From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/bin/train_ds.py | 29 ++++++++++++++++++++++-------
1 files changed, 22 insertions(+), 7 deletions(-)
diff --git a/funasr/bin/train_ds.py b/funasr/bin/train_ds.py
index 032a0cf..415904e 100644
--- a/funasr/bin/train_ds.py
+++ b/funasr/bin/train_ds.py
@@ -27,7 +27,7 @@
from funasr.train_utils.trainer_ds import Trainer
from funasr.schedulers import scheduler_classes
from funasr.train_utils.initialize import initialize
-from funasr.download.download_from_hub import download_model
+from funasr.download.download_model_from_hub import download_model
from funasr.models.lora.utils import mark_only_lora_as_trainable
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
@@ -84,6 +84,8 @@
dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method="env://")
torch.cuda.set_device(local_rank)
+ # rank = dist.get_rank()
+
logging.info("Build model, frontend, tokenizer")
device = kwargs.get("device", "cuda")
kwargs["device"] = "cpu"
@@ -124,16 +126,17 @@
use_ddp=use_ddp,
use_fsdp=use_fsdp,
device=kwargs["device"],
+ excludes=kwargs.get("excludes", None),
output_dir=kwargs.get("output_dir", "./exp"),
**kwargs.get("train_conf"),
)
model = trainer.warp_model(model)
- kwargs["device"] = next(model.parameters()).device
- trainer.device = kwargs["device"]
+ kwargs["device"] = int(os.environ.get("LOCAL_RANK", 0))
+ trainer.device = int(os.environ.get("LOCAL_RANK", 0))
- model, optim, scheduler = trainer.warp_optim_scheduler(model, kwargs)
+ model, optim, scheduler = trainer.warp_optim_scheduler(model, **kwargs)
# dataset
logging.info("Build dataloader")
@@ -143,7 +146,7 @@
dataloader = dataloader_class(**kwargs)
# dataloader_tr, dataloader_val = dataloader_class(**kwargs)
- scaler = GradScaler(enabled=trainer.use_fp16) if trainer.use_fp16 else None
+ scaler = GradScaler(enabled=True) if trainer.use_fp16 or trainer.use_bf16 else None
scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler
trainer.resume_checkpoint(
@@ -158,6 +161,8 @@
time1 = time.perf_counter()
for data_split_i in range(trainer.start_data_split_i, dataloader.data_split_num):
+ time_slice_i = time.perf_counter()
+
dataloader_tr, dataloader_val = dataloader.build_iter(
epoch, data_split_i=data_split_i, start_step=trainer.start_step
)
@@ -178,6 +183,14 @@
torch.cuda.empty_cache()
+ time_escaped = (time.perf_counter() - time_slice_i) / 3600.0
+ logging.info(
+ f"\n\nrank: {local_rank}, "
+ f"time_escaped_epoch: {time_escaped:.3f} hours, "
+ f"estimated to finish {dataloader.data_split_num} data_slices, remaining: {dataloader.data_split_num-data_split_i} slices, {(dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours, "
+ f"epoch: {trainer.max_epoch - epoch} epochs, {((trainer.max_epoch - epoch - 1)*dataloader.data_split_num + dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours\n"
+ )
+
trainer.start_data_split_i = 0
trainer.validate_epoch(model=model, dataloader_val=dataloader_val, epoch=epoch + 1)
scheduler.step()
@@ -189,7 +202,7 @@
time2 = time.perf_counter()
time_escaped = (time2 - time1) / 3600.0
logging.info(
- f"rank: {local_rank}, "
+ f"\n\nrank: {local_rank}, "
f"time_escaped_epoch: {time_escaped:.3f} hours, "
f"estimated to finish {trainer.max_epoch} "
f"epoch: {(trainer.max_epoch - epoch) * time_escaped:.3f} hours\n"
@@ -198,7 +211,9 @@
trainer.train_loss_avg = 0.0
if trainer.rank == 0:
- average_checkpoints(trainer.output_dir, trainer.avg_nbest_model)
+ average_checkpoints(
+ trainer.output_dir, trainer.avg_nbest_model, use_deepspeed=trainer.use_deepspeed
+ )
trainer.close()
--
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