634 lines
25 KiB
Python
634 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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from functools import partial
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import torch
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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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from vllm_omni.diffusion.attention.backends.sdpa import _maybe_reshape_attn_mask
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from vllm_omni.diffusion.attention.backends.utils.piecewise_attn import (
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piecewise_attn,
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)
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from vllm_omni.diffusion.config import get_current_diffusion_config_or_none
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from vllm_omni.platforms import current_omni_platform
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logger = init_logger(__name__)
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class FlashAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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supports_piecewise_spans: bool = True
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@classmethod
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def supports_packed_mask_free(cls) -> bool:
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# CUDA: forward_cuda dispatches the packed cu_seqlens varlen path
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# before ever reading attn_mask. NPU: forward_fa_npu honors the
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# npu_attn_varlen opt-in and rebuilds the padding mask itself if the
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# packed contract fails. XPU reads attn_mask, so models must keep
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# constructing it there.
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return current_omni_platform.is_cuda() or current_omni_platform.is_npu()
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@classmethod
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def supports_attention_mask(cls) -> bool:
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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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return [64, 96, 128, 192, 256]
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@staticmethod
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def get_name() -> str:
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return "FLASH_ATTN"
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@staticmethod
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def get_impl_cls() -> type["FlashAttentionImpl"]:
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return FlashAttentionImpl
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class FlashAttentionImpl(AttentionImpl):
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# Per-platform FP8 KV quantization support.
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# To enable FP8 on a new platform, add its OmniPlatformEnum value here
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# and handle kv_cache_dtype in the corresponding forward_{platform}().
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#
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# TODO(quant-backend): The FP8 quant path currently lives inside
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# FlashAttentionImpl gated by ``attn_metadata.extra["kv_cache_dtype"]``.
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# Eventually extract it into a dedicated FlashAttentionQuantBackend so
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# backend selection (not metadata) decides quant. Until then, model
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# authors can opt a specific Attention layer out via
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# ``Attention(disable_kv_quant=True)``.
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_supported_kv_cache_dtypes = {
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"npu": {"fp8"},
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}
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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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qkv_layout: str | None = None,
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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.num_heads = num_heads
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self.causal = causal
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self.softmax_scale = softmax_scale
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self.qkv_layout = qkv_layout
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cfg = get_current_diffusion_config_or_none()
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self.fa_deterministic = bool(getattr(cfg, "fa_deterministic", False)) if cfg is not None else False
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if backend_kwargs:
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logger.warning("FlashAttentionImpl ignoring backend_kwargs: %s", list(backend_kwargs.keys()))
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def _warn_fa_deterministic_non_dense(self, path: str) -> None:
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if not self.fa_deterministic:
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return
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logger.warning_once(
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"fa_deterministic=True is ignored on the %s FlashAttention path; "
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"only the dense flash_attn_func path passes deterministic=True.",
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path,
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)
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@staticmethod
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def _unwrap_flash_output(out: torch.Tensor | tuple[torch.Tensor, ...]) -> torch.Tensor:
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# FA3 may return (out, lse), FA2 returns out
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return out[0] if isinstance(out, tuple) else out
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@staticmethod
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def _flash_wrapper(q, k, v, *, attn_func, **kwargs):
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return FlashAttentionImpl._unwrap_flash_output(attn_func(q, k, v, **kwargs))
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@staticmethod
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def _flash_varlen_wrapper(q, k, v, *, attn_func, causal, softmax_scale, **kwargs):
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"""Call a varlen-only FlashAttention backend for a dense segment."""
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del kwargs
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batch_size, q_len = q.shape[:2]
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k_len = k.shape[1]
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * q_len, q_len, dtype=torch.int32, device=q.device)
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cu_seqlens_k = torch.arange(0, (batch_size + 1) * k_len, k_len, dtype=torch.int32, device=q.device)
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out = attn_func(
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q=q.flatten(0, 1),
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k=k.flatten(0, 1),
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v=v.flatten(0, 1),
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=q_len,
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max_seqlen_k=k_len,
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causal=causal,
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softmax_scale=softmax_scale,
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)
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out = FlashAttentionImpl._unwrap_flash_output(out)
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return out.reshape(batch_size, q_len, *out.shape[1:])
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def _forward_varlen_masked(
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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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attention_mask: torch.Tensor,
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) -> torch.Tensor:
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from vllm_omni.diffusion.attention.backends.utils.fa import (
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_pad_input,
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_unpad_input,
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_upad_input,
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flash_attn_varlen_func,
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)
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assert attention_mask.ndim == 2, "attention_mask must be 2D, (batch_size, seq_len)"
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query_length = query.size(1)
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q, k, v, indices_q, (cu_seq_lens_q, cu_seq_lens_k), (max_length_q, max_length_k) = _upad_input(
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query, key, value, attention_mask, query_length, _unpad_input
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)
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out_unpad = flash_attn_varlen_func(
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q,
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k,
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v,
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cu_seqlens_q=cu_seq_lens_q,
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cu_seqlens_k=cu_seq_lens_k,
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max_seqlen_q=max_length_q,
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max_seqlen_k=max_length_k,
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**{
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"causal": self.causal,
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"softmax_scale": self.softmax_scale,
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},
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)
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out_unpad = self._unwrap_flash_output(out_unpad)
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return _pad_input(out_unpad, indices_q, query.size(0), query_length)
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def _forward_varlen_packed(
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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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*,
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cu_seqlens_q: torch.Tensor,
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cu_seqlens_k: torch.Tensor,
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max_seqlen_q: int,
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max_seqlen_k: int,
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) -> torch.Tensor:
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"""Run FlashAttention directly on an already packed sequence.
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Some diffusion transformers already maintain exact packed-document
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boundaries. Reusing those boundaries avoids rebuilding boolean masks
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and gathering/scattering Q/K/V in every attention layer.
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"""
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from vllm_omni.diffusion.attention.backends.utils.fa import (
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flash_attn_varlen_func,
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)
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if flash_attn_varlen_func is None:
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raise ImportError("Packed variable-length attention requires flash_attn_varlen_func")
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if query.shape[0] != 1 or key.shape[0] != 1 or value.shape[0] != 1:
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raise ValueError("Packed variable-length attention currently requires batch size 1")
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out = flash_attn_varlen_func(
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q=query.flatten(0, 1),
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k=key.flatten(0, 1),
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v=value.flatten(0, 1),
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=max_seqlen_q,
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max_seqlen_k=max_seqlen_k,
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causal=self.causal,
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softmax_scale=self.softmax_scale,
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)
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out = self._unwrap_flash_output(out)
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return out.reshape_as(query)
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def _forward_varlen_dense(
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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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"""Common wrapper for calling flash_attn_varlen_func for XPU and CUDA in vLLM.
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NOTE: careful to keep the kwargs on these aligned and to pass everything as a keyword
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argument, because some of the args differ positionally at the moment.
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https://github.com/vllm-project/vllm/blob/v0.20.0/vllm/vllm_flash_attn/flash_attn_interface.py#L176
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https://github.com/vllm-project/vllm/blob/v0.20.0/vllm/_xpu_ops.py#L310
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"""
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from vllm_omni.diffusion.attention.backends.utils.fa import (
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flash_attn_varlen_func,
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)
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batch_size, q_len = query.size()[:2]
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k_len = key.size(1)
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * q_len, step=q_len, dtype=torch.int32, device=query.device)
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cu_seqlens_k = torch.arange(0, (batch_size + 1) * k_len, step=k_len, dtype=torch.int32, device=query.device)
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# b s ... -> (b s) ...
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query = query.flatten(0, 1)
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key = key.flatten(0, 1)
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value = value.flatten(0, 1)
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out = flash_attn_varlen_func(
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q=query,
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k=key,
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v=value,
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=q_len,
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max_seqlen_k=k_len,
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causal=self.causal,
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softmax_scale=self.softmax_scale,
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)
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out = self._unwrap_flash_output(out)
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# (b s) h d -> b s h d
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return out.reshape(batch_size, q_len, *out.shape[1:])
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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,
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) -> torch.Tensor:
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"""CUDA/ROCm/MUSA flash attention implementation."""
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from vllm_omni.diffusion.attention.backends.utils.fa import (
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HAS_FLASH_ATTN,
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flash_attn_func,
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flash_attn_varlen_func,
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)
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if not HAS_FLASH_ATTN:
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raise ImportError(
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"FlashAttentionBackend requires Flash Attention. "
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"Please install one of: fa3-fwd, flash-attention, or flash-attn. "
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"Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
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)
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attention_mask = attn_metadata.attn_mask if attn_metadata is not None else None
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full_attn_spans = attn_metadata.full_attn_spans if attn_metadata is not None else None
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extra = attn_metadata.extra if attn_metadata is not None else {}
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# Try piecewise attention
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if full_attn_spans is not None:
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self._warn_fa_deterministic_non_dense("piecewise")
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logger.debug("Using piecewise Flash Attention for mixed causal/full mask")
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if flash_attn_func is not None:
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attn_func = partial(
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FlashAttentionImpl._flash_wrapper,
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attn_func=flash_attn_func,
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)
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elif flash_attn_varlen_func is not None:
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attn_func = partial(
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FlashAttentionImpl._flash_varlen_wrapper,
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attn_func=flash_attn_varlen_func,
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)
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else:
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raise ImportError("Piecewise FlashAttention requires a dense or varlen FlashAttention function")
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return piecewise_attn(
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query,
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key,
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value,
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full_attn_spans,
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self.softmax_scale,
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attn_func,
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query_ranges=attn_metadata.query_ranges,
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)
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packed_keys = ("cu_seqlens_q", "cu_seqlens_k", "max_seqlen_q", "max_seqlen_k")
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present_packed_keys = [key for key in packed_keys if key in extra]
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if present_packed_keys:
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if len(present_packed_keys) != len(packed_keys):
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missing = sorted(set(packed_keys) - set(present_packed_keys))
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raise ValueError(f"Incomplete packed FlashAttention metadata; missing {missing}")
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self._warn_fa_deterministic_non_dense("packed-varlen")
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return self._forward_varlen_packed(
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query,
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key,
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value,
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cu_seqlens_q=extra["cu_seqlens_q"],
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cu_seqlens_k=extra["cu_seqlens_k"],
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max_seqlen_q=extra["max_seqlen_q"],
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max_seqlen_k=extra["max_seqlen_k"],
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)
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if attention_mask is not None and torch.any(~attention_mask):
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self._warn_fa_deterministic_non_dense("masked-varlen")
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return self._forward_varlen_masked(
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query,
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key,
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value,
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attention_mask,
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)
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if flash_attn_func is not None:
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fa_kwargs = {
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"causal": self.causal,
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"softmax_scale": self.softmax_scale,
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}
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if self.fa_deterministic:
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fa_kwargs["deterministic"] = True
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out = flash_attn_func(query, key, value, **fa_kwargs)
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return self._unwrap_flash_output(out)
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self._warn_fa_deterministic_non_dense("dense-varlen-fallback")
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return self._forward_varlen_dense(
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query,
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key,
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value,
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)
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def forward_xpu(
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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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"""XPU flash attention implementation."""
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from vllm_omni.diffusion.attention.backends.utils.fa import (
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HAS_FLASH_ATTN,
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)
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if not HAS_FLASH_ATTN:
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raise ImportError(
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"FlashAttentionBackend requires Flash Attention. "
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"Please assure vllm-xpu-kernels properly installed. "
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"Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
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)
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attention_mask = attn_metadata.attn_mask if attn_metadata is not None else None
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if attention_mask is not None and torch.any(~attention_mask):
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return self._forward_varlen_masked(
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query,
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key,
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value,
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attention_mask,
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)
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return self._forward_varlen_dense(
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query,
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key,
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value,
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)
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def forward_npu(
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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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"""NPU attention implementation using mindiesd."""
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kv_cache_dtype = attn_metadata.extra.get("kv_cache_dtype") if attn_metadata else None
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if kv_cache_dtype is not None:
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return self.forward_fa_quant_npu(query, key, value, attn_metadata)
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return self.forward_fa_npu(query, key, value, attn_metadata)
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def forward_fa_quant_npu(
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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.platforms.npu.quant.kv_quant_npu import fp8_rotate_quant_fa
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layout = self.qkv_layout or "BNSD"
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# Models pass (B, S, H, D); NPU fused op expects (B, N, S, D).
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out = fp8_rotate_quant_fa(
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query.transpose(1, 2),
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key.transpose(1, 2),
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value.transpose(1, 2),
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layout=layout,
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softmax_scale=self.softmax_scale,
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)
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return out.transpose(1, 2)
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def forward_fa_npu(
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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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try:
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from mindiesd import attention_forward
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except ImportError:
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raise ImportError(
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"FlashAttentionBackend NPU implementation requires MindIE-SD. "
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"Please install MindIE-SD to enable NPU attention support. "
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"For installation details, see https://gitcode.com/Ascend/MindIE-SD"
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"Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
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)
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# Opt-in mask-free paths (mirror the CUDA cu_seqlens behavior): the
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# model marks extra["npu_attn_varlen"] and carries packed metadata, so
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# the padding document is excluded without reading or materializing
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# the quadratic full_qk mask.
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# - default: packed varlen via mindiesd attention_forward_varlen;
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# - when MINDIE_SD_FA_TYPE=ascend_laser_attention: prefix K/V
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# slicing via mindiesd attention_forward (so the env-selected op
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# type is honored; the TND varlen op cannot be).
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extra = attn_metadata.extra if attn_metadata else {}
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if extra.get("npu_attn_varlen", False):
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if os.environ.get("MINDIE_SD_FA_TYPE") == "ascend_laser_attention":
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out = self._forward_prefix_kv_slice_npu(query, key, value, extra)
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else:
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out = self._forward_varlen_packed_npu(query, key, value, extra)
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if out is not None:
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return out
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attention_mask = attn_metadata.attn_mask if attn_metadata else None
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if attention_mask is None and extra.get("npu_attn_varlen", False):
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# Models skip mask construction when this opt-in is set
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# (FlashAttentionBackend.supports_packed_mask_free). The packed
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# paths above declined (contract mismatch), so rebuild the padding
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# mask here to keep the masked fallback correct.
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used = extra.get("valid_kv_length")
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if not isinstance(used, int) or not 0 < used <= query.shape[1]:
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raise ValueError(
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"npu_attn_varlen packed metadata is unusable and no attn_mask "
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f"was constructed (valid_kv_length={used!r}, seq_len={query.shape[1]}); "
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"refusing to run unmasked attention over padding rows."
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)
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attention_mask = torch.arange(query.shape[1], device=query.device)[None] < used
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|
|
|
# NPU aclnnFlashAttentionScore requires mask shape to be one of:
|
|
# [B, N, Sq, Skv], [B, 1, Sq, Skv], [1, 1, Sq, Skv], or [Sq, Skv]
|
|
# But the incoming mask is 2D [B, S] — reshape to [B, 1, 1, S]
|
|
# So reuse SDPA's mask reshape logic: [B, S] -> [B, 1, Sq, Skv]
|
|
attention_mask = _maybe_reshape_attn_mask(query, key, attention_mask, mask_mode="full_qk")
|
|
|
|
layout = self.qkv_layout or "BNSD"
|
|
return attention_forward(
|
|
query,
|
|
key,
|
|
value,
|
|
attn_mask=attention_mask,
|
|
opt_mode="manual",
|
|
op_type="fused_attn_score",
|
|
layout=layout,
|
|
)
|
|
|
|
def _resolve_packed_seq_npu(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
extra: dict,
|
|
) -> tuple[list[int], list[int]] | None:
|
|
"""Resolve packed document boundaries (cumulative end offsets per doc).
|
|
|
|
Returns (seq_q, seq_k), e.g. ([used_q, total_q], [used_k, total_k]),
|
|
or None unless the metadata matches the exact contract MiniMax-H3
|
|
produces:
|
|
- q/k/v are [1, T, N, D] (BSND, single packed batch);
|
|
- cu_seqlens describe a [real, pad] two-document packing, with the
|
|
padding document as a strict suffix;
|
|
- max_seqlen_q/k are Python ints equal to the real document length
|
|
(so boundaries are derived without any device sync).
|
|
"""
|
|
if self.causal:
|
|
return None
|
|
packed_keys = ("cu_seqlens_q", "cu_seqlens_k", "max_seqlen_q", "max_seqlen_k")
|
|
if any(k not in extra for k in packed_keys):
|
|
return None
|
|
cu_q, cu_k = extra["cu_seqlens_q"], extra["cu_seqlens_k"]
|
|
used_q, used_k = extra["max_seqlen_q"], extra["max_seqlen_k"]
|
|
if query.shape[0] != 1 or key.shape[0] != 1:
|
|
return None
|
|
if not isinstance(used_q, int) or not isinstance(used_k, int):
|
|
return None
|
|
total_q, total_k = query.shape[1], key.shape[1]
|
|
# .shape is host-side metadata: counting documents never syncs.
|
|
if cu_q.shape[0] != cu_k.shape[0] or cu_q.shape[0] > 3:
|
|
return None
|
|
if not (0 < used_q <= total_q) or not (0 < used_k <= total_k):
|
|
return None
|
|
if used_q == total_q and used_k == total_k:
|
|
# No padding document: one full-length document.
|
|
return [total_q], [total_k]
|
|
if cu_q.shape[0] == 3 and used_q >= total_q - used_q and used_k >= total_k - used_k:
|
|
# [real, pad] packing: the real document must be the longer one
|
|
# (consistent with the max_seqlen naming).
|
|
return [used_q, total_q], [used_k, total_k]
|
|
return None
|
|
|
|
def _forward_varlen_packed_npu(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
extra: dict,
|
|
) -> torch.Tensor | None:
|
|
"""Packed varlen attention on NPU via mindiesd attention_forward_varlen.
|
|
|
|
Returns None (caller falls back to the mask path) when the packed
|
|
contract does not hold; see _resolve_packed_seq_npu.
|
|
"""
|
|
resolved = self._resolve_packed_seq_npu(query, key, extra)
|
|
if resolved is None:
|
|
return None
|
|
seq_q, seq_k = resolved
|
|
|
|
try:
|
|
from mindiesd import attention_forward_varlen
|
|
except ImportError:
|
|
raise ImportError(
|
|
"FlashAttentionBackend NPU implementation requires MindIE-SD. "
|
|
"Please install MindIE-SD to enable NPU attention support. "
|
|
"For installation details, see https://gitcode.com/Ascend/MindIE-SD"
|
|
"Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
|
|
)
|
|
q = query.squeeze(0) # [T, N, D] == TND
|
|
k = key.squeeze(0)
|
|
v = value.squeeze(0)
|
|
# attention_forward_varlen wants cu_seqlens as host lists of cumulative
|
|
# offsets and forwards cu[1:] as actual_seq_qlen/actual_seq_kvlen.
|
|
out = attention_forward_varlen(
|
|
q,
|
|
k,
|
|
v,
|
|
[0, *seq_q],
|
|
[0, *seq_k],
|
|
softmax_scale=self.softmax_scale,
|
|
)
|
|
return out.unsqueeze(0)
|
|
|
|
def _forward_prefix_kv_slice_npu(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
extra: dict,
|
|
) -> torch.Tensor | None:
|
|
"""Mask-free attention by slicing K/V to the valid prefix (zero-copy
|
|
views), then mindiesd attention_forward without a mask.
|
|
|
|
Same numerical contract as the varlen path: the padding document is a
|
|
strict suffix, so dropping it from K/V is identical to masking it out.
|
|
Query keeps full length; outputs on padding rows are never consumed
|
|
downstream. Returns None when the packed contract does not hold.
|
|
|
|
Used when MINDIE_SD_FA_TYPE=ascend_laser_attention: the TND varlen op
|
|
cannot honor the env-selected op type, while attention_forward can.
|
|
|
|
The laser kernel stores unscaled S=QK^T in an fp16 GM workspace, so
|
|
bf16 activations with large outliers overflow 65504 into ±inf and the
|
|
affected rows turn NaN. Models may opt into exact power-of-two input
|
|
pre-scaling via extra["laser_input_scale"] (see abstract.py): q/k/v
|
|
are divided by the factor, the kernel scale_value is multiplied by its
|
|
square, and the output is scaled back. Absent or invalid factor means
|
|
no pre-scaling.
|
|
"""
|
|
resolved = self._resolve_packed_seq_npu(query, key, extra)
|
|
if resolved is None:
|
|
return None
|
|
_, seq_k = resolved
|
|
used_k = seq_k[0] # real document length (first cumulative end)
|
|
|
|
try:
|
|
from mindiesd import attention_forward
|
|
except ImportError:
|
|
raise ImportError(
|
|
"FlashAttentionBackend NPU implementation requires MindIE-SD. "
|
|
"Please install MindIE-SD to enable NPU attention support. "
|
|
"For installation details, see https://gitcode.com/Ascend/MindIE-SD"
|
|
"Otherwise, use SDPA backend by setting DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA"
|
|
)
|
|
# mindiesd always takes BSND input regardless of `layout`; the arg
|
|
# selects the op-internal layout and laser only supports BNSD, so do
|
|
# NOT forward the model's qkv_layout ("BSND" for MiniMax-H3) here.
|
|
layout = "BNSD"
|
|
key = key[:, :used_k]
|
|
value = value[:, :used_k]
|
|
|
|
input_scale = extra.get("laser_input_scale")
|
|
preserve_input_range = isinstance(input_scale, (int, float)) and input_scale > 1
|
|
if preserve_input_range:
|
|
query = query / input_scale
|
|
key = key / input_scale
|
|
value = value / input_scale
|
|
scale = self.softmax_scale
|
|
if preserve_input_range:
|
|
scale *= input_scale**2
|
|
|
|
def _postprocess(out: torch.Tensor) -> torch.Tensor:
|
|
return out * input_scale if preserve_input_range else out
|
|
|
|
return _postprocess(
|
|
attention_forward(
|
|
query,
|
|
key,
|
|
value,
|
|
attn_mask=None,
|
|
opt_mode="manual",
|
|
op_type="fused_attn_score", # MINDIE_SD_FA_TYPE env overrides this
|
|
layout=layout,
|
|
scale=scale,
|
|
)
|
|
)
|