biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Update deprecated type hinting in models (#18132)

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

15
vllm/model_executor/models/stablelm.py
View File

@@ -20,7 +20,8 @@
# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM)
model compatible with HuggingFace weights."""
from typing import Iterable, Optional, Set, Tuple, Union
from collections.abc import Iterable
from typing import Optional, Union
import torch
from torch import nn
@@ -180,7 +181,7 @@ class StablelmDecoderLayer(nn.Module):
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
) -> tuple[torch.Tensor, torch.Tensor]:
# Self Attention
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
@@ -252,8 +253,8 @@ class StableLMEpochModel(nn.Module):
hidden_states = self.norm(hidden_states)
return hidden_states
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"),
@@ -263,7 +264,7 @@ class StableLMEpochModel(nn.Module):
("gate_up_proj", "up_proj", 1),
]
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:
@@ -335,8 +336,8 @@ class StablelmForCausalLM(nn.Module, SupportsPP):
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,
# Models trained using ColossalAI may include these tensors in