp or self.use_deepspeed:
dist.barrier()
logging.info(f"Validate epoch: {epoch}, rank: {self.rank}\n")
@@ -755,7 +759,7 @@
"data_split_i": kwargs.get("data_split_i", 0),
"data_split_num": kwargs.get("data_split_num", 1),
"log_step": batch_idx + kwargs.get("start_step", 0),
- "batch_total": batch_idx + 1,
+ "batch_total": self.batch_total,
"step_in_epoch": batch_idx + 1,
"lr": 0.0,
}
@@ -802,8 +806,8 @@
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()
@@ -881,7 +885,7 @@
if self.use_wandb and wandb is not None:
wandb.log(
description_dict,
- setp=batch_total,
+ step=batch_total,
)
def close(self, writer=None):
--
Gitblit v1.9.1