From 54b6ff57647e28bbe88d8df81f2b112f127660e2 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 22 二月 2024 23:52:22 +0800
Subject: [PATCH] fp16

---
 funasr/bin/train.py |   25 +++++++++++++++----------
 1 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index d916509..4538224 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -44,14 +44,16 @@
 
 def main(**kwargs):
     print(kwargs)
+    
     # set random seed
-    tables.print()
     set_all_random_seed(kwargs.get("seed", 0))
     torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
     torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
     torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
     
     local_rank = int(os.environ.get('LOCAL_RANK', 0))
+    if local_rank == 0:
+        tables.print()
     # Check if we are using DDP or FSDP
     use_ddp = 'WORLD_SIZE' in os.environ and int(os.environ["WORLD_SIZE"]) > 1
     use_fsdp = kwargs.get("use_fsdp", None)
@@ -94,15 +96,18 @@
             init_param = (init_param,)
         logging.info("init_param is not None: %s", init_param)
         for p in init_param:
-            logging.info(f"Loading pretrained params from {p}")
-            load_pretrained_model(
-                model=model,
-                path=p,
-                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
-                oss_bucket=kwargs.get("oss_bucket", None),
-                scope_map=kwargs.get("scope_map", None),
-                excludes=kwargs.get("excludes", None),
-            )
+            if os.path.exists(p):
+                logging.info(f"Loading pretrained params from {p}")
+                load_pretrained_model(
+                    model=model,
+                    path=p,
+                    ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
+                    oss_bucket=kwargs.get("oss_bucket", None),
+                    scope_map=kwargs.get("scope_map", None),
+                    excludes=kwargs.get("excludes", None),
+                )
+            else:
+                logging.info(f"Checkpoint does not exist, init randomly: {p}")
     else:
         initialize(model, kwargs.get("init", "kaiming_normal"))
 

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