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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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19
vllm/model_executor/layers/quantization/utils/marlin_utils.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
import numpy
import torch
@@ -34,9 +33,9 @@ USE_FP32_REDUCE_DEFAULT = True
# without runtime zero-point. We support common cases, i.e. AWQ and GPTQ.
# TODO: we may want to move this into the C++ so its closer to the actual impl
def query_marlin_supported_quant_types(
has_zp: Optional[bool] = None,
has_zp: bool | None = None,
include_fp_type: bool = True,
device_capability: Optional[int] = None,
device_capability: int | None = None,
):
if device_capability is None:
capability_tuple = current_platform.get_device_capability()
@@ -72,10 +71,10 @@ def query_marlin_supported_quant_types(
def _check_marlin_supported(
quant_type: ScalarType,
group_size: Optional[int],
group_size: int | None,
has_zp: bool,
device_capability: Optional[int] = None,
) -> tuple[bool, Optional[str]]:
device_capability: int | None = None,
) -> tuple[bool, str | None]:
if device_capability is None:
capability_tuple = current_platform.get_device_capability()
device_capability = (
@@ -109,7 +108,7 @@ def check_marlin_supported(
quant_type: ScalarType,
group_size: int,
has_zp: bool = False,
device_capability: Optional[int] = None,
device_capability: int | None = None,
) -> bool:
cond, _ = _check_marlin_supported(quant_type, group_size, has_zp, device_capability)
return cond
@@ -164,7 +163,7 @@ def check_marlin_supports_shape(
input_size_per_partition: int,
input_size: int,
group_size: int,
) -> tuple[bool, Optional[str]]:
) -> tuple[bool, str | None]:
try:
verify_marlin_supports_shape(
output_size_per_partition, input_size_per_partition, input_size, group_size
@@ -445,7 +444,7 @@ def apply_gptq_marlin_linear(
output_size_per_partition: int,
input_size_per_partition: int,
is_k_full: bool,
bias: Optional[torch.Tensor] = None,
bias: torch.Tensor | None = None,
use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT,
) -> torch.Tensor:
reshaped_x = input.reshape(-1, input.shape[-1])
@@ -494,7 +493,7 @@ def apply_awq_marlin_linear(
quant_type: ScalarType,
output_size_per_partition: int,
input_size_per_partition: int,
bias: Optional[torch.Tensor] = None,
bias: torch.Tensor | None = None,
use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT,
) -> torch.Tensor:
reshaped_x = input.reshape(-1, input.shape[-1])