[Misc] Improve type annotations for jsontree (#25577)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/aya_vision.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Adapted from https://github.com/huggingface/transformers/tree/main/src/transformers/models/aya_vision
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, Optional, Union, cast
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 from torch import nn
@@ -347,12 +347,16 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
-    def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
-                                  pixel_values: torch.Tensor,
-                                  **kwargs) -> torch.Tensor:
-        target_dtype = vision_tower.get_input_embeddings().weight.dtype
-        image_features = vision_tower(pixel_values.to(dtype=target_dtype),
-                                      **kwargs)
+    def _image_pixels_to_features(
+        self,
+        vision_tower: SiglipVisionModel,
+        pixel_values: torch.Tensor,
+        **kwargs,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
+        target_dtype: torch.dtype = \
+            vision_tower.get_input_embeddings().weight.dtype
+        image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
+            vision_tower(pixel_values.to(dtype=target_dtype), **kwargs)
 
         def select_features(leaf: torch.Tensor):
             return self._select_image_features(
@@ -360,10 +364,7 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
                 strategy=self.config.vision_feature_select_strategy,
             )
 
-        return cast(
-            Union[torch.Tensor, tuple[torch.Tensor, ...]],
-            json_map_leaves(select_features, image_features),
-        )
+        return json_map_leaves(select_features, image_features)
 
     def _select_image_features(self, image_features: torch.Tensor, *,
                                strategy: str) -> torch.Tensor:

vllm/model_executor/models/llava.py

@@ -4,7 +4,7 @@
 from abc import abstractmethod
 from collections.abc import Iterable, Mapping, Sequence
 from typing import (Annotated, Final, Literal, Optional, Protocol, TypeVar,
-                    Union, cast)
+                    Union)
 
 import torch
 import torch.nn as nn
@@ -623,7 +623,8 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = vision_tower(pixel_values)
+        image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
+            vision_tower(pixel_values)
 
         def select_features(leaf: torch.Tensor):
             return self._select_image_features(
@@ -631,10 +632,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
                 strategy=self.config.vision_feature_select_strategy,
             )
 
-        return cast(
-            Union[torch.Tensor, tuple[torch.Tensor, ...]],
-            json_map_leaves(select_features, image_features),
-        )
+        return json_map_leaves(select_features, image_features)
 
     def _process_image_pixels(
         self,

vllm/model_executor/models/minimax_vl_01.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable, Mapping
-from typing import Annotated, Literal, Optional, Union, cast
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 import torch.nn as nn
@@ -254,7 +254,8 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = tuple(vision_tower(p) for p in pixel_values)
+        image_features: tuple[torch.Tensor, ...] = \
+            tuple(vision_tower(p) for p in pixel_values)
 
         def select_features(leaf: torch.Tensor):
             return self._select_image_features(
@@ -262,10 +263,7 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
                 strategy=self.config.vision_feature_select_strategy,
             )
 
-        return cast(
-            Union[torch.Tensor, tuple[torch.Tensor, ...]],
-            json_map_leaves(select_features, image_features),
-        )
+        return json_map_leaves(select_features, image_features)
 
     # adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631
     def pack_image_features(self, image_features: list[torch.Tensor],

vllm/model_executor/models/tarsier.py

@@ -4,7 +4,7 @@
 import math
 from collections.abc import Iterable, Mapping, Sequence
 from typing import (Annotated, Final, Literal, Optional, Protocol, TypeVar,
-                    Union, cast)
+                    Union)
 
 import torch
 import torch.nn as nn
@@ -490,11 +490,8 @@ class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal,
         pixel_values: Union[torch.Tensor, list[torch.Tensor]],
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # From vLLM LLaVA, vision tower output handling
-        image_hidden_states = vision_tower(pixel_values)
-        if not isinstance(image_hidden_states, torch.Tensor):
-            raise TypeError(
-                f"image_hidden_states type: {type(image_hidden_states)}"
-                " is not supported")
+        image_hidden_states: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
+            vision_tower(pixel_values)
 
         def select_features_fn(leaf: torch.Tensor):
             return self._select_image_features(
@@ -502,11 +499,7 @@ class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal,
                 strategy=self.config.vision_feature_select_strategy,
             )
 
-        selected_features = cast(
-            Union[torch.Tensor, tuple[torch.Tensor, ...]],
-            json_map_leaves(select_features_fn, image_hidden_states),
-        )
-        return selected_features
+        return json_map_leaves(select_features_fn, image_hidden_states)
 
     def _add_tarsier_split_tokens(
             self, projected_image_features: torch.Tensor) -> torch.Tensor:

vllm/utils/jsontree.py

@@ -4,7 +4,7 @@
 
 from collections.abc import Iterable
 from functools import reduce
-from typing import Callable, TypeVar, Union, overload
+from typing import Callable, TypeVar, Union, cast, overload
 
 _T = TypeVar("_T")
 _U = TypeVar("_U")
@@ -30,10 +30,42 @@ def json_iter_leaves(value: JSONTree[_T]) -> Iterable[_T]:
         yield value
 
 
+@overload
+def json_map_leaves(
+    func: Callable[[_T], _U],
+    value: Union[_T, dict[str, _T]],
+) -> Union[_U, dict[str, _U]]:
+    ...
+
+
+@overload
+def json_map_leaves(
+    func: Callable[[_T], _U],
+    value: Union[_T, list[_T]],
+) -> Union[_U, list[_U]]:
+    ...
+
+
+@overload
+def json_map_leaves(
+    func: Callable[[_T], _U],
+    value: Union[_T, tuple[_T, ...]],
+) -> Union[_U, tuple[_U, ...]]:
+    ...
+
+
+@overload
 def json_map_leaves(
     func: Callable[[_T], _U],
     value: JSONTree[_T],
 ) -> JSONTree[_U]:
+    ...
+
+
+def json_map_leaves(
+    func: Callable[[_T], _U],
+    value: Union[dict[str, _T], list[_T], tuple[_T, ...], JSONTree[_T]],
+) -> Union[dict[str, _U], list[_U], tuple[_U, ...], JSONTree[_U]]:
     """Apply a function to each leaf in a nested JSON structure."""
     if isinstance(value, dict):
         return {k: json_map_leaves(func, v) for k, v in value.items()}
@@ -45,6 +77,33 @@ def json_map_leaves(
         return func(value)
 
 
+@overload
+def json_reduce_leaves(
+    func: Callable[[_T, _T], _T],
+    value: Union[_T, dict[str, _T]],
+    /,
+) -> _T:
+    ...
+
+
+@overload
+def json_reduce_leaves(
+    func: Callable[[_T, _T], _T],
+    value: Union[_T, list[_T]],
+    /,
+) -> _T:
+    ...
+
+
+@overload
+def json_reduce_leaves(
+    func: Callable[[_T, _T], _T],
+    value: Union[_T, tuple[_T, ...]],
+    /,
+) -> _T:
+    ...
+
+
 @overload
 def json_reduce_leaves(
     func: Callable[[_T, _T], _T],
@@ -65,10 +124,10 @@ def json_reduce_leaves(
 
 
 def json_reduce_leaves(
-    func: Callable[..., Union[_T, _U]],
-    value: JSONTree[_T],
-    initial: _U = ...,  # type: ignore[assignment]
-    /,
+        func: Callable[..., Union[_T, _U]],
+        value: Union[dict[str, _T], list[_T], tuple[_T, ...], JSONTree[_T]],
+        initial: _U = cast(_U, ...),  # noqa: B008
+        /,
 ) -> Union[_T, _U]:
     """
     Apply a function of two arguments cumulatively to each leaf in a