From d43d0853dcf3a1db04302c7b527e92ace3ccfb55 Mon Sep 17 00:00:00 2001
From: AldarisX <aldaris@axnet.icu>
Date: 星期一, 07 四月 2025 21:20:31 +0800
Subject: [PATCH] add intel xpu support (#2468)
---
funasr/train_utils/average_nbest_models.py | 29 ++++++++++++++++++++++-------
1 files changed, 22 insertions(+), 7 deletions(-)
diff --git a/funasr/train_utils/average_nbest_models.py b/funasr/train_utils/average_nbest_models.py
index 0f08804..873f419 100644
--- a/funasr/train_utils/average_nbest_models.py
+++ b/funasr/train_utils/average_nbest_models.py
@@ -16,20 +16,33 @@
from functools import cmp_to_key
-def _get_checkpoint_paths(output_dir: str, last_n: int = 5):
+def _get_checkpoint_paths(output_dir: str, last_n: int = 5, use_deepspeed=False, **kwargs):
"""
Get the paths of the last 'last_n' checkpoints by parsing filenames
in the output directory.
"""
try:
- checkpoint = torch.load(os.path.join(output_dir, "model.pt"), map_location="cpu")
+ if not use_deepspeed:
+ checkpoint = torch.load(os.path.join(output_dir, "model.pt"), map_location="cpu")
+ else:
+ checkpoint = torch.load(
+ os.path.join(output_dir, "model.pt", "mp_rank_00_model_states.pt"),
+ map_location="cpu",
+ )
avg_keep_nbest_models_type = checkpoint["avg_keep_nbest_models_type"]
- val_step_or_eoch = checkpoint[f"val_{avg_keep_nbest_models_type}_step_or_eoch"]
- sorted_items = sorted(val_step_or_eoch.items(), key=lambda x: x[1], reverse=True)
+ val_step_or_epoch = checkpoint[f"val_{avg_keep_nbest_models_type}_step_or_epoch"]
+ sorted_items = sorted(val_step_or_epoch.items(), key=lambda x: x[1], reverse=True)
sorted_items = (
sorted_items[:last_n] if avg_keep_nbest_models_type == "acc" else sorted_items[-last_n:]
)
- checkpoint_paths = [os.path.join(output_dir, key) for key, value in sorted_items[:last_n]]
+ checkpoint_paths = []
+ for key, value in sorted_items[:last_n]:
+ if not use_deepspeed:
+ ckpt = os.path.join(output_dir, key)
+ else:
+ ckpt = os.path.join(output_dir, key, "mp_rank_00_model_states.pt")
+ checkpoint_paths.append(ckpt)
+
except:
print(f"{checkpoint} does not exist, avg the lastet checkpoint.")
# List all files in the output directory
@@ -40,6 +53,7 @@
checkpoint_files.sort(key=lambda x: int(re.search(r"(\d+)", x).group()), reverse=True)
# Get the last 'last_n' checkpoint paths
checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]]
+
return checkpoint_paths
@@ -49,7 +63,7 @@
Average the last 'last_n' checkpoints' model state_dicts.
If a tensor is of type torch.int, perform sum instead of average.
"""
- checkpoint_paths = _get_checkpoint_paths(output_dir, last_n)
+ checkpoint_paths = _get_checkpoint_paths(output_dir, last_n, **kwargs)
print(f"average_checkpoints: {checkpoint_paths}")
state_dicts = []
@@ -62,7 +76,8 @@
# Check if we have any state_dicts to average
if len(state_dicts) < 1:
- raise RuntimeError("No checkpoints found for averaging.")
+ print("No checkpoints found for averaging.")
+ return
# Average or sum weights
avg_state_dict = OrderedDict()
--
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