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Update Optional[x] -> x | None and Union[x, y] to x | y (#26633)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-12 17:51:31 +01:00
committed by GitHub
parent 9bb38130cb
commit 8fcaaf6a16
944 changed files with 9490 additions and 10121 deletions
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35
vllm/model_executor/models/gemma.py
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@@ -20,7 +20,6 @@
from collections.abc import Iterable
from functools import cache
from itertools import islice
from typing import Optional, Union
import torch
from torch import nn
@@ -59,8 +58,8 @@ logger = init_logger(__name__)
@cache
def _get_gemma_act_fn(
hidden_act: Optional[str],
hidden_activation: Optional[str],
hidden_act: str | None,
hidden_activation: str | None,
) -> nn.Module:
if hidden_activation is None:
if hidden_act is not None:
@@ -92,9 +91,9 @@ class GemmaMLP(nn.Module):
self,
hidden_size: int,
intermediate_size: int,
hidden_act: Optional[str] = None,
hidden_activation: Optional[str] = None,
quant_config: Optional[QuantizationConfig] = None,
hidden_act: str | None = None,
hidden_activation: str | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -130,8 +129,8 @@ class GemmaAttention(nn.Module):
head_dim: int,
max_position_embeddings: int = 8192,
rope_theta: float = 10000,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -207,8 +206,8 @@ class GemmaDecoderLayer(nn.Module):
def __init__(
self,
config: GemmaConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -241,7 +240,7 @@ class GemmaDecoderLayer(nn.Module):
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
residual: torch.Tensor | None,
) -> tuple[torch.Tensor, torch.Tensor]:
# Self Attention
if residual is None:
@@ -301,9 +300,9 @@ class GemmaModel(nn.Module):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
intermediate_tensors: IntermediateTensors | None,
inputs_embeds: torch.Tensor | None = None,
) -> torch.Tensor | IntermediateTensors:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
@@ -406,9 +405,9 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
) -> torch.Tensor | IntermediateTensors:
hidden_states = self.model(
input_ids, positions, intermediate_tensors, inputs_embeds
)
@@ -417,7 +416,7 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> Optional[torch.Tensor]:
) -> torch.Tensor | None:
logits = self.logits_processor(self.model.embed_tokens, hidden_states)
return logits