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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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13
vllm/model_executor/models/deepseek_mtp.py
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@@ -1,7 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
from typing import Optional
import torch
import torch.nn as nn
@@ -30,7 +29,7 @@ class SharedHead(nn.Module):
self,
config: PretrainedConfig,
prefix: str,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
) -> None:
super().__init__()
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -84,7 +83,7 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
input_ids: torch.Tensor,
positions: torch.Tensor,
previous_hidden_states: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
inputs_embeds: torch.Tensor | None = None,
spec_step_index: int = 0,
) -> torch.Tensor:
assert inputs_embeds is not None
@@ -136,7 +135,7 @@ class DeepSeekMultiTokenPredictor(nn.Module):
input_ids: torch.Tensor,
positions: torch.Tensor,
previous_hidden_states: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
inputs_embeds: torch.Tensor | None = None,
spec_step_idx: int = 0,
) -> torch.Tensor:
if inputs_embeds is None:
@@ -180,8 +179,8 @@ class DeepSeekMTP(nn.Module, SupportsPP):
input_ids: torch.Tensor,
positions: torch.Tensor,
hidden_states: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
spec_step_idx: int = 0,
) -> torch.Tensor:
hidden_states = self.model(
@@ -193,7 +192,7 @@ class DeepSeekMTP(nn.Module, SupportsPP):
self,
hidden_states: torch.Tensor,
spec_step_idx: int = 0,
) -> Optional[torch.Tensor]:
) -> torch.Tensor | None:
return self.model.compute_logits(hidden_states, spec_step_idx)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: