[misc] upgrade format to py39 (#7256)

src/llamafactory/model/model_utils/visual.py

@@ -15,8 +15,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections.abc import Sequence
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, Tuple
+from typing import TYPE_CHECKING, Optional
 
 import torch
 import transformers
@@ -40,9 +41,9 @@ transformers_logger = transformers.utils.logging.get_logger(__name__)
 class CompositeModel:
     model_type: str
     projector_key: str
-    vision_model_keys: List[str]
-    language_model_keys: List[str]
-    lora_conflict_keys: List[str]
+    vision_model_keys: list[str]
+    language_model_keys: list[str]
+    lora_conflict_keys: list[str]
 
     def get_projector(self, module: "torch.nn.Module") -> "torch.nn.Module":
         for key in self.projector_key.split("."):
@@ -51,15 +52,15 @@ class CompositeModel:
         return module
 
 
-COMPOSITE_MODELS: Dict[str, "CompositeModel"] = {}
+COMPOSITE_MODELS: dict[str, "CompositeModel"] = {}
 
 
 def _register_composite_model(
     model_type: str,
     projector_key: Optional[str] = None,
-    vision_model_keys: Optional[List[str]] = None,
-    language_model_keys: Optional[List[str]] = None,
-    lora_conflict_keys: Optional[List[str]] = None,
+    vision_model_keys: Optional[list[str]] = None,
+    language_model_keys: Optional[list[str]] = None,
+    lora_conflict_keys: Optional[list[str]] = None,
 ):
     COMPOSITE_MODELS[model_type] = CompositeModel(
         model_type=model_type,
@@ -116,12 +117,10 @@ class LlavaMultiModalProjectorForYiVLForVLLM(LlavaMultiModalProjectorForYiVL):
 
 
 def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArguments") -> None:
-    r"""
-    Casts projector output to half precision for fine-tuning quantized VLMs.
-    """
+    r"""Cast projector output to half precision for fine-tuning quantized VLMs."""
 
     def _mm_projector_forward_post_hook(
-        module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
+        module: "torch.nn.Module", args: tuple["torch.Tensor"], output: "torch.Tensor"
     ) -> "torch.Tensor":
         return output.to(model_args.compute_dtype)
 
@@ -137,9 +136,7 @@ def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArgumen
 
 
 def configure_visual_model(config: "PretrainedConfig") -> None:
-    r"""
-    Patches VLMs before loading them.
-    """
+    r"""Patch VLMs before loading them."""
     if getattr(config, "text_config", None) and not getattr(config, "hidden_size", None):
         # required for ds zero3 and valuehead models
         setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
@@ -149,10 +146,8 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
         transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL
 
 
-def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "FinetuningArguments") -> Set[str]:
-    r"""
-    Freezes vision tower and language model for VLM full/freeze tuning.
-    """
+def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "FinetuningArguments") -> set[str]:
+    r"""Freeze vision tower and language model for VLM full/freeze tuning."""
     model_type = getattr(config, "model_type", None)
     forbidden_modules = set()
     if model_type in COMPOSITE_MODELS:
@@ -175,9 +170,7 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
 
 
 def get_image_seqlen(config: "PretrainedConfig") -> int:
-    r"""
-    Computes the number of special tokens per image.
-    """
+    r"""Compute the number of special tokens per image."""
     model_type = getattr(config, "model_type", None)
     if model_type == "llava":
         image_seqlen = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
@@ -192,17 +185,13 @@ def get_image_seqlen(config: "PretrainedConfig") -> int:
 
 
 def get_patch_size(config: "PretrainedConfig", processor: "ProcessorMixin") -> int:
-    r"""
-    Computes the patch size of the vit.
-    """
+    r"""Compute the patch size of the vit."""
     patch_size = getattr(config.vision_config, "patch_size", getattr(processor, "patch_size", -1))
     return patch_size
 
 
 def get_vision_feature_select_strategy(config: "PretrainedConfig", processor: "ProcessorMixin") -> int:
-    r"""
-    Get the vision_feature_select_strategy.
-    """
+    r"""Get the vision_feature_select_strategy."""
     vision_feature_select_strategy = getattr(
         config, "vision_feature_select_strategy", getattr(processor, "vision_feature_select_strategy", "default")
     )
@@ -211,10 +200,8 @@ def get_vision_feature_select_strategy(config: "PretrainedConfig", processor: "P
 
 def patch_target_modules(
     model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
-) -> List[str]:
-    r"""
-    Freezes vision tower for VLM LoRA tuning.
-    """
+) -> list[str]:
+    r"""Freezes vision tower for VLM LoRA tuning."""
     model_type = getattr(model.config, "model_type", None)
     if model_type in COMPOSITE_MODELS:
         forbidden_modules = get_forbidden_modules(model.config, finetuning_args)