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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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22
vllm/model_executor/models/whisper.py
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@@ -2,7 +2,7 @@
import math
from collections.abc import Iterable, Mapping, Sequence
from typing import List, Optional, Set, Tuple, TypedDict, Union
from typing import Optional, TypedDict, Union
import torch
from torch import nn
@@ -382,7 +382,7 @@ class WhisperEncoder(nn.Module):
self.embed_positions.weight.copy_(
sinusoids(*self.embed_positions.weight.shape))
def forward(self, input_features: Union[torch.Tensor, List[torch.Tensor]]):
def forward(self, input_features: Union[torch.Tensor, list[torch.Tensor]]):
hidden_states = []
for features in input_features:
embeds = nn.functional.gelu(self.conv1(features))
@@ -460,7 +460,7 @@ class WhisperModel(nn.Module):
def forward(
self,
input_features: Optional[Union[torch.Tensor, List[torch.Tensor]]],
input_features: Optional[Union[torch.Tensor, list[torch.Tensor]]],
input_ids: Optional[torch.Tensor],
positions: torch.Tensor,
) -> torch.Tensor:
@@ -474,14 +474,14 @@ class WhisperModel(nn.Module):
def get_encoder_outputs(
self,
input_features: Optional[Union[torch.Tensor, List[torch.Tensor]]],
input_features: Optional[Union[torch.Tensor, list[torch.Tensor]]],
) -> Optional[torch.Tensor]:
if input_features is None:
return None
return self.encoder(input_features)
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)
(".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
@@ -491,7 +491,7 @@ class WhisperModel(nn.Module):
(".encoder_attn.kv_proj", ".encoder_attn.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:
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
@@ -722,8 +722,8 @@ class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
sampling_metadata)
return logits
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(self, skip_prefixes=["proj_out."])
# add fake zeros bias for k_proj to state_dict
@@ -732,8 +732,8 @@ class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
def _create_fake_bias_for_k_proj(
weights: Iterable[Tuple[str, torch.Tensor]]
) -> Iterable[Tuple[str, torch.Tensor]]:
weights: Iterable[tuple[str, torch.Tensor]]
) -> Iterable[tuple[str, torch.Tensor]]:
"""
Create full zeros bias for k_proj weight in self-attn and x-attn layers.
So that the bias for k_proj in qkv_proj can be initialized with zeros.