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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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39
vllm/model_executor/models/jamba.py
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@@ -4,7 +4,6 @@
from collections.abc import Iterable
from itertools import islice
from typing import Optional
import torch
from torch import nn
@@ -54,11 +53,11 @@ class JambaMoE(nn.Module):
def __init__(
self,
config: JambaConfig,
num_experts: Optional[int] = None,
top_k: Optional[int] = None,
params_dtype: Optional[torch.dtype] = None,
tp_size: Optional[int] = None,
quant_config: Optional[QuantizationConfig] = None,
num_experts: int | None = None,
top_k: int | None = None,
params_dtype: torch.dtype | None = None,
tp_size: int | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -111,10 +110,10 @@ class JambaMambaDecoderLayer(nn.Module):
self,
config: JambaConfig,
layer_idx: int,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
is_lora_enabled: Optional[bool] = False,
model_config: ModelConfig | None = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
is_lora_enabled: bool | None = False,
prefix: str = "",
**kwargs,
) -> None:
@@ -159,7 +158,7 @@ class JambaMambaDecoderLayer(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
residual: torch.Tensor | None,
**kwargs,
):
if residual is None:
@@ -181,9 +180,9 @@ class JambaAttentionDecoderLayer(nn.Module):
self,
config: JambaConfig,
layer_idx: int,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
model_config: ModelConfig | None = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
**kwargs,
) -> None:
@@ -266,7 +265,7 @@ class JambaAttentionDecoderLayer(nn.Module):
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
residual: torch.Tensor | None,
**kwargs,
):
if residual is None:
@@ -348,8 +347,8 @@ class JambaModel(nn.Module):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
) -> torch.Tensor:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
@@ -523,8 +522,8 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHyb
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
**kwargs,
):
hidden_states = self.model(
@@ -568,7 +567,7 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHyb
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> Optional[torch.Tensor]:
) -> torch.Tensor | None:
logits = self.logits_processor(self.lm_head, hidden_states)
return logits