From 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 19:50:07 +0800
Subject: [PATCH] update
---
funasr/train/trainer.py | 33 +++++++++++++++++++++++++++------
1 files changed, 27 insertions(+), 6 deletions(-)
diff --git a/funasr/train/trainer.py b/funasr/train/trainer.py
index efe2009..4f2e28c 100644
--- a/funasr/train/trainer.py
+++ b/funasr/train/trainer.py
@@ -39,7 +39,7 @@
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.recursive_op import recursive_average
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
-from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.models.base_model import FunASRModel
from funasr.train.distributed_utils import DistributedOption
from funasr.train.reporter import Reporter
from funasr.train.reporter import SubReporter
@@ -94,7 +94,7 @@
wandb_model_log_interval: int
use_pai: bool
oss_bucket: Union[oss2.Bucket, None]
-
+ batch_interval: int
class Trainer:
"""Trainer having a optimizer.
@@ -165,7 +165,7 @@
@classmethod
def run(
cls,
- model: AbsESPnetModel,
+ model: FunASRModel,
optimizers: Sequence[torch.optim.Optimizer],
schedulers: Sequence[Optional[AbsScheduler]],
train_iter_factory: AbsIterFactory,
@@ -186,7 +186,10 @@
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()
if trainer_options.use_amp:
@@ -560,13 +563,31 @@
# [For distributed] Because iteration counts are not always equals between
# processes, send stop-flag to the other processes if iterator is finished
iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")
-
+
+ #get the rank
+ rank = distributed_option.dist_rank
+ #get the num batch updates
+ num_batch_updates = 0
+ #ouput dir
+ output_dir = Path(options.output_dir)
+ #batch interval
+ batch_interval = options.batch_interval
+ assert batch_interval > 0
+
start_time = time.perf_counter()
for iiter, (_, batch) in enumerate(
reporter.measure_iter_time(iterator, "iter_time"), 1
):
assert isinstance(batch, dict), type(batch)
+ if 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) and options.use_pai:
+ buffer = BytesIO()
+ torch.save(model.state_dict(), buffer)
+ options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}batch.pth"), buffer.getvalue())
+
if distributed:
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
if iterator_stop > 0:
@@ -811,4 +832,4 @@
else:
if distributed:
iterator_stop.fill_(1)
- torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
\ No newline at end of file
+ torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
--
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