# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Attention layer with FlashAttention.""" from dataclasses import dataclass from typing import Optional import numpy as np import torch from vllm import _custom_ops as ops from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl, AttentionMetadata, AttentionType, is_quantized_kv_cache) from vllm.attention.layer import Attention from vllm.attention.ops.merge_attn_states import merge_attn_states from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8, get_flash_attn_version, is_flash_attn_varlen_func_available) if is_flash_attn_varlen_func_available(): from vllm.attention.utils.fa_utils import (flash_attn_varlen_func, get_scheduler_metadata, reshape_and_cache_flash) from vllm.config import VllmConfig, get_layers_from_vllm_config from vllm.logger import init_logger from vllm.utils import cdiv from vllm.v1.attention.backends.utils import (AttentionCGSupport, AttentionMetadataBuilder, CommonAttentionMetadata, get_kv_cache_layout) from vllm.v1.kv_cache_interface import AttentionSpec logger = init_logger(__name__) # NOTE(woosuk): This is an arbitrary number. Tune it if needed. _DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH = 16 class FlashAttentionBackend(AttentionBackend): accept_output_buffer: bool = True @classmethod def get_supported_dtypes(cls) -> list[torch.dtype]: return [torch.float16, torch.bfloat16] @classmethod def get_supported_head_sizes(cls) -> list[int]: return [32, 64, 96, 128, 160, 192, 224, 256] @classmethod def validate_head_size(cls, head_size: int) -> None: supported_head_sizes = cls.get_supported_head_sizes() if head_size not in supported_head_sizes: attn_type = cls.__name__.removesuffix("Backend") raise ValueError( f"Head size {head_size} is not supported by {attn_type}. " f"Supported head sizes are: {supported_head_sizes}. " "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use " "FlexAttention backend which supports all head sizes.") @staticmethod def get_name() -> str: return "FLASH_ATTN_VLLM_V1" @staticmethod def get_impl_cls() -> type["FlashAttentionImpl"]: return FlashAttentionImpl @staticmethod def get_metadata_cls() -> type["AttentionMetadata"]: return FlashAttentionMetadata @staticmethod def get_builder_cls() -> type["FlashAttentionMetadataBuilder"]: return FlashAttentionMetadataBuilder @staticmethod def get_kv_cache_shape( num_blocks: int, block_size: int, num_kv_heads: int, head_size: int, ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") return (2, num_blocks, block_size, num_kv_heads, head_size) @staticmethod def get_kv_cache_stride_order() -> tuple[int, ...]: # `stride_order` indicates the permutation that gets # us from `get_kv_cache_shape` to the actual memory layout we want. cache_layout = get_kv_cache_layout() if cache_layout == "NHD": stride_order = (0, 1, 2, 3, 4) elif cache_layout == "HND": stride_order = (0, 1, 3, 2, 4) else: raise ValueError(f"Unknown cache layout format {cache_layout}.") return stride_order @staticmethod def get_fp8_dtype_for_flashattn(kv_cache_dtype: str) -> torch.dtype: if kv_cache_dtype in ("fp8", "fp8_e4m3"): return torch.float8_e4m3fn else: raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}") @dataclass class FlashAttentionMetadata: # NOTE(sang): Definition of context_len, query_len, and seq_len. # |---------- N-1 iteration --------| # |---------------- N iteration ---------------------| # |- tokenA -|......................|-- newTokens ---| # |---------- context_len ----------| # |-------------------- seq_len ---------------------| # |-- query_len ---| num_actual_tokens: int # Number of tokens excluding padding. max_query_len: int query_start_loc: torch.Tensor max_seq_len: int seq_lens: torch.Tensor block_table: torch.Tensor slot_mapping: torch.Tensor # For cascade attention. use_cascade: bool common_prefix_len: int cu_prefix_query_lens: Optional[torch.Tensor] prefix_kv_lens: Optional[torch.Tensor] suffix_kv_lens: Optional[torch.Tensor] # Optional aot scheduling scheduler_metadata: Optional[torch.Tensor] = None prefix_scheduler_metadata: Optional[torch.Tensor] = None max_num_splits: int = 0 causal: bool = True def _get_sliding_window_configs( vllm_config: VllmConfig) -> set[Optional[tuple[int, int]]]: """Get the set of all sliding window configs used in the model.""" sliding_window_configs: set[Optional[tuple[int, int]]] = set() layers = get_layers_from_vllm_config(vllm_config, Attention) for layer in layers.values(): assert isinstance(layer.impl, FlashAttentionImpl) sliding_window_configs.add(layer.impl.sliding_window) return sliding_window_configs class FlashAttentionMetadataBuilder( AttentionMetadataBuilder[FlashAttentionMetadata]): # FA3: # Supports full cudagraphs for all cases. # # FA2: # For FA2, a graph is captured with max_query_len=1, (which is what we # capture by default for num_tokens <= max_num_seqs when there is no # spec-decode) then these graphs will not work for mixed prefill-decode # (unlike FA3). This is due to special max_query_len=1 packed-GQA handling # in FA2. # In summary if we are running with spec decodes the graphs would # work for mixed prefill-decode and uniform-decode. But for non-spec decodes # the graphs would not work for mixed prefill-decode; sorta the inverse # of UNIFORM_SINGLE_TOKEN_DECODE. # Theres probably a better way to describe this using `AttentionCGSupport` # but for now just set it to `UNIFORM_BATCH` to get use to drop down # to FULL_AND_PIECEWISE. # TODO(luka, lucas): audit FA2 as part of: # https://github.com/vllm-project/vllm/issues/22945 cudagraph_support = AttentionCGSupport.ALWAYS \ if get_flash_attn_version() == 3 else AttentionCGSupport.UNIFORM_BATCH def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str], vllm_config: VllmConfig, device: torch.device): self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.parallel_config = vllm_config.parallel_config self.cache_config = vllm_config.cache_config self.compilation_config = vllm_config.compilation_config self.device = device self.num_heads_q = self.model_config.get_num_attention_heads( self.parallel_config) self.num_heads_kv = self.model_config.get_num_kv_heads( self.parallel_config) self.kv_cache_dtype = kv_cache_spec.dtype self.headdim = self.model_config.get_head_size() self.block_size = kv_cache_spec.block_size self.max_num_splits = 0 # No upper bound on the number of splits. self.aot_schedule = (get_flash_attn_version() == 3) self.use_full_cuda_graph = \ self.compilation_config.cudagraph_mode.has_full_cudagraphs() if self.use_full_cuda_graph and self.aot_schedule: self.max_cudagraph_size = self.compilation_config.max_capture_size if self.max_cudagraph_size > 992: # This condition derives from FA3's internal heuristic. # TODO(woosuk): Support larger cudagraph sizes. raise ValueError( "Capture size larger than 992 is not supported for " "full cuda graph.") self.scheduler_metadata = torch.zeros( vllm_config.scheduler_config.max_num_seqs + 1, dtype=torch.int32, device=self.device, ) # When using cuda graph, we need to set the upper bound of the # number of splits so that large enough intermediate buffers are # pre-allocated during capture. self.max_num_splits = _DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH # Sliding window size to be used with the AOT scheduler will be # populated on first build() call. self.aot_sliding_window: Optional[tuple[int, int]] = None def build(self, common_prefix_len: int, common_attn_metadata: CommonAttentionMetadata, fast_build: bool = False) -> FlashAttentionMetadata: """ fast_build disables AOT scheduling, used when there will be few iterations i.e. spec-decode """ num_reqs = common_attn_metadata.num_reqs num_actual_tokens = common_attn_metadata.num_actual_tokens max_query_len = common_attn_metadata.max_query_len max_seq_len = int(common_attn_metadata.seq_lens_cpu.max()) query_start_loc = common_attn_metadata.query_start_loc seq_lens = common_attn_metadata.seq_lens seq_lens_cpu = common_attn_metadata.seq_lens_cpu block_table_tensor = common_attn_metadata.block_table_tensor slot_mapping = common_attn_metadata.slot_mapping causal = common_attn_metadata.causal # the overhead of the aot schedule is not worth it for spec-decode aot_schedule = self.aot_schedule and not fast_build if self.aot_sliding_window is None: self.aot_sliding_window = (-1, -1) # For the AOT scheduler we need the sliding window value to be # constant for all layers to. We have to populate this on the first # build() call so the layers are constructed (cannot populate) # in __init__. if aot_schedule: sliding_window_configs = _get_sliding_window_configs( self.vllm_config) if len(sliding_window_configs) == 1: sliding_window_config = sliding_window_configs.pop() if sliding_window_config is not None: self.aot_sliding_window = sliding_window_config elif len(sliding_window_configs) > 1: self.aot_schedule = False aot_schedule = False def schedule(batch_size, cu_query_lens, max_query_len, seqlens, max_seq_len, causal): cache_dtype = self.cache_config.cache_dtype if cache_dtype.startswith("fp8"): qkv_dtype = FlashAttentionBackend.get_fp8_dtype_for_flashattn( cache_dtype) else: qkv_dtype = self.kv_cache_dtype if aot_schedule: return get_scheduler_metadata( batch_size=batch_size, max_seqlen_q=max_query_len, max_seqlen_k=max_seq_len, num_heads_q=self.num_heads_q, num_heads_kv=self.num_heads_kv, headdim=self.headdim, cache_seqlens=seqlens, qkv_dtype=qkv_dtype, cu_seqlens_q=cu_query_lens, page_size=self.block_size, causal=causal, window_size=self.aot_sliding_window, num_splits=self.max_num_splits, ) return None use_cascade = common_prefix_len > 0 if use_cascade: cu_prefix_query_lens = torch.tensor([0, num_actual_tokens], dtype=torch.int32, device=self.device) prefix_kv_lens = torch.tensor([common_prefix_len], dtype=torch.int32, device=self.device) suffix_kv_lens = (seq_lens_cpu[:num_reqs] - common_prefix_len).to( self.device, non_blocking=True) prefix_scheduler_metadata = schedule( batch_size=1, cu_query_lens=cu_prefix_query_lens, max_query_len=num_actual_tokens, seqlens=prefix_kv_lens, max_seq_len=common_prefix_len, causal=False) scheduler_metadata = schedule(batch_size=num_reqs, cu_query_lens=query_start_loc, max_query_len=max_query_len, seqlens=suffix_kv_lens, max_seq_len=max_seq_len - common_prefix_len, causal=True) else: cu_prefix_query_lens = None prefix_kv_lens = None suffix_kv_lens = None prefix_scheduler_metadata = None scheduler_metadata = schedule(batch_size=num_reqs, cu_query_lens=query_start_loc, max_query_len=max_query_len, seqlens=seq_lens, max_seq_len=max_seq_len, causal=causal) # For FA3 + full cudagraph max_num_splits = 0 if self.use_full_cuda_graph and scheduler_metadata is not None: n = scheduler_metadata.shape[0] self.scheduler_metadata[:n] = scheduler_metadata # NOTE(woosuk): We should zero out the rest of the scheduler # metadata to guarantee the correctness. Otherwise, some thread # blocks may use the invalid scheduler metadata and overwrite the # output buffer. self.scheduler_metadata[n:] = 0 scheduler_metadata = self.scheduler_metadata[:n] if num_actual_tokens <= self.max_cudagraph_size: # NOTE(woosuk): Setting num_splits > 1 may increase the memory # usage, because the intermediate buffers of size [num_splits, # num_heads, num_tokens, head_size] are allocated. Therefore, # we only set num_splits when using cuda graphs. max_num_splits = self.max_num_splits attn_metadata = FlashAttentionMetadata( num_actual_tokens=num_actual_tokens, max_query_len=max_query_len, query_start_loc=query_start_loc, max_seq_len=max_seq_len, seq_lens=seq_lens, block_table=block_table_tensor, slot_mapping=slot_mapping, use_cascade=use_cascade, common_prefix_len=common_prefix_len, scheduler_metadata=scheduler_metadata, cu_prefix_query_lens=cu_prefix_query_lens, prefix_kv_lens=prefix_kv_lens, suffix_kv_lens=suffix_kv_lens, prefix_scheduler_metadata=prefix_scheduler_metadata, max_num_splits=max_num_splits, causal=causal) return attn_metadata def use_cascade_attention(self, *args, **kwargs) -> bool: return use_cascade_attention(*args, **kwargs) class FlashAttentionImpl(AttentionImpl): def __init__( self, num_heads: int, head_size: int, scale: float, num_kv_heads: int, alibi_slopes: Optional[list[float]], sliding_window: Optional[int], kv_cache_dtype: str, logits_soft_cap: Optional[float] = None, attn_type: AttentionType = AttentionType.DECODER, kv_sharing_target_layer_name: Optional[str] = None, sinks: Optional[torch.Tensor] = None, ) -> None: self.num_heads = num_heads self.head_size = head_size self.scale = float(scale) self.num_kv_heads = num_kv_heads if alibi_slopes is not None: alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32) self.alibi_slopes = alibi_slopes if sliding_window is None: self.sliding_window = (-1, -1) elif attn_type == AttentionType.ENCODER_ONLY: self.sliding_window = (sliding_window - 1, sliding_window - 1) else: self.sliding_window = (sliding_window - 1, 0) self.kv_cache_dtype = kv_cache_dtype if logits_soft_cap is None: # In flash-attn, setting logits_soft_cap as 0 means no soft cap. logits_soft_cap = 0 self.logits_soft_cap = logits_soft_cap self.kv_sharing_target_layer_name = kv_sharing_target_layer_name self.num_queries_per_kv = self.num_heads // self.num_kv_heads FlashAttentionBackend.validate_head_size(head_size) if attn_type not in [ AttentionType.DECODER, AttentionType.ENCODER_ONLY ]: raise NotImplementedError("Encoder/decoder cross-attention " "is not implemented for " "FlashAttentionImpl") self.attn_type = attn_type self.vllm_flash_attn_version = get_flash_attn_version() if is_quantized_kv_cache(self.kv_cache_dtype) \ and not flash_attn_supports_fp8(): raise NotImplementedError( "FlashAttention does not support fp8 kv-cache on this device.") self.sinks = sinks if self.sinks is not None: assert self.vllm_flash_attn_version == 3, ( "Sinks are only supported in FlashAttention 3") assert self.sinks.shape[0] == num_heads, ( "Sinks must have the same number of heads as the number of " "heads in the layer") def forward( self, layer: torch.nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: FlashAttentionMetadata, output: Optional[torch.Tensor] = None, output_scale: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Forward pass with FlashAttention. Args: query: shape = [num_tokens, num_heads, head_size] key: shape = [num_tokens, num_kv_heads, head_size] value: shape = [num_tokens, num_kv_heads, head_size] kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size] attn_metadata: Metadata for attention. Returns: shape = [num_tokens, num_heads * head_size] NOTE: FP8 quantization, flash-attn expect the size of {q,k,v}_descale to be (num_sequences, num_kv_heads). We use torch's .expand() to avoid duplicating values """ assert output is not None, "Output tensor must be provided." if output_scale is not None: raise NotImplementedError( "fused output quantization is not yet supported" " for FlashAttentionImpl") if attn_metadata is None: # Profiling run. return output attn_type = self.attn_type # IMPORTANT! # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead # in this method. For example, `view` and `slice` (or `[:n]`) operations # are surprisingly slow even in the case they do not invoke any GPU ops. # Minimize the PyTorch ops in this method as much as possible. # Whenever making a change in this method, please benchmark the # performance to make sure it does not introduce any overhead. num_actual_tokens = attn_metadata.num_actual_tokens # Handle encoder attention differently - no KV cache needed if attn_type in (AttentionType.ENCODER_ONLY, ): # For encoder attention, # we use direct Q, K, V tensors without caching return self._forward_encoder_attention(query[:num_actual_tokens], key[:num_actual_tokens], value[:num_actual_tokens], output[:num_actual_tokens], attn_metadata, layer) # For decoder and cross-attention, use KV cache as before key_cache, value_cache = kv_cache.unbind(0) if self.kv_sharing_target_layer_name is None: # Reshape the input keys and values and store them in the cache. # Skip this if sharing KV cache with an earlier attention layer. # NOTE(woosuk): Here, key and value are padded while slot_mapping is # not padded. However, we don't need to do key[:num_actual_tokens] # and value[:num_actual_tokens] because the reshape_and_cache_flash # op uses the slot_mapping's shape to determine the number of # actual tokens. reshape_and_cache_flash( key, value, key_cache, value_cache, attn_metadata.slot_mapping, self.kv_cache_dtype, layer._k_scale, layer._v_scale, ) if self.kv_cache_dtype.startswith("fp8"): dtype = FlashAttentionBackend.get_fp8_dtype_for_flashattn( self.kv_cache_dtype) key_cache = key_cache.view(dtype) value_cache = value_cache.view(dtype) num_tokens, num_heads, head_size = query.shape query, _ = ops.scaled_fp8_quant( query.reshape( (num_tokens, num_heads * head_size)).contiguous(), layer._q_scale) query = query.reshape((num_tokens, num_heads, head_size)) if not attn_metadata.use_cascade: cu_seqlens_q = attn_metadata.query_start_loc seqused_k = attn_metadata.seq_lens max_seqlen_q = attn_metadata.max_query_len max_seqlen_k = attn_metadata.max_seq_len block_table = attn_metadata.block_table scheduler_metadata = attn_metadata.scheduler_metadata descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1]) flash_attn_varlen_func( q=query[:num_actual_tokens], k=key_cache, v=value_cache, out=output[:num_actual_tokens], cu_seqlens_q=cu_seqlens_q, max_seqlen_q=max_seqlen_q, seqused_k=seqused_k, max_seqlen_k=max_seqlen_k, softmax_scale=self.scale, causal=attn_metadata.causal, alibi_slopes=self.alibi_slopes, window_size=self.sliding_window, block_table=block_table, softcap=self.logits_soft_cap, scheduler_metadata=scheduler_metadata, fa_version=self.vllm_flash_attn_version, q_descale=layer._q_scale.expand(descale_shape), k_descale=layer._k_scale.expand(descale_shape), v_descale=layer._v_scale.expand(descale_shape), num_splits=attn_metadata.max_num_splits, s_aux=self.sinks, ) return output # Cascade attention (rare case). cascade_attention( output[:num_actual_tokens], query[:num_actual_tokens], key_cache, value_cache, cu_query_lens=attn_metadata.query_start_loc, max_query_len=attn_metadata.max_query_len, cu_prefix_query_lens=attn_metadata.cu_prefix_query_lens, prefix_kv_lens=attn_metadata.prefix_kv_lens, suffix_kv_lens=attn_metadata.suffix_kv_lens, max_kv_len=attn_metadata.max_seq_len, softmax_scale=self.scale, alibi_slopes=self.alibi_slopes, sliding_window=self.sliding_window, logits_soft_cap=self.logits_soft_cap, block_table=attn_metadata.block_table, common_prefix_len=attn_metadata.common_prefix_len, fa_version=self.vllm_flash_attn_version, prefix_scheduler_metadata=attn_metadata.prefix_scheduler_metadata, suffix_scheduler_metadata=attn_metadata.scheduler_metadata, q_descale=layer._q_scale, k_descale=layer._k_scale, v_descale=layer._v_scale, ) return output def _forward_encoder_attention( self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, output: torch.Tensor, attn_metadata: FlashAttentionMetadata, layer: torch.nn.Module, ) -> torch.Tensor: """Forward pass for encoder attention without KV cache. Args: query: shape = [num_encoder_tokens, num_heads, head_size] key: shape = [num_encoder_tokens, num_kv_heads, head_size] value: shape = [num_encoder_tokens, num_kv_heads, head_size] output: shape = [num_encoder_tokens, num_heads, head_size] attn_metadata: Encoder attention metadata layer: The attention layer """ # For encoder attention, process FP8 quantization if needed if self.kv_cache_dtype.startswith("fp8"): raise NotImplementedError( "quantization is not supported for encoder attention") # Use encoder-specific metadata for sequence information cu_seqlens_q = attn_metadata.query_start_loc cu_seqlens_k = attn_metadata.query_start_loc max_seqlen_q = attn_metadata.max_query_len max_seqlen_k = attn_metadata.max_query_len descale_shape = ( cu_seqlens_q.shape[0] - 1, # type: ignore[union-attr] self.num_kv_heads) # Call flash attention directly on Q, K, V tensors flash_attn_varlen_func( q=query, k=key, v=value, out=output, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, softmax_scale=self.scale, causal=False, # Encoder attention is bidirectional alibi_slopes=self.alibi_slopes, window_size=self.sliding_window, softcap=self.logits_soft_cap, fa_version=self.vllm_flash_attn_version, q_descale=layer._q_scale.expand(descale_shape), k_descale=layer._k_scale.expand(descale_shape), v_descale=layer._v_scale.expand(descale_shape), ) return output def use_cascade_attention( common_prefix_len: int, query_lens: np.ndarray, num_query_heads: int, num_kv_heads: int, use_alibi: bool, use_sliding_window: bool, use_local_attention: bool, num_sms: int, ) -> bool: """Decide whether to use cascade attention. This function 1) checks whether cascade attention is supported with the given configuration, and 2) heuristically decides whether using cascade attention can improve performance. """ # Too short common prefix. Probably not worth using cascade attention. # We use an arbitrary threshold of 256 tokens. TODO: Tune this threshold. # NOTE(woosuk): This is the common case. We should return False as soon as # possible to avoid any unnecessary computation. if common_prefix_len < 256: return False # Cascade attention is currently not supported with these variants. if use_alibi or use_sliding_window or use_local_attention: return False # Too few queries. Probably not worth using cascade attention. # We use an arbitrary threshold of 8 queries. TODO: Tune this threshold. num_reqs = len(query_lens) if num_reqs < 8: return False # Heuristics to decide whether using cascade attention is beneficial. # 1. When FlashDecoding is not used for normal attention, cascade attention # is likely to be faster since it saves memory bandwidth. num_queries_per_kv = num_query_heads // num_kv_heads # The criteria for using FlashDecoding can be found in the following link: # https://github.com/vllm-project/flash-attention/blob/96266b1111111f3d11aabefaf3bacbab6a89d03c/csrc/flash_attn/flash_api.cpp#L535 use_flash_decoding = (num_queries_per_kv > 1 and not use_sliding_window and not use_alibi and np.all(query_lens == 1)) if not use_flash_decoding: # Use cascade attention. return True # 2. When FlashDecoding is used for normal attention, it is not clear # whether cascade attention is beneficial, because FlashDecoding can # launch more CTAs than cascade attention. # We use a simple performance model to compare the two methods. # NOTE(woosuk): The performance model is very rough and may not be # accurate. num_tokens = num_reqs # NOTE(woosuk): These are default tile sizes. flash-attn might use # different tile sizes (e.g., 64 or 256) depending on the configuration. q_tile_size = 128 kv_tile_size = 128 num_prefix_tiles = cdiv(common_prefix_len, kv_tile_size) cascade_ctas = num_query_heads * cdiv(num_tokens, q_tile_size) cascade_waves = cdiv(cascade_ctas, num_sms) cascade_time = cascade_waves * num_prefix_tiles flash_decoding_ctas = (num_reqs * num_kv_heads * cdiv(num_queries_per_kv, q_tile_size)) flash_decoding_ctas *= num_prefix_tiles flash_decoding_time = cdiv(flash_decoding_ctas, num_sms) # Use cascade attention if it is faster than FlashDecoding. return cascade_time < flash_decoding_time def cascade_attention( output: torch.Tensor, query: torch.Tensor, key_cache: torch.Tensor, value_cache: torch.Tensor, cu_query_lens: torch.Tensor, max_query_len: int, cu_prefix_query_lens: torch.Tensor, prefix_kv_lens: torch.Tensor, suffix_kv_lens: torch.Tensor, max_kv_len: int, softmax_scale: float, alibi_slopes: Optional[torch.Tensor], sliding_window: tuple[int, int], logits_soft_cap: float, block_table: torch.Tensor, common_prefix_len: int, fa_version: int, prefix_scheduler_metadata: Optional[torch.Tensor] = None, suffix_scheduler_metadata: Optional[torch.Tensor] = None, q_descale: Optional[torch.Tensor] = None, k_descale: Optional[torch.Tensor] = None, v_descale: Optional[torch.Tensor] = None, ) -> torch.Tensor: assert alibi_slopes is None, ("Cascade attention does not support ALiBi.") # TODO: Support sliding window. assert sliding_window == (-1, -1), ( "Cascade attention does not support sliding window.") num_tokens = query.shape[0] block_size = key_cache.shape[-3] assert common_prefix_len % block_size == 0 num_common_kv_blocks = common_prefix_len // block_size assert num_common_kv_blocks > 0 descale_shape = (cu_prefix_query_lens.shape[0] - 1, key_cache.shape[-2]) # Process shared prefix. prefix_output, prefix_lse = flash_attn_varlen_func( q=query, k=key_cache, v=value_cache, cu_seqlens_q=cu_prefix_query_lens, seqused_k=prefix_kv_lens, max_seqlen_q=num_tokens, max_seqlen_k=common_prefix_len, softmax_scale=softmax_scale, causal=False, window_size=sliding_window, block_table=block_table[:1], softcap=logits_soft_cap, return_softmax_lse=True, scheduler_metadata=prefix_scheduler_metadata, fa_version=fa_version, q_descale=q_descale.expand(descale_shape) if q_descale is not None else None, k_descale=k_descale.expand(descale_shape) if k_descale is not None else None, v_descale=v_descale.expand(descale_shape) if v_descale is not None else None, ) descale_shape = (cu_query_lens.shape[0] - 1, key_cache.shape[-2]) # Process suffix per query. suffix_output, suffix_lse = flash_attn_varlen_func( q=query, k=key_cache, v=value_cache, cu_seqlens_q=cu_query_lens, seqused_k=suffix_kv_lens, max_seqlen_q=max_query_len, max_seqlen_k=max_kv_len - common_prefix_len, softmax_scale=softmax_scale, causal=True, window_size=sliding_window, block_table=block_table[:, num_common_kv_blocks:], softcap=logits_soft_cap, return_softmax_lse=True, scheduler_metadata=suffix_scheduler_metadata, fa_version=fa_version, q_descale=q_descale.expand(descale_shape) if q_descale is not None else None, k_descale=k_descale.expand(descale_shape) if k_descale is not None else None, v_descale=v_descale.expand(descale_shape) if v_descale is not None else None, ) # Merge prefix and suffix outputs, and store the result in output. merge_attn_states(output, prefix_output, prefix_lse, suffix_output, suffix_lse)