fix: skip setting seed when descriptor/fitting_net doesn't exist (#1584)

Support some corner cases.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Bug Fixes**
- Improved handling of model descriptor and fitting network keys to
ensure robust seed setting in training functions.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

dpgen/generator/run.py

@@ -583,7 +583,9 @@ def make_train_dp(iter_index, jdata, mdata):
  mdata["deepmd_version"]
  ) < Version("3"):
  # 1.x
- if jinput["model"]["descriptor"]["type"] == "hybrid":
+ if "descriptor" not in jinput["model"]:
+ pass
+ elif jinput["model"]["descriptor"]["type"] == "hybrid":
  for desc in jinput["model"]["descriptor"]["list"]:
  desc["seed"] = random.randrange(sys.maxsize) % (2**32)
  elif jinput["model"]["descriptor"]["type"] == "loc_frame":
@@ -592,9 +594,10 @@ def make_train_dp(iter_index, jdata, mdata):
  jinput["model"]["descriptor"]["seed"] = random.randrange(
  sys.maxsize
  ) % (2**32)
- jinput["model"]["fitting_net"]["seed"] = random.randrange(sys.maxsize) % (
- 2**32
- )
+ if "fitting_net" in jinput["model"]:
+ jinput["model"]["fitting_net"]["seed"] = random.randrange(
+ sys.maxsize
+ ) % (2**32)
  if "type_embedding" in jinput["model"]:
  jinput["model"]["type_embedding"]["seed"] = random.randrange(
  sys.maxsize