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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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29
vllm/model_executor/models/bamba.py
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@@ -4,7 +4,6 @@
# Added by the IBM Team, 2024
from collections.abc import Iterable
from typing import Optional
import torch
from torch import nn
@@ -52,7 +51,7 @@ class BambaMLP(nn.Module):
def __init__(
self,
config: BambaConfig,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
bias: bool = False,
) -> None:
super().__init__()
@@ -87,9 +86,9 @@ class BambaMixerDecoderLayer(nn.Module):
self,
config: BambaConfig,
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 = "",
) -> None:
super().__init__()
@@ -119,7 +118,7 @@ class BambaMixerDecoderLayer(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
residual: torch.Tensor | None,
**kwargs,
):
if residual is None:
@@ -141,9 +140,9 @@ class BambaAttentionDecoderLayer(nn.Module):
self,
config: BambaConfig,
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 = "",
) -> None:
super().__init__()
@@ -235,7 +234,7 @@ class BambaAttentionDecoderLayer(nn.Module):
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
residual: torch.Tensor | None,
**kwargs,
):
if residual is None:
@@ -314,8 +313,8 @@ class BambaModel(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:
@@ -497,8 +496,8 @@ class BambaForCausalLM(
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(
@@ -510,7 +509,7 @@ class BambaForCausalLM(
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