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luopingyi
2026-01-09 15:09:53 +08:00
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vllm/v1/attention/backends/utils.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import abc
import functools
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, ClassVar, Generic, TypeVar
import numpy as np
import torch
from vllm.utils import cdiv
if TYPE_CHECKING:
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.worker.gpu_input_batch import InputBatch
import vllm.envs as envs
from vllm.distributed.kv_transfer.kv_connector.utils import (
get_kv_connector_cache_layout)
from vllm.logger import init_logger
logger = init_logger(__name__)
@dataclass
class CommonAttentionMetadata:
"""
Per-batch attention metadata, shared across layers and backends.
AttentionMetadataBuilder instances use it to construct per-layer metadata.
"""
query_start_loc: torch.Tensor
"""(batch_size + 1,), the start location of each request in query Tensor"""
seq_lens: torch.Tensor
"""(batch_size,), the length of each request including both computed tokens
and newly scheduled tokens"""
num_reqs: int
"""Number of requests"""
num_actual_tokens: int
"""Total number of tokens in batch"""
max_query_len: int
"""Longest query in batch"""
num_speculative_tokens: int = 0
"""Number of speculative tokens"""
slot_mapping: torch.Tensor = None
"""(batch_size, seq_len), slot mapping"""
spec_layer_decoding: bool = False
M = TypeVar("M")
class AttentionMetadataBuilder(abc.ABC, Generic[M]):
# Does this backend/builder support CUDA Graphs for attention.
full_cudagraph_supported: ClassVar[bool] = False
@abstractmethod
def build(self, common_prefix_len: int,
common_attn_metadata: CommonAttentionMetadata) -> M:
"""
Central method that builds attention metadata.
Some builders (MLA) require reorder_batch to be called prior to build.
"""
raise NotImplementedError
def can_run_in_cudagraph(
self, common_attn_metadata: CommonAttentionMetadata) -> bool:
"""
Can this batch (with given metadata) use CUDA Graphs for attention.
"""
return False
def build_for_cudagraph_capture(
self, common_attn_metadata: CommonAttentionMetadata) -> M:
"""
Build attention metadata for CUDA graph capture. Uses build by default.
Subclasses that override this method should call self.build or
super().build_for_cudagraph_capture.
"""
return self.build(common_prefix_len=0,
common_attn_metadata=common_attn_metadata)
def use_cascade_attention(
self,
common_prefix_len: int,
query_lens: np.ndarray,
num_query_heads: int,
num_kv_heads: int,
use_alibi: bool,
use_sliding_window: bool,
num_sms: int,
) -> bool:
return False
def reorder_batch(self, input_batch: "InputBatch",
scheduler_output: "SchedulerOutput") -> bool:
"""
This method can reorder the batch if desired by the backend.
:return: Has the batch been reordered (default False).
"""
return False
def validate_kv_sharing_target(current_layer_name, target_layer_name,
static_forward_context):
error_msg = (f"Specified KV sharing target layer for {current_layer_name} "
f"is not valid: target layer {target_layer_name} ")
if current_layer_name == target_layer_name:
raise ValueError(error_msg +
"cannot be the same as the current layer.")
if target_layer_name not in static_forward_context:
from vllm.model_executor.models.utils import extract_layer_index
# If target layer name is not in the static fwd context, it means either
# a) the target layer does not come BEFORE the current layer, or
# b) the target layer is not an Attention layer that exists in the model
current_layer_idx = extract_layer_index(current_layer_name)
target_layer_idx = extract_layer_index(target_layer_name)
if current_layer_idx <= target_layer_idx:
raise ValueError(error_msg + "must come before the current layer.")
else:
raise ValueError(error_msg +
"is not a valid Attention layer in the model.")
# Currently KV sharing is only supported between layers of the same type
target_layer_attn_type = static_forward_context[
target_layer_name].attn_type
expected = static_forward_context[current_layer_name].attn_type
if target_layer_attn_type != expected:
raise ValueError(
error_msg +
f"must be the same type as the current layer ({expected}).")
@functools.lru_cache
def get_kv_cache_layout():
# Override with format specified by the user.
cache_layout = envs.VLLM_KV_CACHE_LAYOUT
if cache_layout is None:
cache_layout = get_kv_connector_cache_layout()
else:
logger.info_once("`VLLM_KV_CACHE_LAYOUT` environment variable " \
"detected. Setting KV cache layout to %s.", cache_layout)
return cache_layout
#
# Take in `query_start_loc_np` and `seq_lens_np` and break the sequences into
# local attention blocks, where each block is passed to the attention kernel
# as an independent local ("virtual") batch item.
#
# For example, if are performing a chunked prefill a batch of 3 sequences:
# q_seqlens = [4, 10, 5]
# kv_seqlens = [6, 17, 9]
# Then normally for regular attention we would compute with an attention mask
# for batch idx 0 (q_seqlens = 4, kv_seqlens = 6) like:
# batch idx: 0 (q_seqlens = 4, kv_seqlens = 6)
# k_toks > 0 1 2 3 4 5
# q_toks v _____________
# 0 | 1 1 1
# 1 | 1 1 1 1
# 2 | 1 1 1 1 1
# 3 | 1 1 1 1 1 1
#
# for local attention (with attn_chunk_size = 4) we would compute with an
# attention mask like:
# batch idx: 0 (q_seqlens = 4, kv_seqlens = 6, attn_chunk_size = 4)
# k_toks > 0 1 2 3 4 5
# q_toks v _____________
# 0 | 1 1 1
# 1 | 1 1 1 1
# 2 | 1
# 3 | 1 1
#
# We can simulate this mask using standard flash-attention by breaking the
# sequences into local ("virtual") batches, where each local batch item is a
# local attention block, so in this case batch idx 0 would be broken up into:
#
# local-batch idx: 0 (q_seqlens = 2, kv_seqlens = 4) (batch 0)
# k_toks > 0 1 2 3
# q_toks v _____________
# 0 | 1 1 1
# 1 | 1 1 1 1
# local-batch idx: 1 (q_seqlens = 2, kv_seqlens = 2) (batch 0)
# k_toks > 4 5
# q_toks v _____________
# 2 | 1
# 3 | 1 1
#
# e.g. if we have:
# attn_chunk_size = 4
# query_start_loc_np = [0, 4, 14, 19] (q_seqlens = [4, 10, 5])
# Then this function would return:
# __b0__ ______b1______ __b2__ < orig batch indices
# q_seqlens_local = [ 2, 2, 1, 4, 4, 1, 4, 1]
# cu_seqlens_q_local = [0, 4, 6, 10, 14, 18, 19, 23, 24]
# seqlens_k_local = [ 4, 2, 4, 4, 4, 1, 4, 1]
# block_table_local : shape[local_virtual_batches, pages_per_local_batch]
def make_local_attention_virtual_batches(
attn_chunk_size: int,
query_start_loc_np: np.ndarray,
seq_lens_np: np.ndarray,
block_table: torch.Tensor,
block_size: int = 0,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, torch.Tensor]:
q_seqlens = query_start_loc_np[1:] - query_start_loc_np[:-1]
actual_batch_size = seq_lens_np.shape[0]
# Handle if we are starting in the middle of a local attention block,
# we assume q_seqlens > 0 (for all elements), for each batch idx we compute
# the number of tokens that are not in the first local attention block and
# then we can simply use a cdiv for the rest.
# For example if we have:
# attn_chunk_size = 4
# q_seqlens = [4, 10, 5]
# k_seqlens = [6, 17, 9]
# Then we would get:
# new_tokens_in_first_block = [2, 1, 4]
# local_blocks = [2, 4, 2]
q_tokens_in_first_block = np.minimum(
attn_chunk_size - ((seq_lens_np - q_seqlens) % attn_chunk_size),
q_seqlens).astype(np.int32)
tokens_in_last_block = attn_chunk_size + (seq_lens_np % -attn_chunk_size)
local_blocks = 1 + cdiv(q_seqlens - q_tokens_in_first_block,
attn_chunk_size)
# Once we know the number of local blocks we can compute the request spans
# for each batch idx, we can figure out the number of "virtual" requests we
# have to make,
# For the above example we would get:
# seqlens_q_local = [2, 2, 1, 4, 4, 1, 4, 1]
#
# First Get batched arange. (E.g., [2, 4, 2] -> [0, 1, 0, 1, 2, 3, 0, 1])
# (TODO: max a utility to share this code with _prepare_inputs)
# arange step 1. [2, 4, 2] -> [2, 6, 8]
cu_num_blocks = np.cumsum(local_blocks)
virtual_batches = cu_num_blocks[-1]
# arange step 2. [2, 6, 8] -> [0, 0, 2, 2, 2, 2, 6, 6]
block_offsets = np.repeat(cu_num_blocks - local_blocks, local_blocks)
# arange step 3. [0, 1, 0, 1, 2, 3, 0, 1]
arange = np.arange(virtual_batches, dtype=np.int32) - block_offsets
# also compute reverse arange (i.e. [1, 0, 3, 2, 1, 0, 1, 0])
rarange = np.repeat(local_blocks, local_blocks) - arange - 1
# Then we can compute the seqlens_q_local, handling the fact that the
# first and last blocks could be partial
seqlens_q_local = \
np.repeat(q_seqlens - q_tokens_in_first_block, local_blocks)
# set the first block since this may be a partial block
seqlens_q_local[arange == 0] = q_tokens_in_first_block
# set the remaining blocks
seqlens_q_local[arange > 0] = np.minimum(
seqlens_q_local - attn_chunk_size * (arange - 1),
attn_chunk_size)[arange > 0]
# convert from q_seqlens to cu_seqlens_q
cu_seqlens_q_local = np.pad(np.cumsum(seqlens_q_local), (1, 0))\
.astype(np.int32)
# compute the seqlens_k_local,
# basically a full local attention block for all but the last block in each
# batch
# For our example this will be:
# seqlens_k_local = [4, 2, 4, 4, 4, 1, 4, 1]
seqlens_k_local = np.full(cu_num_blocks[-1],
attn_chunk_size,
dtype=np.int32)
seqlens_k_local[cu_num_blocks - 1] = tokens_in_last_block
k_seqstarts_absolute = np.repeat(seq_lens_np, local_blocks) - \
(rarange * attn_chunk_size + \
np.repeat(tokens_in_last_block, local_blocks))
# For the example the local attention blocks start at:
# _b0_ _____b1_____ _b2_
# k_seqstarts_absolute = [0, 4, 4, 8, 12, 16, 4, 8]
block_starts = k_seqstarts_absolute // block_size
assert attn_chunk_size % block_size == 0, \
f"attn_chunk_size {attn_chunk_size} is not " \
f"divisible by block_size {block_size}"
pages_per_local_batch = attn_chunk_size // block_size
# Create a block_table for the local attention blocks
# For out example if we have a block-table like (assuming block_size=2):
# block_table = [
# [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], < batch 0
# [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], < batch 1
# [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], < batch 2
# ]
# Then for the local batches we would want a block-table like
# block_table_local = [
# [ 0, 1 ], < local-batch 0, (batch 0, starting from k[0])
# [ 2, 3 ], < local-batch 1, (batch 0, starting from k[4])
# [ 12, 13 ], < local-batch 2, (batch 1, starting from k[4])
# [ 14, 15 ], < local-batch 3, (batch 1, starting from k[8])
# [ 16, 17 ], < local-batch 4, (batch 1, starting from k[12])
# [ 18, 19 ], < local-batch 5, (batch 1, starting from k[16])
# [ 22, 23 ], < local-batch 6, (batch 2, starting from k[4])
# [ 24, 25 ], < local-batch 7, (batch 2, starting from k[8])
# ]
block_indices= np.broadcast_to(
np.arange(pages_per_local_batch, dtype=np.int32),
(virtual_batches, pages_per_local_batch)) \
+ np.expand_dims(block_starts, axis=1)
block_indices = block_indices.flatten().clip(max=block_table.shape[1] - 1)
batch_indices = np.repeat(np.arange(actual_batch_size, dtype=np.int32),
local_blocks * pages_per_local_batch)
block_table_local = block_table[batch_indices, block_indices]\
.view(virtual_batches, -1)
return seqlens_q_local, cu_seqlens_q_local, seqlens_k_local, \
block_table_local