From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/train_utils/trainer.py | 49 +++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 45 insertions(+), 4 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 91b30b0..3cd61a1 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -147,9 +147,18 @@
for epoch in range(self.start_epoch, self.max_epoch + 1):
self._train_epoch(epoch)
+
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
self._validate_epoch(epoch)
-
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
+
if self.rank == 0:
self._save_checkpoint(epoch)
@@ -164,7 +173,10 @@
if self.use_ddp or self.use_fsdp:
dist.barrier()
- self.writer.close()
+
+
+ if self.writer:
+ self.writer.close()
def _train_epoch(self, epoch):
@@ -192,7 +204,25 @@
my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
with my_context():
time2 = time.perf_counter()
+ # print("before, GPU, memory: {:.3f} GB, "
+ # "{:.3f} GB, "
+ # "{:.3f} GB, "
+ # "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
+ # torch.cuda.max_memory_allocated()/1024/1024/1024,
+ # torch.cuda.memory_reserved()/1024/1024/1024,
+ # torch.cuda.max_memory_reserved()/1024/1024/1024,
+ # ))
+
retval = self.model(**batch)
+ torch.cuda.empty_cache()
+ # print("after, GPU, memory: {:.3f} GB, "
+ # "{:.3f} GB, "
+ # "{:.3f} GB, "
+ # "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
+ # torch.cuda.max_memory_allocated()/1024/1024/1024,
+ # torch.cuda.memory_reserved()/1024/1024/1024,
+ # torch.cuda.max_memory_reserved()/1024/1024/1024,
+ # ))
time3 = time.perf_counter()
speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
loss, stats, weight = retval
@@ -230,6 +260,8 @@
continue
# Execute an optimization step (update model parameters)
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
self.optim.step()
self.scheduler.step()
# Clear gradients for the next accumulation stage
@@ -243,12 +275,21 @@
pbar.update(1)
if self.local_rank == 0:
+ gpu_info = "GPU, memory: {:.3f} GB, " \
+ "{:.3f} GB, "\
+ "{:.3f} GB, "\
+ "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
+ torch.cuda.max_memory_allocated()/1024/1024/1024,
+ torch.cuda.memory_reserved()/1024/1024/1024,
+ torch.cuda.max_memory_reserved()/1024/1024/1024,
+ )
description = (
- f"Epoch: {epoch}/{self.max_epoch}, "
+ f"Train epoch: {epoch}/{self.max_epoch}, "
f"step {batch_idx}/{len(self.dataloader_train)}, "
f"{speed_stats}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+ f"{gpu_info}"
)
pbar.set_description(description)
if self.writer:
@@ -306,7 +347,7 @@
pbar.update(1)
if self.local_rank == 0:
description = (
- f"validation: \nEpoch: {epoch}/{self.max_epoch}, "
+ f"validation epoch: {epoch}/{self.max_epoch}, "
f"step {batch_idx}/{len(self.dataloader_train)}, "
f"{speed_stats}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
--
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