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/train_utils/trainer.py | 94 +++++++++++++++++++++++++++--------------------
1 files changed, 54 insertions(+), 40 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index dd0ac7a..665a7af 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -85,7 +85,12 @@
self.batch_total = 0
self.use_fp16 = use_fp16
self.save_checkpoint_interval = kwargs.get("save_checkpoint_interval", 5000)
- self.validate_interval = kwargs.get("validate_interval", 5000)
+ self.validate_interval = kwargs.get("validate_interval", -1)
+ if self.validate_interval < 0:
+ self.validate_interval = self.save_checkpoint_interval
+ assert (
+ self.save_checkpoint_interval == self.validate_interval
+ ), f"save_checkpoint_interval must equal to validate_interval"
self.keep_nbest_models = kwargs.get("keep_nbest_models", 500)
self.avg_keep_nbest_models_type = kwargs.get("avg_keep_nbest_models_type", "acc")
self.avg_nbest_model = kwargs.get("avg_nbest_model", 10)
@@ -308,6 +313,7 @@
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
+ print(checkpoint["train_acc_avg"])
self.train_acc_avg = (
checkpoint["train_acc_avg"] if "train_acc_avg" in checkpoint else 0
)
@@ -356,10 +362,10 @@
time_beg = time.perf_counter()
time5 = time_beg
for batch_idx, batch in enumerate(dataloader_train):
- if self.use_ddp or self.use_fsdp:
- dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
- if iterator_stop > 0:
- break
+ # if self.use_ddp or self.use_fsdp:
+ # dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+ # if iterator_stop > 0:
+ # break
self.batch_total += 1
self.step_in_epoch += 1
time1 = time.perf_counter()
@@ -375,14 +381,12 @@
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()
+ # 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}"
loss, stats, weight = retval
stats = {k: v for k, v in stats.items() if v is not None}
if self.use_ddp or self.use_fsdp:
@@ -397,34 +401,28 @@
# Multiply world_size because DistributedDataParallel
# automatically normalizes the gradient by world_size.
loss *= self.world_size
+ # loss *= self.world_size
# Scale the loss since we're not updating for every mini-batch
loss = loss / accum_grad
+
+ time3 = time.perf_counter()
+ speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
if self.use_fp16:
scaler.scale(loss).backward()
else:
loss.backward()
time4 = time.perf_counter()
- speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
+ speed_stats["backward_and_AllReaduce_time"] = f"{time4 - time3:0.3f}"
self.train_loss_avg = (
- self.train_loss_avg * (self.step_in_epoch - 1) + loss.detach().cpu().item()
- ) / self.step_in_epoch
+ self.train_loss_avg * (batch_idx + kwargs.get("start_step", 0))
+ + loss.detach().cpu().item()
+ ) / (batch_idx + kwargs.get("start_step", 0) + 1)
if "acc" in stats:
self.train_acc_avg = (
- self.train_acc_avg * (self.step_in_epoch - 1)
+ self.train_acc_avg * (batch_idx + kwargs.get("start_step", 0))
+ stats["acc"].detach().cpu().item()
- ) / self.step_in_epoch
- if self.use_ddp or self.use_fsdp:
- train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(
- self.device
- )
- train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(
- self.device
- )
- dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
- dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
- self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
- self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
+ ) / (batch_idx + kwargs.get("start_step", 0) + 1)
# Perform an optimizer step only after accumulating enough gradients
if (batch_idx + 1) % accum_grad == 0:
@@ -453,8 +451,22 @@
scheduler.step()
# Clear gradients for the next accumulation stage
optim.zero_grad(set_to_none=True)
- total_time = f"{time.perf_counter() - time5:0.3f}"
+
+ if self.use_ddp or self.use_fsdp:
+ train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(
+ self.device
+ )
+ train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(
+ self.device
+ )
+ dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
+ dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
+ self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
+ self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
+
+ total_time = f"{(time.perf_counter() - time5)/accum_grad:0.3f}"
time5 = time.perf_counter()
+
speed_stats["optim_time"] = f"{time5 - time4:0.3f}"
speed_stats["total_time"] = total_time
@@ -464,11 +476,12 @@
batch_num_epoch = len(dataloader_train)
self.log(
epoch,
- batch_idx + kwargs.get("start_step", 0),
+ batch_idx,
+ log_step=batch_idx + kwargs.get("start_step", 0),
step_in_epoch=self.step_in_epoch,
batch_num_epoch=batch_num_epoch,
lr=lr,
- loss=loss.detach().cpu().item(),
+ loss=accum_grad * loss.detach().cpu().item(),
speed_stats=speed_stats,
stats=stats,
writer=writer,
@@ -503,14 +516,14 @@
)
time_beg = time.perf_counter()
- else:
- if self.use_ddp or self.use_fsdp:
- iterator_stop.fill_(1)
- dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
+ # else:
+ # if self.use_ddp or self.use_fsdp:
+ # iterator_stop.fill_(1)
+ # dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
if self.use_ddp or self.use_fsdp:
dist.barrier()
- iterator_stop = torch.tensor(0).to(self.device)
+ # iterator_stop = torch.tensor(0).to(self.device)
def validate_epoch(
self,
@@ -633,11 +646,12 @@
tag="train",
data_split_i=0,
data_split_num=1,
+ log_step=None,
**kwargs,
):
if (batch_idx + 1) % self.log_interval == 0:
-
+ batch_idx = log_step if log_step is not None else batch_idx
gpu_info = (
"GPU, memory: usage: {:.3f} GB, "
"peak: {:.3f} GB, "
@@ -659,9 +673,9 @@
f"data_slice: {data_split_i}/{data_split_num}, "
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}), "
- f"(acc_avg_epoch: {acc_avg_epoch:.3f}), "
+ f"(loss_avg_slice: {loss_avg_epoch:.3f}), "
+ f"(ppl_avg_slice: {math.exp(loss_avg_epoch):.3e}), "
+ f"(acc_avg_slice: {acc_avg_epoch:.3f}), "
f"(lr: {lr:.3e}), "
f"{[(k, round(v.detach().cpu().item(), 3)) for k, v in stats.items()]}, "
f"{speed_stats}, "
--
Gitblit v1.9.1