From a0f03bd2a87d97d47a1636bbe6f0855a43160331 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 15 五月 2024 19:48:50 +0800
Subject: [PATCH] Dev gzf deepspeed (#1732)
---
funasr/train_utils/trainer.py | 50 ++++++++++++++++++++++++++++----------------------
1 files changed, 28 insertions(+), 22 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 01e2924..50f99f0 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -382,8 +382,6 @@
):
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:
@@ -398,34 +396,28 @@
# 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:
@@ -454,8 +446,22 @@
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
@@ -662,9 +668,9 @@
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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