From 3cd3473bf7a3b41484baa86d9092248d78e7af39 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 四月 2023 17:17:37 +0800
Subject: [PATCH] docs
---
funasr/train/trainer.py | 22 ++++++++++++----------
1 files changed, 12 insertions(+), 10 deletions(-)
diff --git a/funasr/train/trainer.py b/funasr/train/trainer.py
index 2260f00..7c187e9 100644
--- a/funasr/train/trainer.py
+++ b/funasr/train/trainer.py
@@ -186,9 +186,6 @@
logging.warning("No keep_nbest_models is given. Change to [1]")
trainer_options.keep_nbest_models = [1]
keep_nbest_models = trainer_options.keep_nbest_models
-
- #assert batch_interval is set and >0
- assert trainer_options.batch_interval > 0
output_dir = Path(trainer_options.output_dir)
reporter = Reporter()
@@ -571,8 +568,7 @@
#ouput dir
output_dir = Path(options.output_dir)
#batch interval
- batch_interval = options.batch_interval
- assert batch_interval > 0
+ batch_interval = options.batch_interval
start_time = time.perf_counter()
for iiter, (_, batch) in enumerate(
@@ -580,16 +576,22 @@
):
assert isinstance(batch, dict), type(batch)
- if rank == 0:
+ if batch_interval > 0 and (not distributed_option.distributed or rank == 0):
if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")):
num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else model.module.get_num_updates()
- if (num_batch_updates%batch_interval == 0) and (options.oss_bucket is not None):
- if options.use_pai:
+ if num_batch_updates % batch_interval == 0:
+ if options.use_pai and options.oss_bucket is not None:
buffer = BytesIO()
- torch.save(model.state_dict(), buffer)
+ if hasattr(model, "module"):
+ torch.save(model.module.state_dict(), buffer)
+ else:
+ torch.save(model.state_dict(), buffer)
options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue())
else:
- torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
+ if hasattr(model, "module"):
+ torch.save(model.module.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
+ else:
+ torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
if distributed:
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
--
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