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Update deprecated type hinting in models (#18132)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-05-15 06:06:50 +01:00
committed by GitHub
parent 83f74c698f
commit 26d0419309
130 changed files with 971 additions and 901 deletions
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12
vllm/model_executor/models/phi4mm.py
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@@ -1,7 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
import math
from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Dict, List, Literal, Optional, Tuple, TypedDict, Union
from typing import Any, Literal, Optional, TypedDict, Union
import numpy as np
import torch
@@ -392,7 +392,7 @@ class Phi4MMImageEncoder(nn.Module):
class Phi4MMImagePixelInputs(TypedDict):
type: Literal["pixel_values"]
data: Union[torch.Tensor, List[torch.Tensor]]
data: Union[torch.Tensor, list[torch.Tensor]]
"""
Shape:
`(batch_size * num_images, 1 + num_patches, num_channels, height, width)`
@@ -417,7 +417,7 @@ class Phi4MMImagePixelInputs(TypedDict):
class Phi4MMImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
data: Union[torch.Tensor, List[torch.Tensor]]
data: Union[torch.Tensor, list[torch.Tensor]]
"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
`hidden_size` must match the hidden size of language model backbone.
@@ -426,7 +426,7 @@ class Phi4MMImageEmbeddingInputs(TypedDict):
class Phi4MMAudioFeatureInputs(TypedDict):
type: Literal["audio_features"]
data: Union[torch.Tensor, List[torch.Tensor]]
data: Union[torch.Tensor, list[torch.Tensor]]
"""Shape: `(batch_size * num_audios, 80, M)"""
@@ -1031,7 +1031,7 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
return audio_embeds
def _parse_and_validate_image_input(self,
**kwargs: object) -> Optional[Dict]:
**kwargs: object) -> Optional[dict]:
input_image_embeds: NestedTensors = kwargs.get("input_image_embeds")
if input_image_embeds is None:
return None
@@ -1238,7 +1238,7 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
sampling_metadata)
return logits
def load_weights(self, weights: Iterable[Tuple[str,
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> None:
weights = ((name, data) for name, data in weights
if "lora" not in name)