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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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20
vllm/model_executor/models/aria.py
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@@ -1,6 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
from collections.abc import Iterable, Mapping, Sequence
from typing import List, Optional, Set, Tuple, TypedDict, Union
from typing import Optional, TypedDict, Union
import torch
import torch.nn as nn
@@ -66,8 +66,8 @@ class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
# Identity layer
self.post_layernorm = nn.Identity()
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
@@ -75,7 +75,7 @@ class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
("qkv_proj", "v_proj", "v"),
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
loaded_params: set[str] = set()
for name, loaded_weight in weights:
# NOTE: post_layernorm is not used in Aria
@@ -326,8 +326,8 @@ class AriaTextModel(LlamaModel, SupportsQuant):
# Adapted from LlamaModel.load_weights with the modification of adding
# the expert weights mapping to `stacked_params_mapping`
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
(".qkv_proj", ".q_proj", "q"),
@@ -339,7 +339,7 @@ class AriaTextModel(LlamaModel, SupportsQuant):
("experts.w2_weight", "experts.fc2.weight", 'w2'),
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
@@ -528,7 +528,7 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
self.vocab_size, logit_scale)
def _validate_image_sizes(
self, images: List[torch.Tensor]) -> List[torch.Tensor]:
self, images: list[torch.Tensor]) -> list[torch.Tensor]:
if not all(img.shape == images[0].shape for img in images):
raise ValueError("All images must be the same size")
return images
@@ -578,7 +578,7 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
def _process_image_input(
self, image_input: AriaImagePixelInputs
) -> Tuple[torch.Tensor, torch.Tensor]:
) -> tuple[torch.Tensor, torch.Tensor]:
assert self.vision_tower is not None
pixel_values = image_input['pixel_values']
@@ -651,6 +651,6 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
sampling_metadata)
return logits
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
loader = AutoWeightsLoader(self)
loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)