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vllm/vllm/v1/attention/backends/utils.py
Maximilien de Bayser 1cd6eaba54
Support encoder-only models without KV-Cache (#21270) 
Signed-off-by: Max de Bayser <maxdebayser@gmail.com>
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Russell Bryant <rbryant@redhat.com>
2025-07-26 21:09:52 +08:00

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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, Optional, TypeVar
import numpy as np
import torch
from vllm.attention.layer import Attention
from vllm.config import VllmConfig, get_layers_from_vllm_config
from vllm.utils import cdiv
if TYPE_CHECKING:
from vllm.attention.backends.abstract import AttentionImpl
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
from vllm.v1.kv_cache_interface import AttentionSpec
logger = init_logger(__name__)
_KV_CACHE_LAYOUT_OVERRIDE = None
@dataclass
class CommonAttentionMetadata:
"""
Per-batch attention metadata, shared across layers and backends.
AttentionMetadataBuilder instances use it to construct per-layer metadata.
For many of the tensors we keep both GPU and CPU versions.
"""
query_start_loc: torch.Tensor
query_start_loc_cpu: torch.Tensor
"""(batch_size + 1,), the start location of each request in query Tensor"""
seq_lens: torch.Tensor
seq_lens_cpu: torch.Tensor
"""(batch_size,), the length of each request including both computed tokens
and newly scheduled tokens"""
num_computed_tokens_cpu: torch.Tensor
"""(batch_size,), the number of computed tokens for each request"""
num_reqs: int
"""Number of requests"""
num_actual_tokens: int
"""Total number of tokens in batch"""
max_query_len: int
"""Longest query in batch"""
block_table_tensor: torch.Tensor
slot_mapping: torch.Tensor
causal: bool = True
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 __init__(self, kv_cache_spec: AttentionSpec, vllm_config: VllmConfig,
device: torch.device):
self.kv_cache_spec = kv_cache_spec
@abstractmethod
def build(self,
common_prefix_len: int,
common_attn_metadata: CommonAttentionMetadata,
fast_build: bool = False) -> M:
"""
Central method that builds attention metadata.
Some builders (MLA) require reorder_batch to be called prior to build.
Args:
common_prefix_len: The length of the common prefix of the batch.
common_attn_metadata: The common attention metadata.
fast_build: The meta-data will prioritize speed of building over
then speed at execution. Can be used for spec-decode where the
result of a build call may only be used for few layers/iters.
"""
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,
use_local_attention: 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
@functools.lru_cache
def get_kv_cache_layout():
global _KV_CACHE_LAYOUT_OVERRIDE
# 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)
if _KV_CACHE_LAYOUT_OVERRIDE is not None:
cache_layout = _KV_CACHE_LAYOUT_OVERRIDE
return cache_layout
def set_kv_cache_layout(cache_layout: str):
global _KV_CACHE_LAYOUT_OVERRIDE
_KV_CACHE_LAYOUT_OVERRIDE = cache_layout
@dataclass
class PerLayerParameters:
"""
Currently, FlashInfer backend only support models in which all layers share
the same values for the following hyperparameters.
"""
window_left: int
logits_soft_cap: Optional[float]
sm_scale: float
def get_per_layer_parameters(
vllm_config: VllmConfig,
cls_: type['AttentionImpl']) -> dict[str, PerLayerParameters]:
"""
Scan all attention layers and determine some hyperparameters
to use during `plan`.
"""
layers = get_layers_from_vllm_config(vllm_config, Attention)
per_layer_params: dict[str, PerLayerParameters] = {}
for key, layer in layers.items():
impl = layer.impl
assert isinstance(impl, cls_)
# Infer hyperparameters from the attention layer
window_size = getattr(impl, "sliding_window", None)
window_left = window_size[0] if window_size is not None else -1
logits_soft_cap = getattr(impl, "logits_soft_cap", None)
sm_scale = impl.scale
per_layer_params[key] = PerLayerParameters(window_left,
logits_soft_cap, sm_scale)
return per_layer_params
def infer_global_hyperparameters(
per_layer_params: dict[str, PerLayerParameters]) -> PerLayerParameters:
"""
Currently, FlashInfer backend only support models in which all layers share
the same values for the following hyperparameters:
- `window_left`
- `logits_soft_cap`
- `sm_scale`
So this function asserts that all layers share the same values for these
hyperparameters and returns the global values.
"""
assert len(per_layer_params) > 0, "No attention layers found in the model."
param_sets = list(per_layer_params.values())
global_params = param_sets[0]
for params in param_sets:
assert params == global_params, (
"FlashInfer backend currently only supports models in which all "
"layers share the same values for the following hyperparameters: "
"`window_left`, `logits_soft_cap`, `sm_scale`.")
return global_params
#
# 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,
common_attn_metadata: CommonAttentionMetadata,
block_size: int = 0,
) -> CommonAttentionMetadata:
query_start_loc_np = common_attn_metadata.query_start_loc_cpu.numpy()
seq_lens_np = common_attn_metadata.seq_lens_cpu.numpy()
block_table = common_attn_metadata.block_table_tensor
device = common_attn_metadata.query_start_loc.device
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
num_computed_tokens_local = seqlens_k_local - seqlens_q_local
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)
query_start_loc_cpu = torch.from_numpy(cu_seqlens_q_local)
seq_lens_cpu = torch.from_numpy(seqlens_k_local)
return CommonAttentionMetadata(
query_start_loc_cpu=query_start_loc_cpu,
query_start_loc=query_start_loc_cpu.to(device=device,
non_blocking=True),
seq_lens_cpu=seq_lens_cpu,
seq_lens=seq_lens_cpu.to(device=device, non_blocking=True),
num_computed_tokens_cpu=torch.from_numpy(num_computed_tokens_local),
num_reqs=len(seq_lens_cpu),
num_actual_tokens=common_attn_metadata.num_actual_tokens,
max_query_len=seqlens_q_local.max(),
block_table_tensor=block_table_local,
slot_mapping=common_attn_metadata.slot_mapping,
causal=True,
)
def split_decodes_and_prefills(
common_attn_metadata: CommonAttentionMetadata,
decode_threshold: int = 1,
) -> tuple[int, int, int, int]:
"""
Assuming a reordered batch, finds the boundary between prefill and decode
requests.
Args:
common_attn_metadata: CommonAttentionMetadata object containing the
batch metadata.
decode_threshold: The maximum query length to be considered a decode.
Returns:
num_decodes: The number of decode requests.
num_prefills: The number of prefill requests.
num_decode_tokens: The number of tokens in the decode requests.
num_prefill_tokens: The number of tokens in the prefill requests.
"""
max_query_len = common_attn_metadata.max_query_len
num_reqs = common_attn_metadata.num_reqs
num_tokens = common_attn_metadata.num_actual_tokens
query_start_loc = common_attn_metadata.query_start_loc_cpu
if max_query_len <= decode_threshold:
return num_reqs, 0, num_tokens, 0
query_lens = query_start_loc[1:] - query_start_loc[:-1]
is_prefill = query_lens > decode_threshold
if not torch.any(is_prefill):
return num_reqs, 0, num_tokens, 0
first_prefill = is_prefill.int().argmax(dim=-1).item()
assert torch.all(query_lens[first_prefill:] > decode_threshold)
assert torch.all(query_lens[:first_prefill] <= decode_threshold)
num_decodes = first_prefill
num_prefills = num_reqs - num_decodes
num_decode_tokens = query_start_loc[first_prefill].item()
num_prefill_tokens = num_tokens - num_decode_tokens
return (num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens)
def reorder_batch_to_split_decodes_and_prefills(
input_batch: "InputBatch",
scheduler_output: "SchedulerOutput",
decode_threshold: int = 1,
) -> bool:
"""
Reorders the batch to split into prefill and decode requests; places all
requests with <= decode_threshold tokens at the front of the batch.
Returns:
True if the batch was modified, False otherwise.
"""
# We now want to reorder the batch so that the "decode" requests are at
# the front and the "prefill" requests are at the back using the least
# amount of swaps possible. (NOTE for now we loosely use "decode" to mean
# requests where attention is likely memory-bound and "prefill" to mean
# requests where attention is likely compute-bound, TODO(lucas): figure out
# a better naming here)
decodes = []
prefills = []
num_decode_tokens = 0
num_prefill_tokens = 0
for i, req_id in enumerate(input_batch.req_ids):
num_tokens = scheduler_output.num_scheduled_tokens[req_id]
# for now treat 1 scheduled token as "decode" even if its not,
# we should update this to something like < 8 in the future but
# currently the TritonMLA._forward_decode only supports
# num_tokens = 1
if num_tokens <= decode_threshold:
decodes.append(i)
num_decode_tokens += num_tokens
else:
prefills.append(i)
num_prefill_tokens += num_tokens
# We hope that this is fairly minimal since decodes
# should be around for a number of iterations so hopefully they are
# relatively stationary (and new request are generally appended to the
# persistent batch so already should be at the back)
# To achieve this we loop over the decodes in descending order and
# the prefills in ascending order. We swap decodes from the "back"
# i.e. past where the last decode should be in the reodorered with
# prefills from the front of the batch.
# `decodes` and `prefills` are already in ascending order just based on
# the above loop
num_decodes = len(decodes)
num_prefills = len(prefills)
modified_batch = False
for i in range(1, min(num_decodes, num_prefills) + 1):
# If the decode is at the "back" of the batch, i, we can swap it
# with the prefill closest to the front of the batch
decode_idx = decodes[num_decodes - i]
if decode_idx < num_decodes:
break
input_batch.swap_states(prefills[i - 1], decode_idx)
modified_batch = True
return modified_batch
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