From 1d1ef01b4e23630a99a3be7e9d1dce9550a793e9 Mon Sep 17 00:00:00 2001
From: yhliang <68215459+yhliang-aslp@users.noreply.github.com>
Date: 星期四, 11 五月 2023 16:26:24 +0800
Subject: [PATCH] Merge branch 'main' into dev_smohan
---
funasr/train/trainer.py | 14 ++++++++++++++
1 files changed, 14 insertions(+), 0 deletions(-)
diff --git a/funasr/train/trainer.py b/funasr/train/trainer.py
index 7c187e9..a40f031 100644
--- a/funasr/train/trainer.py
+++ b/funasr/train/trainer.py
@@ -95,6 +95,7 @@
use_pai: bool
oss_bucket: Union[oss2.Bucket, None]
batch_interval: int
+ bias_grad_times: float
class Trainer:
"""Trainer having a optimizer.
@@ -546,8 +547,11 @@
no_forward_run = options.no_forward_run
ngpu = options.ngpu
use_wandb = options.use_wandb
+ bias_grad_times = options.bias_grad_times
distributed = distributed_option.distributed
+ if bias_grad_times != 1.0:
+ logging.warning("Using bias_grad_times: {} for gradient scaling".format(bias_grad_times))
if log_interval is None:
try:
log_interval = max(len(iterator) // 20, 10)
@@ -690,6 +694,16 @@
scale_factor=0.55,
)
+ # for contextual training
+ if bias_grad_times != 1.0:
+ # contextual related parameter names
+ cr_pnames = ["bias_encoder", "bias_embed", "decoder.bias_decoder", "decoder.bias_output"]
+ for name, param in model.named_parameters():
+ for cr_pname in cr_pnames:
+ if cr_pname in name:
+ param.grad *= bias_grad_times
+ continue
+
# compute the gradient norm to check if it is normal or not
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(),
--
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