From 35b1c051f6db3649a818547902497d219c871b84 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 14 三月 2024 09:33:30 +0800
Subject: [PATCH] Dev gzf llm (#1493)
---
funasr/train_utils/trainer.py | 13 ++++++++++---
1 files changed, 10 insertions(+), 3 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 723a149..a00b3de 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -88,6 +88,7 @@
scaler = GradScaler(enabled=use_fp16) if use_fp16 else None
scaler = ShardedGradScaler(enabled=use_fp16) if use_ddp else scaler
self.scaler = scaler
+ self.save_checkpoint_interval = kwargs.get("save_checkpoint_interval", 5000)
try:
@@ -104,7 +105,7 @@
self.writer = SummaryWriter(os.path.join(self.output_dir, "tensorboard")) if rank == 0 else None
- def _save_checkpoint(self, epoch):
+ def _save_checkpoint(self, epoch, step=None):
"""
Saves a checkpoint containing the model's state, the optimizer's state,
and the scheduler's state at the end of the given epoch. This method is
@@ -123,7 +124,11 @@
state["scaler_state"] = self.scaler.state_dict()
# Create output directory if it does not exist
os.makedirs(self.output_dir, exist_ok=True)
- filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
+ if step is None:
+ filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
+ else:
+ filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}.{step}')
+
torch.save(state, filename)
print(f'\nCheckpoint saved to {filename}\n')
@@ -337,8 +342,10 @@
for key, var in speed_stats.items():
self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var), self.batch_total)
-
+ if (batch_idx+1) % self.save_checkpoint_interval == 0 and self.rank == 0:
+ self._save_checkpoint(epoch, step=batch_idx+1)
pbar.close()
+
def _validate_epoch(self, epoch):
"""
--
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