From eb92e79fb94e7b3df8f27c8ce3e607a70dff2a2e Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期三, 28 二月 2024 15:21:32 +0800
Subject: [PATCH] test
---
funasr/train_utils/trainer.py | 46 ++++++++++++++++++++++++++++++++--------------
1 files changed, 32 insertions(+), 14 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 10f7f80..c232642 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -3,6 +3,7 @@
import torch
import logging
from tqdm import tqdm
+from datetime import datetime
import torch.distributed as dist
from contextlib import nullcontext
# from torch.utils.tensorboard import SummaryWriter
@@ -107,14 +108,10 @@
filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
torch.save(state, filename)
- print(f'Checkpoint saved to {filename}')
+ print(f'\nCheckpoint saved to {filename}\n')
latest = Path(os.path.join(self.output_dir, f'model.pt'))
- try:
- latest.unlink()
- except:
- pass
+ torch.save(state, latest)
- latest.symlink_to(filename)
def _resume_checkpoint(self, resume_path):
"""
@@ -128,7 +125,20 @@
if os.path.isfile(ckpt):
checkpoint = torch.load(ckpt)
self.start_epoch = checkpoint['epoch'] + 1
- self.model.load_state_dict(checkpoint['state_dict'])
+ # self.model.load_state_dict(checkpoint['state_dict'])
+ src_state = checkpoint['state_dict']
+ dst_state = self.model.state_dict()
+ for k in dst_state.keys():
+ if not k.startswith("module.") and "module."+k in src_state.keys():
+ k_ddp = "module."+k
+ else:
+ k_ddp = k
+ if k_ddp in src_state.keys():
+ dst_state[k] = src_state[k_ddp]
+ else:
+ print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")
+
+ self.model.load_state_dict(dst_state)
self.optim.load_state_dict(checkpoint['optimizer'])
self.scheduler.load_state_dict(checkpoint['scheduler'])
print(f"Checkpoint loaded successfully from '{ckpt}'")
@@ -147,7 +157,7 @@
self._resume_checkpoint(self.output_dir)
for epoch in range(self.start_epoch, self.max_epoch + 1):
-
+ time1 = time.perf_counter()
self._train_epoch(epoch)
@@ -169,6 +179,9 @@
self.scheduler.step()
+ time2 = time.perf_counter()
+ time_escaped = (time2 - time1)/3600.0
+ print(f"\ntime_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n")
if self.rank == 0:
average_checkpoints(self.output_dir, self.avg_nbest_model)
@@ -188,7 +201,7 @@
epoch (int): The current epoch number.
"""
self.model.train()
- pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
+ pbar = tqdm(colour="blue", desc=f"rank: {self.local_rank}, Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
dynamic_ncols=True)
# Set the number of steps for gradient accumulation
@@ -273,12 +286,17 @@
torch.cuda.memory_reserved()/1024/1024/1024,
torch.cuda.max_memory_reserved()/1024/1024/1024,
)
+ lr = self.scheduler.get_last_lr()[0]
+ time_now = datetime.now()
+ time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
description = (
+ f"{time_now}, "
f"rank: {self.local_rank}, "
f"epoch: {epoch}/{self.max_epoch}, "
- f"step: {batch_idx}/{len(self.dataloader_train)}, total: {self.batch_total}, "
+ f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
- f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+ f"(lr: {lr:.3e}), "
+ f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
f"{speed_stats}, "
f"{gpu_info}"
)
@@ -307,7 +325,7 @@
"""
self.model.eval()
with torch.no_grad():
- pbar = tqdm(colour="red", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_val),
+ pbar = tqdm(colour="red", desc=f"rank: {self.local_rank}, Validation Epoch: {epoch + 1}", total=len(self.dataloader_val),
dynamic_ncols=True)
speed_stats = {}
time5 = time.perf_counter()
@@ -341,9 +359,9 @@
description = (
f"rank: {self.local_rank}, "
f"validation epoch: {epoch}/{self.max_epoch}, "
- f"step: {batch_idx}/{len(self.dataloader_val)}, "
+ f"step: {batch_idx+1}/{len(self.dataloader_val)}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
- f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+ f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
f"{speed_stats}, "
)
pbar.set_description(description)
--
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