From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/bin/train.py | 52 +++++++++++++++++++++++++++++++++++++---------------
1 files changed, 37 insertions(+), 15 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index eb1611a..2729b80 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -13,7 +13,7 @@
from contextlib import nullcontext
import torch.distributed as dist
-from collections.abc import Sequence
+
from omegaconf import DictConfig, OmegaConf
from torch.cuda.amp import autocast, GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
@@ -27,7 +27,7 @@
from funasr.train_utils.trainer import Trainer
from funasr.schedulers import scheduler_classes
from funasr.train_utils.initialize import initialize
-from funasr.download.download_from_hub import download_model
+from funasr.download.download_model_from_hub import download_model
from funasr.models.lora.utils import mark_only_lora_as_trainable
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
@@ -99,7 +99,7 @@
if freeze_param is not None:
if "," in freeze_param:
freeze_param = eval(freeze_param)
- if isinstance(freeze_param, Sequence):
+ if not isinstance(freeze_param, (list, tuple)):
freeze_param = (freeze_param,)
logging.info("freeze_param is not None: %s", freeze_param)
for t in freeze_param:
@@ -107,8 +107,9 @@
if k.startswith(t + ".") or k == t:
logging.info(f"Setting {k}.requires_grad = False")
p.requires_grad = False
+ if local_rank == 0:
+ logging.info(f"{model_summary(model)}")
- logging.info(f"model info: {model_summary(model)}")
if use_ddp:
model = model.cuda(local_rank)
model = DDP(
@@ -145,8 +146,6 @@
else:
model = model.to(device=kwargs.get("device", "cuda"))
- if local_rank == 0:
- logging.info(f"{model}")
kwargs["device"] = next(model.parameters()).device
# optim
@@ -182,23 +181,32 @@
scaler = GradScaler(enabled=trainer.use_fp16) if trainer.use_fp16 else None
scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler
- trainer.resume_checkpoint(model=model, optim=optim, scheduler=scheduler, scaler=scaler)
+ trainer.resume_checkpoint(
+ model=model,
+ optim=optim,
+ scheduler=scheduler,
+ scaler=scaler,
+ )
tensorboard_dir = os.path.join(kwargs.get("output_dir"), "tensorboard")
os.makedirs(tensorboard_dir, exist_ok=True)
try:
from tensorboardX import SummaryWriter
- writer = SummaryWriter(tensorboard_dir) if trainer.rank == 0 else None
+ writer = SummaryWriter(tensorboard_dir) # if trainer.rank == 0 else None
except:
writer = None
dataloader_tr, dataloader_val = None, None
- for epoch in range(trainer.start_epoch, trainer.max_epoch + 1):
+ for epoch in range(trainer.start_epoch, trainer.max_epoch):
time1 = time.perf_counter()
- for data_split_i in range(dataloader.data_split_num):
- dataloader_tr, dataloader_val = dataloader.build_iter(epoch, data_split_i=data_split_i)
+ 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
+ )
+
trainer.train_epoch(
model=model,
optim=optim,
@@ -210,17 +218,29 @@
writer=writer,
data_split_i=data_split_i,
data_split_num=dataloader.data_split_num,
+ start_step=trainer.start_step,
)
-
+ 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} slices, {(dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours, "
+ f"epoch: {trainer.max_epoch - epoch} epochs, {((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, writer=writer
+ model=model, dataloader_val=dataloader_val, epoch=epoch + 1, writer=writer
)
scheduler.step()
-
- trainer.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler)
+ trainer.step_in_epoch = 0
+ trainer.save_checkpoint(
+ epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler
+ )
time2 = time.perf_counter()
time_escaped = (time2 - time1) / 3600.0
@@ -230,6 +250,8 @@
f"estimated to finish {trainer.max_epoch} "
f"epoch: {(trainer.max_epoch - epoch) * time_escaped:.3f} hours\n"
)
+ trainer.train_acc_avg = 0.0
+ trainer.train_loss_avg = 0.0
if trainer.rank == 0:
average_checkpoints(trainer.output_dir, trainer.avg_nbest_model)
--
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