From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords
---
funasr/train_utils/trainer.py | 50 ++++++++++++++++++++++++++++++--------------------
1 files changed, 30 insertions(+), 20 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index d46a21c..afc632d 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)
@@ -396,6 +401,7 @@
# 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
@@ -409,24 +415,14 @@
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:
@@ -455,8 +451,22 @@
scheduler.step()
# Clear gradients for the next accumulation stage
optim.zero_grad(set_to_none=True)
+
+ 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
@@ -471,7 +481,7 @@
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,
@@ -663,9 +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}, "
--
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