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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/_ipex_ops.py
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@@ -1,7 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional, Union
import torch
@@ -65,7 +64,7 @@ class ipex_ops:
context_lens: torch.Tensor,
block_size: int,
max_context_len: int,
alibi_slopes: Optional[torch.Tensor],
alibi_slopes: torch.Tensor | None,
kv_cache_dtype: str,
k_scale: float,
v_scale: float,
@@ -107,7 +106,7 @@ class ipex_ops:
context_lens: torch.Tensor,
block_size: int,
max_context_len: int,
alibi_slopes: Optional[torch.Tensor],
alibi_slopes: torch.Tensor | None,
kv_cache_dtype: str,
k_scale: float,
v_scale: float,
@@ -174,7 +173,7 @@ class ipex_ops:
out: torch.Tensor,
seqlen_q: torch.Tensor,
seqlen_k: torch.Tensor,
alibi_slopes: Optional[torch.Tensor],
alibi_slopes: torch.Tensor | None,
max_seqlen_q: int,
max_seqlen_k: int,
pdropout: float,
@@ -254,8 +253,8 @@ class ipex_ops:
value_cache: torch.Tensor,
slot_mapping: torch.Tensor,
kv_cache_dtype: str,
k_scale: Optional[torch.Tensor] = None,
v_scale: Optional[torch.Tensor] = None,
k_scale: torch.Tensor | None = None,
v_scale: torch.Tensor | None = None,
k_scale_float: float = 1.0,
v_scale_float: float = 1.0,
) -> None:
@@ -283,10 +282,10 @@ class ipex_ops:
softmax_scale: float,
causal: bool,
block_table: torch.Tensor,
alibi_slopes: Optional[torch.Tensor],
window_size: Optional[list[int]] = None,
softcap: Optional[float] = 0.0,
cu_seqlens_k: Optional[torch.Tensor] = None,
alibi_slopes: torch.Tensor | None,
window_size: list[int] | None = None,
softcap: float | None = 0.0,
cu_seqlens_k: torch.Tensor | None = None,
# The following parameters are not used in ipex kernel currently,
# we keep API compatible to CUDA's.
scheduler_metadata=None,
@@ -295,7 +294,7 @@ class ipex_ops:
k_descale=None,
v_descale=None,
num_splits=0,
s_aux: Optional[torch.Tensor] = None,
s_aux: torch.Tensor | None = None,
):
if cu_seqlens_k is None:
# cu_seqlens_k is not used in ipex kernel.
@@ -344,10 +343,10 @@ class ipex_ops:
cache_seqlens: torch.Tensor,
qkv_dtype=torch.bfloat16,
headdim_v=None,
cu_seqlens_q: Optional[torch.Tensor] = None,
cu_seqlens_k_new: Optional[torch.Tensor] = None,
cache_leftpad: Optional[torch.Tensor] = None,
page_size: Optional[int] = None,
cu_seqlens_q: torch.Tensor | None = None,
cu_seqlens_k_new: torch.Tensor | None = None,
cache_leftpad: torch.Tensor | None = None,
page_size: int | None = None,
max_seqlen_k_new=0,
causal=False,
window_size=(-1, -1), # -1 means infinite context window
@@ -382,11 +381,11 @@ class ipex_ops:
@staticmethod
def scaled_fp8_quant(
input: torch.Tensor,
scale: Optional[torch.Tensor] = None,
num_token_padding: Optional[int] = None,
scale_ub: Optional[torch.Tensor] = None,
scale: torch.Tensor | None = None,
num_token_padding: int | None = None,
scale_ub: torch.Tensor | None = None,
use_per_token_if_dynamic: bool = False,
output: Optional[torch.Tensor] = None,
output: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Quantize input tensor to FP8 and return quantized tensor and scale.
@@ -414,7 +413,7 @@ class ipex_ops:
"""
# This code assumes batch_dim and num_tokens are flattened
assert input.ndim == 2
shape: Union[tuple[int, int], torch.Size] = input.shape
shape: tuple[int, int] | torch.Size = input.shape
out_dtype: torch.dtype = current_platform.fp8_dtype()
if num_token_padding:
shape = (max(num_token_padding, input.shape[0]), shape[1])