[misc] upgrade format to py39 (#7256)

src/llamafactory/train/test_utils.py

@@ -12,7 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import TYPE_CHECKING, Dict, Optional, Sequence, Set, Tuple, Union
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Optional, Union
 
 import torch
 from peft import PeftModel
@@ -43,7 +44,7 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_k
             assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
 
 
-def check_lora_model(model: "LoraModel") -> Tuple[Set[str], Set[str]]:
+def check_lora_model(model: "LoraModel") -> tuple[set[str], set[str]]:
     linear_modules, extra_modules = set(), set()
     for name, param in model.named_parameters():
         if any(module in name for module in ["lora_A", "lora_B"]):
@@ -83,7 +84,7 @@ def load_reference_model(
 ) -> Union["PreTrainedModel", "LoraModel"]:
     current_device = get_current_device()
     if add_valuehead:
-        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
+        model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
             model_path, torch_dtype=torch.float16, device_map=current_device
         )
         if not is_trainable:
@@ -111,7 +112,7 @@ def load_dataset_module(**kwargs) -> "DatasetModule":
 
 
 def patch_valuehead_model() -> None:
-    def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]) -> None:
+    def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: dict[str, "torch.Tensor"]) -> None:
         state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
         self.v_head.load_state_dict(state_dict, strict=False)
         del state_dict