From 93ef505e2d426b6aa1e58c0b4721999de789ff8e Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 28 四月 2024 15:14:57 +0800
Subject: [PATCH] Dev gzf exp (#1670)
---
funasr/train_utils/trainer.py | 31 +++++++++++++++++++++++++++----
1 files changed, 27 insertions(+), 4 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 66f8778..5685b8f 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -116,6 +116,7 @@
self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
self.start_data_split_i = 0
self.start_step = 0
+ self.step_cur_in_epoch = 0
self.use_wandb = kwargs.get("use_wandb", False)
if self.use_wandb:
wandb.login(key=kwargs.get("wandb_token"))
@@ -137,6 +138,8 @@
optim=None,
scheduler=None,
scaler=None,
+ step_cur_in_epoch=None,
+ **kwargs,
):
"""
Saves a checkpoint containing the model's state, the optimizer's state,
@@ -147,6 +150,7 @@
epoch (int): The epoch number at which the checkpoint is being saved.
"""
+ step_cur_in_epoch = None if step is None else step_cur_in_epoch
if self.rank == 0:
logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
# self.step_or_epoch += 1
@@ -161,7 +165,12 @@
"best_step_or_epoch": self.best_step_or_epoch,
"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
"step": step,
+ "step_cur_in_epoch": step_cur_in_epoch,
+ "data_split_i": kwargs.get("data_split_i", 0),
+ "data_split_num": kwargs.get("data_split_num", 1),
+ "batch_total": self.batch_total,
}
+ step = step_cur_in_epoch
if hasattr(model, "module"):
state["state_dict"] = model.module.state_dict()
@@ -293,6 +302,12 @@
self.batch_total = checkpoint["batch_total"] if "batch_total" in checkpoint else 0
self.start_step = checkpoint["step"] if "step" in checkpoint else 0
self.start_step = 0 if self.start_step is None else self.start_step
+ self.step_cur_in_epoch = (
+ checkpoint["step_cur_in_epoch"] if "step_cur_in_epoch" in checkpoint else 0
+ )
+ self.step_cur_in_epoch = (
+ 0 if self.step_cur_in_epoch is None else self.step_cur_in_epoch
+ )
model.to(self.device)
print(f"Checkpoint loaded successfully from '{ckpt}'")
@@ -321,7 +336,7 @@
"""
if self.use_ddp or self.use_fsdp:
dist.barrier()
- logging.info(f"Train epoch: {epoch}, rank: {self.local_rank}\n")
+ logging.info(f"Train epoch: {epoch}, rank: {self.rank}\n")
model.train()
# Set the number of steps for gradient accumulation
@@ -341,6 +356,7 @@
if iterator_stop > 0:
break
self.batch_total += 1
+ self.step_cur_in_epoch += 1
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1-time_beg:0.3f}"
@@ -443,6 +459,7 @@
self.log(
epoch,
batch_idx,
+ step_cur_in_epoch=self.step_cur_in_epoch,
batch_num_epoch=batch_num_epoch,
lr=lr,
loss=loss.detach().cpu().item(),
@@ -461,6 +478,7 @@
epoch=epoch,
writer=writer,
step=batch_idx + 1,
+ step_cur_in_epoch=self.step_cur_in_epoch,
)
if (batch_idx + 1) % self.save_checkpoint_interval == 0:
@@ -471,6 +489,9 @@
scheduler=scheduler,
scaler=scaler,
step=batch_idx + 1,
+ step_cur_in_epoch=self.step_cur_in_epoch,
+ data_split_i=kwargs.get("data_split_i", 0),
+ data_split_num=kwargs.get("data_split_num", 1),
)
time_beg = time.perf_counter()
@@ -500,7 +521,7 @@
"""
if self.use_ddp or self.use_fsdp:
dist.barrier()
- logging.info(f"Validate epoch: {epoch}, rank: {self.local_rank}\n")
+ logging.info(f"Validate epoch: {epoch}, rank: {self.rank}\n")
model.eval()
with torch.no_grad():
@@ -578,10 +599,10 @@
iterator_stop.fill_(1)
dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
- if kwargs.get("step", None) is None:
+ if kwargs.get("step_cur_in_epoch", None) is None:
ckpt_name = f"model.pt.ep{epoch}"
else:
- ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step")}'
+ ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_cur_in_epoch")}'
self.val_acc_step_or_eoch[ckpt_name] = self.val_acc_avg
self.val_loss_step_or_eoch[ckpt_name] = self.val_loss_avg
model.train()
@@ -594,6 +615,7 @@
self,
epoch=0,
batch_idx=0,
+ step_cur_in_epoch=0,
batch_num_epoch=-1,
lr=0.0,
loss=0.0,
@@ -626,6 +648,7 @@
f"{tag}, "
f"rank: {self.rank}, "
f"epoch: {epoch}/{self.max_epoch}, "
+ f"step_cur_in_epoch: {step_cur_in_epoch}, "
f"data_slice: {data_split_i}/{data_split_num}, "
f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
f"(loss_avg_rank: {loss:.3f}), "
--
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