From 82530ddf974a706df5a6a1e258d80c8dbc3f1d72 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 10 六月 2024 09:19:16 +0800
Subject: [PATCH] fix bug

---
 funasr/bin/train_ds.py |   18 +++++++++++++++---
 1 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/funasr/bin/train_ds.py b/funasr/bin/train_ds.py
index b0931b0..d9b7679 100644
--- a/funasr/bin/train_ds.py
+++ b/funasr/bin/train_ds.py
@@ -130,8 +130,8 @@
 
     model = trainer.warp_model(model)
 
-    kwargs["device"] = next(model.parameters()).device
-    trainer.device = kwargs["device"]
+    kwargs["device"] = int(os.environ.get("LOCAL_RANK", 0))
+    trainer.device = int(os.environ.get("LOCAL_RANK", 0))
 
     model, optim, scheduler = trainer.warp_optim_scheduler(model, **kwargs)
 
@@ -158,6 +158,8 @@
         time1 = time.perf_counter()
 
         for data_split_i in range(trainer.start_data_split_i, dataloader.data_split_num):
+            time_slice_i = time.perf_counter()
+
             dataloader_tr, dataloader_val = dataloader.build_iter(
                 epoch, data_split_i=data_split_i, start_step=trainer.start_step
             )
@@ -177,6 +179,14 @@
             trainer.start_step = 0
 
             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"
+            )
 
         trainer.start_data_split_i = 0
         trainer.validate_epoch(model=model, dataloader_val=dataloader_val, epoch=epoch + 1)
@@ -198,7 +208,9 @@
         trainer.train_loss_avg = 0.0
 
     if trainer.rank == 0:
-        average_checkpoints(trainer.output_dir, trainer.avg_nbest_model)
+        average_checkpoints(
+            trainer.output_dir, trainer.avg_nbest_model, use_deepspeed=trainer.use_deepspeed
+        )
 
     trainer.close()
 

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