From 7a3b8744859d8a6c81c56bf2979970b8f197cacb Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 25 四月 2023 01:15:59 +0800
Subject: [PATCH] update
---
funasr/build_utils/build_trainer.py | 52 +++++++++++++++++++++++++++++++++++-----------------
1 files changed, 35 insertions(+), 17 deletions(-)
diff --git a/funasr/build_utils/build_trainer.py b/funasr/build_utils/build_trainer.py
index 55bc89c..437baa5 100644
--- a/funasr/build_utils/build_trainer.py
+++ b/funasr/build_utils/build_trainer.py
@@ -75,14 +75,14 @@
grad_clip: float
grad_clip_type: float
log_interval: Optional[int]
- no_forward_run: bool
- use_tensorboard: bool
- use_wandb: bool
+ # no_forward_run: bool
+ # use_tensorboard: bool
+ # use_wandb: bool
output_dir: Union[Path, str]
max_epoch: int
max_update: int
seed: int
- sharded_ddp: bool
+ # sharded_ddp: bool
patience: Optional[int]
keep_nbest_models: Union[int, List[int]]
nbest_averaging_interval: int
@@ -90,7 +90,7 @@
best_model_criterion: Sequence[Sequence[str]]
val_scheduler_criterion: Sequence[str]
unused_parameters: bool
- wandb_model_log_interval: int
+ # wandb_model_log_interval: int
use_pai: bool
oss_bucket: Union[oss2.Bucket, None]
@@ -107,7 +107,6 @@
schedulers: Sequence[Optional[AbsScheduler]],
train_dataloader: AbsIterFactory,
valid_dataloader: AbsIterFactory,
- trainer_options,
distributed_option: DistributedOption):
self.trainer_options = self.build_options(args)
self.model = model
@@ -115,7 +114,6 @@
self.schedulers = schedulers
self.train_dataloader = train_dataloader
self.valid_dataloader = valid_dataloader
- self.trainer_options = trainer_options
self.distributed_option = distributed_option
def build_options(self, args: argparse.Namespace) -> TrainerOptions:
@@ -128,16 +126,15 @@
"""Reserved for future development of another Trainer"""
pass
- @staticmethod
- def resume(
- checkpoint: Union[str, Path],
- model: torch.nn.Module,
- reporter: Reporter,
- optimizers: Sequence[torch.optim.Optimizer],
- schedulers: Sequence[Optional[AbsScheduler]],
- scaler: Optional[GradScaler],
- ngpu: int = 0,
- ):
+ def resume(self,
+ checkpoint: Union[str, Path],
+ model: torch.nn.Module,
+ reporter: Reporter,
+ optimizers: Sequence[torch.optim.Optimizer],
+ schedulers: Sequence[Optional[AbsScheduler]],
+ scaler: Optional[GradScaler],
+ ngpu: int = 0,
+ ):
states = torch.load(
checkpoint,
map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
@@ -800,3 +797,24 @@
if distributed:
iterator_stop.fill_(1)
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
+
+
+def build_trainer(
+ args,
+ model: FunASRModel,
+ optimizers: Sequence[torch.optim.Optimizer],
+ schedulers: Sequence[Optional[AbsScheduler]],
+ train_dataloader: AbsIterFactory,
+ valid_dataloader: AbsIterFactory,
+ distributed_option: DistributedOption
+):
+ trainer = Trainer(
+ args=args,
+ model=model,
+ optimizers=optimizers,
+ schedulers=schedulers,
+ train_dataloader=train_dataloader,
+ valid_dataloader=valid_dataloader,
+ distributed_option=distributed_option
+ )
+ return trainer
--
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