From f57b68121a526baea43b2e93f4540d8a2995f633 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 29 四月 2024 15:15:24 +0800
Subject: [PATCH] batch
---
funasr/train_utils/trainer.py | 98 +++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 82 insertions(+), 16 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 713e171..e86420c 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -15,6 +15,11 @@
from funasr.train_utils.average_nbest_models import average_checkpoints
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
+try:
+ import wandb
+except:
+ wandb = None
+
@contextmanager
def maybe_autocast(enabled):
@@ -108,6 +113,23 @@
self.val_acc_step_or_eoch = {}
self.val_loss_step_or_eoch = {}
+ self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
+ self.start_data_split_i = 0
+ self.start_step = 0
+ self.step_in_epoch = 0
+ self.use_wandb = kwargs.get("use_wandb", False)
+ if self.use_wandb:
+ wandb.login(key=kwargs.get("wandb_token"))
+ wandb.init(
+ config=kwargs,
+ project=kwargs.get("wandb_project", "my_project"),
+ entity=kwargs.get("wandb_team", "my_team"),
+ name=kwargs.get("wandb_exp_name", "my_exp"),
+ dir=output_dir,
+ job_type="training",
+ reinit=True,
+ )
+
def save_checkpoint(
self,
epoch,
@@ -116,6 +138,8 @@
optim=None,
scheduler=None,
scaler=None,
+ step_in_epoch=None,
+ **kwargs,
):
"""
Saves a checkpoint containing the model's state, the optimizer's state,
@@ -126,6 +150,7 @@
epoch (int): The epoch number at which the checkpoint is being saved.
"""
+ step_in_epoch = None if step is None else step_in_epoch
if self.rank == 0:
logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
# self.step_or_epoch += 1
@@ -139,7 +164,13 @@
"val_loss_step_or_eoch": self.val_loss_step_or_eoch,
"best_step_or_epoch": self.best_step_or_epoch,
"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
+ "step": step,
+ "step_in_epoch": step_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_in_epoch
if hasattr(model, "module"):
state["state_dict"] = model.module.state_dict()
@@ -173,7 +204,7 @@
)
else:
logging.info(
- f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}"
+ f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
elif self.avg_keep_nbest_models_type == "loss":
if (
@@ -188,7 +219,7 @@
)
else:
logging.info(
- f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}"
+ f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
else:
print("Undo")
@@ -229,7 +260,7 @@
ckpt = os.path.join(self.output_dir, "model.pt")
if os.path.isfile(ckpt):
checkpoint = torch.load(ckpt, map_location="cpu")
- self.start_epoch = checkpoint["epoch"] + 1
+ self.start_epoch = checkpoint["epoch"]
# self.model.load_state_dict(checkpoint['state_dict'])
src_state = checkpoint["state_dict"]
dst_state = model.state_dict()
@@ -265,6 +296,17 @@
self.best_step_or_epoch = (
checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
)
+ self.start_data_split_i = (
+ checkpoint["data_split_i"] if "data_split_i" in checkpoint else 0
+ )
+ 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_in_epoch = (
+ checkpoint["step_in_epoch"] if "step_in_epoch" in checkpoint else 0
+ )
+ self.step_in_epoch = 0 if self.step_in_epoch is None else self.step_in_epoch
+
model.to(self.device)
print(f"Checkpoint loaded successfully from '{ckpt}'")
else:
@@ -292,7 +334,7 @@
"""
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
@@ -312,6 +354,7 @@
if iterator_stop > 0:
break
self.batch_total += 1
+ self.step_in_epoch += 1
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1-time_beg:0.3f}"
@@ -324,6 +367,12 @@
time2 = time.perf_counter()
with maybe_autocast(self.use_fp16):
retval = model(**batch)
+
+ if (
+ self.reset_gpu_cache
+ and (torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024) > 70
+ ):
+ torch.cuda.empty_cache()
time3 = time.perf_counter()
speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
@@ -408,6 +457,7 @@
self.log(
epoch,
batch_idx,
+ step_in_epoch=self.step_in_epoch,
batch_num_epoch=batch_num_epoch,
lr=lr,
loss=loss.detach().cpu().item(),
@@ -419,16 +469,17 @@
data_split_num=kwargs.get("data_split_num", 1),
)
- if (batch_idx + 1) % self.validate_interval == 0:
+ if self.step_in_epoch % self.validate_interval == 0:
self.validate_epoch(
model=model,
dataloader_val=dataloader_val,
epoch=epoch,
writer=writer,
step=batch_idx + 1,
+ step_in_epoch=self.step_in_epoch,
)
- if (batch_idx + 1) % self.save_checkpoint_interval == 0:
+ if self.step_in_epoch % self.save_checkpoint_interval == 0:
self.save_checkpoint(
epoch,
model=model,
@@ -436,6 +487,9 @@
scheduler=scheduler,
scaler=scaler,
step=batch_idx + 1,
+ step_in_epoch=self.step_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()
@@ -465,7 +519,7 @@
"""
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():
@@ -543,10 +597,10 @@
iterator_stop.fill_(1)
dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
- if kwargs.get("step", None) is None:
+ if kwargs.get("step_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_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()
@@ -559,6 +613,7 @@
self,
epoch=0,
batch_idx=0,
+ step_in_epoch=0,
batch_num_epoch=-1,
lr=0.0,
loss=0.0,
@@ -589,10 +644,10 @@
acc_avg_epoch = getattr(self, f"{tag}_acc_avg")
description = (
f"{tag}, "
- f"rank: {self.local_rank}, "
+ f"rank: {self.rank}, "
f"epoch: {epoch}/{self.max_epoch}, "
f"data_slice: {data_split_i}/{data_split_num}, "
- f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
+ f"step_in_slice: {batch_idx + 1}/{batch_num_epoch}, step_in_epoch: {step_in_epoch}, total step: {self.batch_total}, "
f"(loss_avg_rank: {loss:.3f}), "
f"(loss_avg_epoch: {loss_avg_epoch:.3f}), "
f"(ppl_avg_epoch: {math.exp(loss_avg_epoch):.3e}), "
@@ -604,18 +659,29 @@
)
logging.info(description)
+ description_dict = {
+ f"rank{self.rank}_loss/{tag}": loss,
+ f"rank{self.rank}_lr/{tag}": lr,
+ }
+
if writer is not None:
- writer.add_scalar(f"rank{self.local_rank}_loss/{tag}", loss, self.batch_total)
- writer.add_scalar(f"rank{self.local_rank}_lr/{tag}", lr, self.batch_total)
- writer.add_scalar(f"rank{self.local_rank}_lr/{tag}", lr, self.batch_total)
+ writer.add_scalar(f"rank{self.rank}_loss/{tag}", loss, self.batch_total)
+ writer.add_scalar(f"rank{self.rank}_lr/{tag}", lr, self.batch_total)
for key, var in stats.items():
writer.add_scalar(
- f"stats_rank{self.local_rank}_{key}/{tag}", var.item(), self.batch_total
+ f"stats_rank{self.rank}_{key}/{tag}", var.item(), self.batch_total
)
+ description_dict[f"stats_rank{self.rank}_{key}/{tag}"] = var.item()
for key, var in speed_stats.items():
writer.add_scalar(
- f"stats_rank{self.local_rank}_{key}/{tag}", eval(var), self.batch_total
+ f"stats_rank{self.rank}_{key}/{tag}", eval(var), self.batch_total
)
+ description_dict[f"stats_rank{self.rank}_{key}/{tag}"] = eval(var)
+ if self.use_wandb and wandb is not None:
+ wandb.log(
+ description_dict,
+ setp=self.batch_total,
+ )
def close(self, writer=None):
--
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