Skip to content

[10/n] decouple quantization impl from vllm dependency - fix import #13524

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
FlamingoPg merged 5 commits into from
Nov 20, 2025
Merged

[10/n] decouple quantization impl from vllm dependency - fix import #13524

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
a6f23b6
Refactor FP8 imports and remove dependency checks
FlamingoPg Nov 18, 2025
10b6aa2
Merge branch 'main' into clean-vllm-dependency-marlin
FlamingoPg Nov 18, 2025
5eba110
Refactor FP8 quantization to check CUDA support first
FlamingoPg Nov 18, 2025
78e71d9
Merge branch 'main' into clean-vllm-dependency-marlin
AniZpZ Nov 19, 2025
11932b0
fmt
AniZpZ Nov 19, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 6 additions & 25 deletions python/sglang/srt/layers/quantization/fp8.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,35 +10,12 @@
from torch.nn import Module
from torch.nn.parameter import Parameter

from sglang.srt.distributed import get_tp_group
from sglang.srt.distributed.device_communicators.pynccl_allocator import (
use_symmetric_memory,
)
from sglang.srt.layers.dp_attention import is_allocation_symmetric

try:
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
apply_fp8_marlin_linear,
prepare_fp8_layer_for_marlin,
)

MARLIN_FP8_AVAILABLE = True
except ImportError:
MARLIN_FP8_AVAILABLE = False

def dummy_func(*args, **kwargs):
raise ImportError(
"marlin FP8 requires some operators from vllm. Please install vllm."
)

apply_fp8_marlin_linear = prepare_fp8_layer_for_marlin = dummy_func


from sglang.srt.distributed import get_tensor_model_parallel_world_size, get_tp_group
from sglang.srt.distributed.device_communicators.pynccl_allocator import (
use_symmetric_memory,
)
from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
from sglang.srt.layers.dp_attention import is_allocation_symmetric
from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
from sglang.srt.layers.moe.moe_runner.deep_gemm import DeepGemmMoeQuantInfo
from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
Expand Down Expand Up @@ -69,6 +46,10 @@ def dummy_func(*args, **kwargs):
normalize_e4m3fn_to_e4m3fnuz,
)
from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
from sglang.srt.layers.quantization.marlin_utils_fp8 import (
apply_fp8_marlin_linear,
prepare_fp8_layer_for_marlin,
)
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import (
all_close_1d,
Expand Down Expand Up @@ -228,7 +209,7 @@ def __init__(self, quant_config: Union[Fp8Config, W4AFp8Config]):
# For GPUs that lack FP8 hardware support, we can leverage the Marlin
# kernel for fast weight-only FP8 quantization
self.use_marlin = False
if _is_cuda and MARLIN_FP8_AVAILABLE:
if _is_cuda:
force_marlin = get_bool_env_var("SGLANG_FORCE_FP8_MARLIN")
auto_enable = can_auto_enable_marlin_fp8()
self.use_marlin = force_marlin or auto_enable
Expand Down
Loading