speech_recognition/FunASR
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This commit is contained in:
游雁 2024-02-20 17:02:44 +08:00
parent cb8b09e085
commit 58b6154a73
2 changed files with 16 additions and 12 deletions
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4
funasr/bin/train.py
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@ -44,14 +44,16 @@ def main_hydra(kwargs: DictConfig):
def main(**kwargs):
print(kwargs)
# set random seed
tables.print()
set_all_random_seed(kwargs.get("seed", 0))
torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
local_rank = int(os.environ.get('LOCAL_RANK', 0))
if local_rank == 0:
tables.print()
# Check if we are using DDP or FSDP
use_ddp = 'WORLD_SIZE' in os.environ and int(os.environ["WORLD_SIZE"]) > 1
use_fsdp = kwargs.get("use_fsdp", None)

24
funasr/train_utils/trainer.py
View File

@ -69,6 +69,7 @@ class Trainer:
self.device = next(model.parameters()).device
self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
self.kwargs = kwargs
self.log_interval = kwargs.get("log_interval", 50)
try:
@ -274,8 +275,8 @@ class Trainer:
if self.local_rank == 0:
pbar.update(1)
if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1:
pbar.update(self.log_interval)
gpu_info = "GPU, memory: {:.3f} GB, " \
"{:.3f} GB, "\
"{:.3f} GB, "\
@ -285,23 +286,23 @@ class Trainer:
torch.cuda.max_memory_reserved()/1024/1024/1024,
)
description = (
f"rank: {self.local_rank}, "
f"Train epoch: {epoch}/{self.max_epoch}, "
f"step {batch_idx}/{len(self.dataloader_train)}, "
f"{speed_stats}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
f"{gpu_info}"
f"rank: {self.local_rank}"
)
pbar.set_description(description)
if self.writer:
self.writer.add_scalar('Loss/train', loss.item(),
self.writer.add_scalar(f'rank{self.local_rank}, Loss/train', loss.item(),
epoch*len(self.dataloader_train) + batch_idx)
for key, var in stats.items():
self.writer.add_scalar(f'{key}/train', var.item(),
self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', var.item(),
epoch * len(self.dataloader_train) + batch_idx)
for key, var in speed_stats.items():
self.writer.add_scalar(f'{key}/train', eval(var),
self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', eval(var),
epoch * len(self.dataloader_train) + batch_idx)
# if batch_idx == 2:
@ -347,9 +348,10 @@ class Trainer:
time4 = time.perf_counter()
if self.local_rank == 0:
pbar.update(1)
if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1:
pbar.update(self.log_interval)
description = (
f"rank: {self.local_rank}, "
f"validation epoch: {epoch}/{self.max_epoch}, "
f"step {batch_idx}/{len(self.dataloader_train)}, "
f"{speed_stats}, "
@ -359,11 +361,11 @@ class Trainer:
)
pbar.set_description(description)
if self.writer:
self.writer.add_scalar('Loss/val', loss.item(),
self.writer.add_scalar(f"rank{self.local_rank}, Loss/val", loss.item(),
epoch*len(self.dataloader_train) + batch_idx)
for key, var in stats.items():
self.writer.add_scalar(f'{key}/val', var.item(),
self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', var.item(),
epoch * len(self.dataloader_train) + batch_idx)
for key, var in speed_stats.items():
self.writer.add_scalar(f'{key}/val', eval(var),
self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', eval(var),
epoch * len(self.dataloader_train) + batch_idx)