From e299cfecaf979833d9c4d7c70e44cb92ea066afe Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 09 五月 2024 20:02:37 +0800
Subject: [PATCH] total_time/accum_grad
---
funasr/train_utils/trainer.py | 83 +++++++++++++++++++++++++----------------
1 files changed, 50 insertions(+), 33 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index e86420c..28fbb29 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -169,6 +169,8 @@
"data_split_i": kwargs.get("data_split_i", 0),
"data_split_num": kwargs.get("data_split_num", 1),
"batch_total": self.batch_total,
+ "train_loss_avg": kwargs.get("train_loss_avg", 0),
+ "train_acc_avg": kwargs.get("train_acc_avg", 0),
}
step = step_in_epoch
if hasattr(model, "module"):
@@ -306,7 +308,13 @@
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
+ )
+ self.train_loss_avg = (
+ checkpoint["train_loss_avg"] if "train_loss_avg" in checkpoint else 0
+ )
model.to(self.device)
print(f"Checkpoint loaded successfully from '{ckpt}'")
else:
@@ -374,49 +382,41 @@
):
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:
- # Apply weighted averaging for loss and stats
- loss = (loss * weight.type(loss.dtype)).sum()
- # if distributed, this method can also apply all_reduce()
- # stats, weight = recursive_average(stats, weight, distributed=True)
- if self.use_ddp or self.use_fsdp:
- dist.all_reduce(weight, op=dist.ReduceOp.SUM)
- # Now weight is summation over all workers
- loss /= weight.sum() # shape:[1] -> shape:[]
- # Multiply world_size because DistributedDataParallel
- # automatically normalizes the gradient by world_size.
- loss *= self.world_size
+ # if self.use_ddp or self.use_fsdp:
+ # # Apply weighted averaging for loss and stats
+ # loss = (loss * weight.type(loss.dtype)).sum()
+ # # if distributed, this method can also apply all_reduce()
+ # # stats, weight = recursive_average(stats, weight, distributed=True)
+ # if self.use_ddp or self.use_fsdp:
+ # dist.all_reduce(weight, op=dist.ReduceOp.SUM)
+ # # Now weight is summation over all workers
+ # loss /= weight.sum() # shape:[1] -> shape:[]
+ # # 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 * batch_idx + loss.detach().cpu().item()
- ) / (batch_idx + 1)
+ self.train_loss_avg * (self.step_in_epoch - 1) + loss.detach().cpu().item()
+ ) / self.step_in_epoch
if "acc" in stats:
self.train_acc_avg = (
- self.train_acc_avg * batch_idx + stats["acc"].detach().cpu().item()
- ) / (batch_idx + 1)
- 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
+ self.train_acc_avg * (self.step_in_epoch - 1)
+ + stats["acc"].detach().cpu().item()
+ ) / self.step_in_epoch
# Perform an optimizer step only after accumulating enough gradients
if (batch_idx + 1) % accum_grad == 0:
@@ -445,8 +445,21 @@
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}"
+ total_time = f"{(time.perf_counter() - time5)/accum_grad:0.3f}"
time5 = time.perf_counter()
+
+ 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
+
speed_stats["optim_time"] = f"{time5 - time4:0.3f}"
speed_stats["total_time"] = total_time
@@ -457,6 +470,7 @@
self.log(
epoch,
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,
@@ -490,6 +504,8 @@
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),
+ train_loss_avg=self.train_loss_avg,
+ train_acc_avg=self.train_acc_avg,
)
time_beg = time.perf_counter()
@@ -623,11 +639,12 @@
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, "
--
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