From 6ce4909ecc39b990e253d692afc4b28451cde33e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 22 三月 2024 21:08:55 +0800
Subject: [PATCH] update
---
funasr/train_utils/trainer.py | 28 ++++++++++++++--------------
1 files changed, 14 insertions(+), 14 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 49b9fbc..3e83581 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -290,13 +290,13 @@
self.train_loss_avg = (self.train_loss_avg*batch_idx + loss.detach().cpu().item())/(batch_idx+1)
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
+ # 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
# Perform an optimizer step only after accumulating enough gradients
@@ -412,13 +412,13 @@
self.val_loss_avg = (self.val_loss_avg*batch_idx + loss.detach().cpu().item())/(batch_idx+1)
if "acc" in stats:
self.val_acc_avg = (self.val_acc_avg * batch_idx + stats["acc"].detach().cpu().item()) / (batch_idx + 1)
- if self.use_ddp or self.use_fsdp:
- val_loss_avg = torch.tensor(self.val_loss_avg, dtype=torch.float32).to(self.device)
- val_acc_avg = torch.tensor(self.val_acc_avg, dtype=torch.float32).to(self.device)
- dist.all_reduce(val_loss_avg, op=dist.ReduceOp.SUM)
- dist.all_reduce(val_acc_avg, op=dist.ReduceOp.SUM)
- self.val_loss_avg = val_loss_avg.detach().cpu().item() / self.world_size
- self.val_acc_avg = val_acc_avg.detach().cpu().item() / self.world_size
+ # if self.use_ddp or self.use_fsdp:
+ # val_loss_avg = torch.tensor(self.val_loss_avg, dtype=torch.float32).to(self.device)
+ # val_acc_avg = torch.tensor(self.val_acc_avg, dtype=torch.float32).to(self.device)
+ # dist.all_reduce(val_loss_avg, op=dist.ReduceOp.SUM)
+ # dist.all_reduce(val_acc_avg, op=dist.ReduceOp.SUM)
+ # self.val_loss_avg = val_loss_avg.detach().cpu().item() / self.world_size
+ # self.val_acc_avg = val_acc_avg.detach().cpu().item() / self.world_size
batch_num_epoch = -1
if hasattr(dataloader_val, "__len__"):
--
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