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/bin/train.py | 14 ++++++++------
1 files changed, 8 insertions(+), 6 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index 1feb416..c56d047 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -20,6 +20,7 @@
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.algorithms.join import Join
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
+from tensorboardX import SummaryWriter
from funasr.train_utils.average_nbest_models import average_checkpoints
from funasr.register import tables
@@ -27,7 +28,7 @@
from funasr.train_utils.trainer import Trainer
from funasr.schedulers import scheduler_classes
from funasr.train_utils.initialize import initialize
-from funasr.download.download_from_hub import download_model
+from funasr.download.download_model_from_hub import download_model
from funasr.models.lora.utils import mark_only_lora_as_trainable
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
@@ -191,8 +192,6 @@
tensorboard_dir = os.path.join(kwargs.get("output_dir"), "tensorboard")
os.makedirs(tensorboard_dir, exist_ok=True)
try:
- from tensorboardX import SummaryWriter
-
writer = SummaryWriter(tensorboard_dir) # if trainer.rank == 0 else None
except:
writer = None
@@ -222,14 +221,17 @@
)
trainer.start_step = 0
- torch.cuda.empty_cache()
+ device = next(model.parameters()).device
+ if device.type == "cuda":
+ with torch.cuda.device(device):
+ torch.cuda.empty_cache()
time_escaped = (time.perf_counter() - time_slice_i) / 3600.0
logging.info(
f"rank: {local_rank}, "
f"time_escaped_epoch: {time_escaped:.3f} hours, "
- f"estimated to finish {dataloader.data_split_num} data_slices, remaining: {(dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours"
- f"epoch: {((trainer.max_epoch - epoch - 1)*dataloader.data_split_num + dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours\n"
+ f"estimated to finish {dataloader.data_split_num} data_slices, remaining: {dataloader.data_split_num-data_split_i} slices, {(dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours, "
+ f"epoch: {trainer.max_epoch - epoch} epochs, {((trainer.max_epoch - epoch - 1)*dataloader.data_split_num + dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours\n"
)
trainer.start_data_split_i = 0
--
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