From 45d7aa9004763684fb748ee17942ecba81042201 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 19 六月 2024 10:26:40 +0800
Subject: [PATCH] decoding
---
funasr/train_utils/trainer_ds.py | 61 ++++++++++++++++++++++++++----
1 files changed, 52 insertions(+), 9 deletions(-)
diff --git a/funasr/train_utils/trainer_ds.py b/funasr/train_utils/trainer_ds.py
index ec76531..85513a5 100644
--- a/funasr/train_utils/trainer_ds.py
+++ b/funasr/train_utils/trainer_ds.py
@@ -29,9 +29,10 @@
with torch.cuda.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False):
yield
else:
- if dtype == torch.float16:
- with autocast(enabled=True):
- yield
+ if dtype == torch.float16 or dtype == torch.bfloat16:
+ yield
+ # with autocast(enabled=True, dtype=dtype):
+ # yield
else:
yield
@@ -60,6 +61,7 @@
use_ddp: bool = False,
use_fsdp: bool = False,
use_fp16: bool = False,
+ use_bf16: bool = False,
use_deepspeed: bool = False,
output_dir: str = "./",
**kwargs,
@@ -78,7 +80,7 @@
output_dir (str): The directory where model checkpoints will be saved. Default is './'.
resume (str, optional): The file path to a checkpoint to resume training from.
"""
- self.rank = kwargs.get("rank", 0)
+ self.rank = rank
self.local_rank = local_rank
self.world_size = world_size
self.use_ddp = use_ddp
@@ -98,8 +100,11 @@
self.batch_total = 0
self.dtype = torch.float32
self.use_fp16 = use_fp16
+ self.use_bf16 = use_bf16
if self.use_fp16:
self.dtype = torch.float16
+ if self.use_bf16:
+ self.dtype = torch.bfloat16
self.save_checkpoint_interval = kwargs.get("save_checkpoint_interval", 5000)
self.validate_interval = kwargs.get("validate_interval", 5000)
self.keep_nbest_models = kwargs.get("keep_nbest_models", 500)
@@ -147,6 +152,16 @@
self.use_deepspeed = use_deepspeed
self.deepspeed_config = kwargs.get("deepspeed_config", "")
+ excludes = kwargs.get("excludes", None)
+ if excludes is not None:
+ if isinstance(excludes, str):
+ excludes = excludes.split(",")
+ self.excludes = excludes
+ effective_save_name_excludes = kwargs.get("effective_save_name_excludes", None)
+ if effective_save_name_excludes is not None:
+ if isinstance(effective_save_name_excludes, str):
+ effective_save_name_excludes = effective_save_name_excludes.split(",")
+ self.effective_save_name_excludes = effective_save_name_excludes
def save_checkpoint(
self,
@@ -277,11 +292,12 @@
elif self.use_fsdp:
pass
elif self.rank == 0:
- logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
+ logging.info(
+ f"Save checkpoint: {epoch}, rank: {self.rank}, local_rank: {self.local_rank}\n"
+ )
# self.step_or_epoch += 1
state = {
"epoch": epoch,
- "state_dict": model.state_dict(),
"optimizer": optim.state_dict(),
"scheduler": scheduler.state_dict(),
"saved_ckpts": self.saved_ckpts,
@@ -299,7 +315,24 @@
}
step = step_in_epoch
if hasattr(model, "module"):
- state["state_dict"] = model.module.state_dict()
+ state_dict = model.module.state_dict()
+ else:
+ state_dict = model.state_dict()
+
+ if self.effective_save_name_excludes is not None:
+ logging.info(f"effective_save_name_excludes: {self.effective_save_name_excludes}")
+ dst_state_dict = {}
+ for k in state_dict.keys():
+ for k_ex in self.effective_save_name_excludes:
+ k_tmp = k.replace("module.", "")
+ if k.startswith(k_ex):
+ logging.info(f"key: {k} matching: {k_ex}, not save it")
+ break
+ else:
+ dst_state_dict[k] = state_dict[k]
+ state["state_dict"] = dst_state_dict
+ else:
+ state["state_dict"] = state_dict
if scaler:
state["scaler_state"] = scaler.state_dict()
@@ -440,6 +473,16 @@
src_state = checkpoint["state_dict"]
dst_state = model.state_dict()
for k in dst_state.keys():
+ excludes_flag = False
+ if self.excludes is not None:
+ for k_ex in self.excludes:
+ k_tmp = k.replace("module.", "")
+ if k_tmp.startswith(k_ex):
+ logging.info(f"key: {k} matching: {k_ex}, excluded")
+ excludes_flag = True
+ break
+ if excludes_flag:
+ continue
if not k.startswith("module.") and "module." + k in src_state.keys():
k_ddp = "module." + k
elif k.startswith("module.") and "module." + k not in src_state.keys():
@@ -640,7 +683,7 @@
scaled_loss = model.backward(loss)
else:
loss = loss / self.accum_grad
- if self.use_fp16:
+ if self.use_fp16 or self.use_bf16:
scaler.scale(loss).backward()
else:
loss.backward()
@@ -668,7 +711,7 @@
# Execute an optimization step (update model parameters)
if self.use_ddp or self.use_fsdp:
dist.barrier()
- if self.use_fp16:
+ if self.use_fp16 or self.use_bf16:
scaler.step(optim)
scaler.update()
else:
--
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