attention.py

# 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. Use "auto" or None for automatic selection."""

    flash_attn_version: Literal[2, 3, 4] | None = None
    """Force vllm to use a specific flash-attention version (2, 3, or 4).
    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."""

    tq_max_kv_splits_for_cuda_graph: int = 32
    """TurboQuant max NUM_KV_SPLITS for cuda graph decode.
    Fixes the split count so grid dimensions are constant across captures,
    and buffers can be pre-allocated to avoid inflating the memory estimate."""

    use_cudnn_prefill: bool = False
    """Whether to use cudnn prefill."""

    use_trtllm_ragged_deepseek_prefill: bool = False
    """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 = True
    """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: set[str] = set()
        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.

        The special value "auto" is treated as None, which triggers
        automatic backend selection.
        """
        if isinstance(value, str):
            if value.lower() == "auto":
                return None
            return AttentionBackendEnum[value.upper()]
        return value