From 58b6154a73331a8807127d4579ed473432ce88de Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 20 二月 2024 17:02:44 +0800
Subject: [PATCH] update
---
funasr/train_utils/trainer.py | 75 ++++++++++++++++++++++++++++++-------
1 files changed, 61 insertions(+), 14 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 91b30b0..f99161a 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -69,6 +69,7 @@
self.device = next(model.parameters()).device
self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
self.kwargs = kwargs
+ self.log_interval = kwargs.get("log_interval", 50)
try:
@@ -147,9 +148,18 @@
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 +174,10 @@
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 +205,25 @@
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 +261,8 @@
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
@@ -241,24 +274,35 @@
speed_stats["total_time"] = total_time
- pbar.update(1)
- if self.local_rank == 0:
+
+ if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1:
+ pbar.update(self.log_interval)
+ 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"rank: {self.local_rank}, "
+ 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:
- self.writer.add_scalar('Loss/train', loss.item(),
+ self.writer.add_scalar(f'rank{self.local_rank}, Loss/train', loss.item(),
epoch*len(self.dataloader_train) + batch_idx)
for key, var in stats.items():
- self.writer.add_scalar(f'{key}/train', var.item(),
+ self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', var.item(),
epoch * len(self.dataloader_train) + batch_idx)
for key, var in speed_stats.items():
- self.writer.add_scalar(f'{key}/train', eval(var),
+ self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', eval(var),
epoch * len(self.dataloader_train) + batch_idx)
# if batch_idx == 2:
@@ -303,22 +347,25 @@
loss = loss
time4 = time.perf_counter()
- pbar.update(1)
- if self.local_rank == 0:
+
+ if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1:
+ pbar.update(self.log_interval)
description = (
- f"validation: \nEpoch: {epoch}/{self.max_epoch}, "
+ f"rank: {self.local_rank}, "
+ 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}), "
f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+ f"rank: {self.local_rank}"
)
pbar.set_description(description)
if self.writer:
- self.writer.add_scalar('Loss/val', loss.item(),
+ self.writer.add_scalar(f"rank{self.local_rank}, Loss/val", loss.item(),
epoch*len(self.dataloader_train) + batch_idx)
for key, var in stats.items():
- self.writer.add_scalar(f'{key}/val', var.item(),
+ self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', var.item(),
epoch * len(self.dataloader_train) + batch_idx)
for key, var in speed_stats.items():
- self.writer.add_scalar(f'{key}/val', eval(var),
+ self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', eval(var),
epoch * len(self.dataloader_train) + batch_idx)
\ No newline at end of file
--
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