Files
vllm/vllm/config/attention.py
Pavani Majety 578977bb5e [SM100] Resubmit FMHA FP8 prefill for MLA (#31195)
Signed-off-by: Pavani Majety <pmajety@nvidia.com>
2026-02-10 16:18:43 -05:00

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2.5 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any, Literal
from pydantic import field_validator
from vllm.config.utils import config
from vllm.v1.attention.backends.registry import AttentionBackendEnum
@config
class AttentionConfig:
"""Configuration for attention mechanisms in vLLM."""
backend: AttentionBackendEnum | None = None
"""Attention backend to use. If None, will be selected automatically."""
flash_attn_version: Literal[2, 3] | None = None
"""Force vllm to use a specific flash-attention version (2 or 3).
Only valid when using the flash-attention backend."""
use_prefill_decode_attention: bool = False
"""Use separate prefill and decode kernels for attention instead of
the unified triton kernel."""
flash_attn_max_num_splits_for_cuda_graph: int = 32
"""Flash Attention max number splits for cuda graph decode."""
use_cudnn_prefill: bool = False
"""Whether to use cudnn prefill."""
use_trtllm_ragged_deepseek_prefill: bool = True
"""Whether to use TRTLLM ragged deepseek prefill."""
use_trtllm_attention: bool | None = None
"""If set to True/False, use or don't use the TRTLLM attention backend
in flashinfer. If None, auto-detect the attention backend in flashinfer."""
disable_flashinfer_prefill: bool = False
"""Whether to disable flashinfer prefill."""
disable_flashinfer_q_quantization: bool = False
"""If set, when using fp8 kv, do not quantize Q to fp8."""
use_prefill_query_quantization: bool = False
"""If set, quantize query for attention in prefill."""
def compute_hash(self) -> str:
"""
Provide a hash that uniquely identifies all the configs
that affect the structure of the computation
graph from input ids/embeddings to the final hidden states,
excluding anything before input ids/embeddings and after
the final hidden states.
"""
from vllm.config.utils import get_hash_factors, hash_factors
ignored_factors: list[str] = []
factors = get_hash_factors(self, ignored_factors)
return hash_factors(factors)
@field_validator("backend", mode="before")
@classmethod
def validate_backend_before(cls, value: Any) -> Any:
"""Enable parsing of the `backend` enum type from string."""
if isinstance(value, str):
return AttentionBackendEnum[value.upper()]
return value