384 lines
14 KiB
Python
384 lines
14 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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import torch
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from packaging.version import InvalidVersion, Version
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from vllm.logger import init_logger
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from vllm_omni.diffusion.attention.backends.abstract import (
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AttentionBackend,
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AttentionImpl,
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AttentionMetadata,
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)
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logger = init_logger(__name__)
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try:
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import flashinfer
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from flashinfer.prefill import BatchPrefillWithRaggedKVCacheWrapper
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HAS_FLASHINFER = True
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except Exception as e:
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HAS_FLASHINFER = False
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logger.warning(
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"FlashInfer is unavailable; FLASHINFER_ATTN backend will not work. Reason: %s",
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e,
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)
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class FlashInferAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@classmethod
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def supports_attention_mask(cls) -> bool:
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# Non-CuTe batch-prefill backends accept boolean custom masks. CuTe-DSL
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# falls back to SDPA for nontrivial masks.
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return True
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@staticmethod
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def get_supported_head_sizes() -> list[int]:
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# FlashInfer dense prefill is well-tested for these head_dims on
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# Ampere/Hopper/Blackwell. Covers the dominant diffusion DiT shapes
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# (SD3 = 64, Flux/HV/Wan = 128, joint-attn = 256).
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return [64, 128, 256]
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@staticmethod
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def get_name() -> str:
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return "FLASHINFER_ATTN"
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@staticmethod
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def get_impl_cls() -> type["FlashInferAttentionImpl"]:
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return FlashInferAttentionImpl
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class FlashInferAttentionImpl(AttentionImpl):
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_QK_DTYPES = {torch.float16, torch.bfloat16}
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_VO_DTYPES = {torch.float16, torch.bfloat16, torch.float8_e4m3fn}
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@dataclass(frozen=True)
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class _WrapperPlanKey:
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batch_size: int
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qo_len: int
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kv_len: int
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num_q_heads: int
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num_kv_heads: int
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head_dim_qk: int
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head_dim_k: int
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head_dim_vo: int
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q_dtype: torch.dtype
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k_dtype: torch.dtype
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v_dtype: torch.dtype
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o_dtype: torch.dtype
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causal: bool
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softmax_scale: float
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has_custom_mask: bool
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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softmax_scale: float,
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causal: bool = False,
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num_kv_heads: int | None = None,
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prefix: str = "",
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backend_kwargs: dict | None = None,
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**extra_impl_args,
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) -> None:
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self.causal = causal
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self.softmax_scale = softmax_scale
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self.device = torch.device("cuda", torch.accelerator.current_device_index())
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backend_kwargs = backend_kwargs or {}
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quant = backend_kwargs.get("quant") or {}
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self.dtype_qk = self._check_dtype(quant.get("dtype_qk"), "dtype_qk", self._QK_DTYPES)
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self.dtype_vo = self._check_dtype(quant.get("dtype_vo"), "dtype_vo", self._VO_DTYPES)
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requested_backend = quant.get("flashinfer_backend", "auto")
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if not HAS_FLASHINFER:
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raise ImportError("FLASHINFER_ATTN backend requires flashinfer")
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self._check_flashinfer_version()
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self.flashinfer_backend = self._select_backend(requested_backend, self.device)
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workspace_size = 0 if self.flashinfer_backend == "cute-dsl" else 128 * 1024 * 1024
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self._workspace = torch.empty(
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workspace_size,
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device=self.device,
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dtype=torch.uint8,
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)
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self._wrapper = BatchPrefillWithRaggedKVCacheWrapper(
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self._workspace,
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kv_layout="NHD",
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backend=self.flashinfer_backend,
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)
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self._qo_indptr: torch.Tensor | None = None
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self._kv_indptr: torch.Tensor | None = None
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self._plan_key: FlashInferAttentionImpl._WrapperPlanKey | None = None
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if self.dtype_qk is not None or self.dtype_vo is not None:
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logger.info_once(
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"FLASHINFER_ATTN dtype override: Q/K=%s, V=%s.",
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self.dtype_qk,
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self.dtype_vo,
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)
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logger.info_once(
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"FLASHINFER_ATTN initialized backend=%s on %s.",
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self.flashinfer_backend,
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self.device,
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)
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def _check_flashinfer_version(self) -> None:
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if self.dtype_qk == self.dtype_vo:
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return
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try:
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flashinfer_version = Version(flashinfer.__version__)
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except (AttributeError, InvalidVersion):
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return
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if flashinfer_version <= Version("0.6.15"):
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raise RuntimeError(
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f"FlashInfer {flashinfer_version} is too old for reliable mixed "
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f"QK/V dtype attention (Q/K={self.dtype_qk}, V={self.dtype_vo}); "
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"install flashinfer >= 0.6.16rc1."
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)
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@staticmethod
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def _select_backend(requested_backend: str, device: torch.device) -> str:
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if requested_backend != "auto":
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return requested_backend
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major, _minor = torch.cuda.get_device_capability(device)
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if major >= 10:
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return "cute-dsl"
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if major >= 9:
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return "fa3"
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return "fa2"
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@classmethod
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def _check_dtype(
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cls,
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dtype: torch.dtype | str | None,
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option_name: str,
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allowed: set[torch.dtype],
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) -> torch.dtype | None:
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if dtype is None:
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return None
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if isinstance(dtype, str):
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dtype = torch.float8_e4m3fn if dtype == "fp8_e4m3" else getattr(torch, dtype)
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if dtype not in allowed:
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choices = ", ".join(sorted(str(item) for item in allowed))
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raise ValueError(f"Unsupported {option_name}={dtype}; expected one of: {choices}")
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return dtype
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@staticmethod
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def _pack_mask_for_flashinfer(
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attn_mask: torch.Tensor, batch_size: int, qo_len: int, kv_len: int
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) -> torch.Tensor | None:
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"""Convert a diffusion-style attn_mask into the boolean form
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FlashInfer's ``custom_mask`` expects (``True`` = keep).
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Returns either ``(qo_len, kv_len)`` (shared across the batch) or
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``(batch_size, qo_len, kv_len)`` (per-sample), or ``None`` when the
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mask is all-keep (elide). Only boolean masks are handled here;
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additive/float masks raise ``ValueError`` so the caller falls back to
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SDPA, which applies them with the correct softmax semantics. Shape
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mismatches also raise ``ValueError``.
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"""
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mask = attn_mask
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if mask.dtype != torch.bool:
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# Additive masks (0 / -inf / -1e4 / finfo.min) cannot be faithfully
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# reduced to a boolean keep-mask here; SDPA handles them correctly.
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raise ValueError(
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f"non-boolean attn_mask (dtype={mask.dtype}); FlashInfer custom_mask "
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"is boolean-only — deferring to SDPA"
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)
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# Diffusion masks arrive as (qo,kv), (1,1,kv), (B,1,1,kv), (B,1,qo,kv)
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# or (B,H,qo,kv). The mask is identical across heads, so collapse the
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# head dim, but keep a real batch dim — indexing mask[0] would reuse
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# sample 0's padding for every sample (wrong under CFG / mixed lengths).
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if mask.dim() == 4:
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mask = mask[:, 0] # (B, qo|1, kv)
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if mask.dim() == 3 and mask.shape[0] == 1:
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mask = mask[0] # (qo|1, kv) — shared across the batch
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try:
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if mask.dim() >= 3:
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mask = mask.broadcast_to((batch_size, qo_len, kv_len))
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else:
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mask = mask.broadcast_to((qo_len, kv_len))
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except RuntimeError as e:
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raise ValueError(
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f"attn_mask shape {tuple(attn_mask.shape)} cannot broadcast to "
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f"(batch={batch_size}, qo_len={qo_len}, kv_len={kv_len})"
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) from e
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if mask.all():
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return None
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# ``broadcast_to`` returns a non-contiguous view; materialize for the
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# kernel, which reads from GPU memory directly.
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return mask.contiguous()
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def _sdpa_fallback(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attn_metadata: AttentionMetadata | None,
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) -> torch.Tensor:
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from vllm_omni.diffusion.attention.backends.sdpa import SDPAImpl
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impl = SDPAImpl(
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num_heads=query.shape[2],
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head_size=query.shape[3],
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softmax_scale=self.softmax_scale,
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causal=self.causal,
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)
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return impl.forward_cuda(query, key, value, attn_metadata)
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@torch.compiler.disable
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def _plan_wrapper(
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self,
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key: _WrapperPlanKey,
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flat_mask: torch.Tensor | None,
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) -> None:
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self._wrapper.plan(
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self._qo_indptr,
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self._kv_indptr,
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key.num_q_heads,
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key.num_kv_heads,
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key.head_dim_qk,
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head_dim_vo=key.head_dim_vo,
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custom_mask=flat_mask,
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causal=key.causal,
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sm_scale=key.softmax_scale,
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q_data_type=key.q_dtype,
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# FlashInfer versions newer than 0.6.15 can dispatch K and V with
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# independent runtime dtypes even though plan() names this K/V.
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kv_data_type=key.k_dtype,
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o_data_type=key.o_dtype,
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)
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@torch.compiler.disable
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def _make_indptr(self, batch_size: int, sequence_length: int) -> torch.Tensor:
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return (
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torch.arange(
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batch_size + 1,
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device=self.device,
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dtype=torch.int32,
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)
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* sequence_length
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)
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def _ensure_plan(
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self,
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key: _WrapperPlanKey,
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flat_mask: torch.Tensor | None,
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) -> None:
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key_changed = key != self._plan_key
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if not key_changed and flat_mask is None:
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return
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if key_changed:
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self._qo_indptr = self._make_indptr(key.batch_size, key.qo_len)
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self._kv_indptr = self._make_indptr(key.batch_size, key.kv_len)
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# A custom mask's contents may change without its shape changing, so
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# copy/re-plan it for every masked invocation.
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self._plan_wrapper(key, flat_mask)
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self._plan_key = key
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def _run_batch_prefill(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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custom_mask: torch.Tensor | None,
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) -> torch.Tensor:
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if query.device != self.device or key.device != self.device or value.device != self.device:
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raise ValueError(
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"FLASHINFER_ATTN inputs must remain on the layer initialization "
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f"device {self.device}; got Q={query.device}, K={key.device}, "
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f"V={value.device}"
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)
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batch_size, qo_len, num_q_heads, head_dim_qk = query.shape
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kv_len = key.shape[1]
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num_kv_heads = key.shape[2]
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head_dim_k = key.shape[3]
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head_dim_vo = value.shape[3]
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q = query.reshape(batch_size * qo_len, num_q_heads, head_dim_qk)
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k = key.reshape(batch_size * kv_len, num_kv_heads, head_dim_k)
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v = value.reshape(batch_size * kv_len, num_kv_heads, head_dim_vo)
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if self.dtype_qk is not None:
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q = q.to(self.dtype_qk)
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k = k.to(self.dtype_qk)
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if self.dtype_vo is not None:
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v = v.to(self.dtype_vo)
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flat_mask = None
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if custom_mask is not None:
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if custom_mask.dim() == 2:
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custom_mask = custom_mask.unsqueeze(0).expand(batch_size, -1, -1)
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flat_mask = custom_mask.contiguous().view(-1)
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self._ensure_plan(
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FlashInferAttentionImpl._WrapperPlanKey(
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batch_size=batch_size,
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qo_len=qo_len,
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kv_len=kv_len,
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num_q_heads=num_q_heads,
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num_kv_heads=num_kv_heads,
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head_dim_qk=head_dim_qk,
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head_dim_k=head_dim_k,
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head_dim_vo=head_dim_vo,
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q_dtype=q.dtype,
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k_dtype=k.dtype,
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v_dtype=v.dtype,
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o_dtype=query.dtype,
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causal=self.causal,
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softmax_scale=self.softmax_scale,
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has_custom_mask=flat_mask is not None,
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),
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flat_mask,
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)
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out = self._run_wrapper(q, k, v)
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out = out.reshape(batch_size, qo_len, num_q_heads, head_dim_vo)
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return out.to(query.dtype) if out.dtype != query.dtype else out
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@torch.compiler.disable
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def _run_wrapper(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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) -> torch.Tensor:
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return self._wrapper.run(query, key, value)
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def forward_cuda(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attn_metadata: AttentionMetadata | None = None,
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) -> torch.Tensor:
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batch_size = query.shape[0]
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custom_mask = None
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if attn_metadata is not None and attn_metadata.attn_mask is not None:
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try:
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custom_mask = self._pack_mask_for_flashinfer(
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attn_metadata.attn_mask,
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batch_size=batch_size,
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qo_len=query.shape[1],
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kv_len=key.shape[1],
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)
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except ValueError as error:
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logger.debug("Falling back to SDPA for mask path: %s", error)
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return self._sdpa_fallback(query, key, value, attn_metadata)
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if custom_mask is not None and self.causal:
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return self._sdpa_fallback(query, key, value, attn_metadata)
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if custom_mask is not None and self.flashinfer_backend == "cute-dsl":
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logger.debug("CuTe DSL does not support custom masks; deferring to SDPA")
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return self._sdpa_fallback(query, key, value, attn_metadata)
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return self._run_batch_prefill(query, key, value, custom_mask)
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