322 lines
11 KiB
Python
322 lines
11 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 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.utils.piecewise_attn import (
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piecewise_attn,
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)
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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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@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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if backend_kwargs:
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logger.warning("FlashAttentionImpl ignoring backend_kwargs: %s", list(backend_kwargs.keys()))
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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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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_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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)
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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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# Try piecewise attention
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if full_attn_spans is not None:
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logger.debug("Using piecewise Flash Attention for mixed causal/full mask")
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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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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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)
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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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if flash_attn_func is not None:
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out = flash_attn_func(
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query,
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key,
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value,
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causal=self.causal,
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softmax_scale=self.softmax_scale,
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)
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return self._unwrap_flash_output(out)
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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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attention_mask = attn_metadata.attn_mask if attn_metadata else None
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layout = self.qkv_layout or "BNSD"
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return attention_forward(
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query,
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key,
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value,
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attn_mask=attention_mask,
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opt_mode="manual",
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op_type="fused_attn_score",
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layout=layout,
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)
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