Files
2026-04-24 09:58:03 +08:00

193 lines
7.6 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
from math import lcm
from typing import TYPE_CHECKING
import vllm.envs as envs
from vllm.logger import init_logger
from vllm.model_executor.models import ModelRegistry
from vllm.platforms import current_platform
from vllm.utils.math_utils import cdiv, round_up
from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
from vllm_mlu.mlu_hijack_utils import MluHijackObject
from vllm.model_executor.models.config import (HybridAttentionMambaModelConfig,
MambaModelConfig)
from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec, MLAAttentionSpec
if TYPE_CHECKING:
from vllm.config import VllmConfig
logger = init_logger(__name__)
@classmethod
def vllm__module_executor__models__config__HybridAttentionMambaModelConfig__verify_and_update_config(
cls,
vllm_config: "VllmConfig"
) -> None:
"""
Ensure that page size of attention layers is greater than or
equal to the mamba layers. If not, automatically set the attention
block size to ensure that it is. If the attention page size is
strictly greater than the mamba page size, we pad the mamba page size
to make them equal.
Args:
vllm_config: vLLM Config
"""
# Save the user input before it gets modified by MambaModelConfig
mamba_block_size = vllm_config.cache_config.mamba_block_size
# Enable FULL_AND_PIECEWISE by default
MambaModelConfig.verify_and_update_config(vllm_config)
cache_config = vllm_config.cache_config
model_config = vllm_config.model_config
parallel_config = vllm_config.parallel_config
if cache_config.cache_dtype == "auto":
kv_cache_dtype = model_config.dtype
else:
kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
# get attention page size (for 1 token)
# Attention backend constraints:
# - FlashAttention (FA) requires block size to be multiple of 16
# - MLA (Multi-head Latent Attention) requires larger alignment:
# * CUTLASS_MLA backend: kernel_block_size 128 alignment
# * Other MLA backends: kernel_block_size 64 alignment
if model_config.use_mla:
use_cutlass_mla = envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA"
kernel_block_alignment_size = 128 if use_cutlass_mla else 64
attn_page_size_1_token = MLAAttentionSpec(
block_size=1,
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
head_size=model_config.get_head_size(),
dtype=kv_cache_dtype,
).page_size_bytes
else:
kernel_block_alignment_size = 16
if (
current_platform.is_device_capability(100)
and model_config.get_head_size() == 256
and (
envs.VLLM_ATTENTION_BACKEND is None
or envs.VLLM_ATTENTION_BACKEND == "FLASHINFER"
)
):
# https://github.com/flashinfer-ai/flashinfer/issues/1993 reports that`
# head size 256 and block size 16 is not supported on blackwell.
kernel_block_alignment_size = 32
attn_page_size_1_token = FullAttentionSpec(
block_size=1,
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
head_size=model_config.get_head_size(),
dtype=kv_cache_dtype,
).page_size_bytes
model_cls, _ = ModelRegistry.resolve_model_cls(
model_config.architecture,
model_config=model_config,
)
# get mamba page size
mamba_page_size = MambaSpec(
shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
block_size=model_config.max_model_len,
).page_size_bytes
# Model may be marked as is_hybrid
# but mamba is skipped via config,
# return directly
if mamba_page_size == 0:
return
if cache_config.enable_prefix_caching:
# With prefix caching, select attention block size to
# optimize for mamba kernel performance
# Mamba2 SSD kernel uses a chunk_size, e.g. 256
# Align the block to the kernel: use lowest multiple of chunk_size
# of attention tokens that would fit mamba_page_size:
# e.g. for mamba page size = 788kB
# attn_1_token = 2kB -> fits ~394 tokens
# then round up to a mulitple of 256 -> 512 tokens
# End result:
# attn_block_size = 512
# mamba_block_size = 512 (aligned to a multiple of chunk_size)
# TODO(tdoublep): this constraint can be relaxed fairly
# easily by changing the way we layout chunks in the
# mamba2 kernels.
base_chunk_size = mamba_block_size or model_config.get_mamba_chunk_size()
attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)
attn_block_size = chunk_size * cdiv(attn_tokens_per_mamba_state, chunk_size)
cache_config.mamba_block_size = attn_block_size
else:
# Without prefix caching, select minimum valid attention block size
# to minimize mamba state padding
# Calculate minimum attention block size that satisfies both:
# 1. Backend alignment requirements (kernel_block_alignment_size)
# 2. Mamba page size compatibility (attn_page_size >= mamba_page_size)
attn_block_size = kernel_block_alignment_size * cdiv(
mamba_page_size, kernel_block_alignment_size * attn_page_size_1_token
)
'''
=============================
Modify by vllm_mlu
=============================
@brief: support qwen3-next
'''
if (vllm_config.mlu_config.enable_mamba_split_page_size):
vllm_config.mlu_config.mamba_to_attn_block_ratio = cdiv(attn_block_size, cache_config.block_size)
cache_config.mamba_page_size_padded = cache_config.block_size * attn_page_size_1_token
return
'''
==================
End of MLU Hijack
==================
'''
# override attention block size if either (a) the
# user has not set it or (b) the user has set it
# too small.
if cache_config.block_size is None or cache_config.block_size < attn_block_size:
cache_config.block_size = attn_block_size
logger.info(
"Setting attention block size to %d tokens "
"to ensure that attention page size is >= mamba page size.",
attn_block_size,
)
# compute new attention page size
attn_page_size = cache_config.block_size * attn_page_size_1_token
assert attn_page_size >= mamba_page_size
if attn_page_size == mamba_page_size:
# don't need to pad mamba page size
return
# pad mamba page size to exactly match attention
if (
cache_config.mamba_page_size_padded is None
or cache_config.mamba_page_size_padded != attn_page_size
):
cache_config.mamba_page_size_padded = attn_page_size
mamba_padding_pct = (
100 * (attn_page_size - mamba_page_size) / mamba_page_size
)
logger.info(
"Padding mamba page size by %.2f%% to ensure "
"that mamba page size and attention page size are "
"exactly equal.",
mamba_padding_pct,
)
MluHijackObject.apply_hijack(HybridAttentionMambaModelConfig,
HybridAttentionMambaModelConfig.verify_and_update_config,
vllm__module_executor__models__config__HybridAttentionMambaModelConfig__verify_and_update_config)