From 407625a73478c2bb5e20c62bfbdf53a55a1e6575 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 12 六月 2024 15:18:42 +0800
Subject: [PATCH] decoding

---
 funasr/train_utils/trainer_ds.py |   17 ++++++++++++++++-
 1 files changed, 16 insertions(+), 1 deletions(-)

diff --git a/funasr/train_utils/trainer_ds.py b/funasr/train_utils/trainer_ds.py
index 1a553f8..c973728 100644
--- a/funasr/train_utils/trainer_ds.py
+++ b/funasr/train_utils/trainer_ds.py
@@ -147,6 +147,10 @@
 
         self.use_deepspeed = use_deepspeed
         self.deepspeed_config = kwargs.get("deepspeed_config", "")
+        self.excludes = kwargs.get("excludes", None)
+        if self.excludes is not None:
+            if isinstance(self.excludes, str):
+                self.excludes = self.excludes.split(",")
 
     def save_checkpoint(
         self,
@@ -167,6 +171,8 @@
         Args:
             epoch (int): The epoch number at which the checkpoint is being saved.
         """
+        if self.use_ddp or self.use_fsdp:
+            dist.barrier()
         step_in_epoch = None if step is None else step_in_epoch
         if self.use_deepspeed:
 
@@ -438,6 +444,12 @@
                     src_state = checkpoint["state_dict"]
                     dst_state = model.state_dict()
                     for k in dst_state.keys():
+                        if excludes is not None:
+                            for k_ex in excludes:
+                                k_tmp = k.replace("module.", "")
+                                if k_tmp.startswith(k_ex):
+                                    logging.info(f"key: {{k}} matching: {k_ex}, excluded")
+                                    continue
                         if not k.startswith("module.") and "module." + k in src_state.keys():
                             k_ddp = "module." + k
                         elif k.startswith("module.") and "module." + k not in src_state.keys():
@@ -621,7 +633,6 @@
             self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
 
     def forward_step(self, model, batch, loss_dict={}):
-        dtype = torch.bfloat16
         with maybe_autocast(dtype=self.dtype, use_deepspeed=self.use_deepspeed):
             retval = model(**batch)
 
@@ -761,6 +772,10 @@
             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
+
+        if self.use_ddp or self.use_fsdp or self.use_deepspeed:
+            dist.barrier()
+
         model.train()
 
     def log(

--
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