# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from __future__ import annotations import copy from collections import Counter from dataclasses import dataclass, fields, replace from enum import Enum, IntEnum from math import prod from typing import TYPE_CHECKING import torch from typing_extensions import Self from vllm.logger import init_logger from vllm.utils.math_utils import cdiv, round_up from vllm.utils.torch_utils import get_dtype_size, nvfp4_kv_cache_full_dim from vllm.v1.attention.backends.registry import MambaAttentionBackendEnum from vllm.v1.kv_cache_spec_registry import KVCacheSpecRegistry if TYPE_CHECKING: from vllm.config import VllmConfig logger = init_logger(__name__) # --------------------------------------------------------------------------- # KV cache quantization mode # --------------------------------------------------------------------------- class KVQuantMode(IntEnum): """KV cache quantization mode. Used by attention backends and kernels to dispatch quantization logic without string matching on ``kv_cache_dtype``. """ NONE = 0 FP8_PER_TENSOR = 1 # per-tensor scales (current fp8 path) INT8_PER_TOKEN_HEAD = 2 # per-token-head dynamic scales for int8 FP8_PER_TOKEN_HEAD = 3 # per-token-head dynamic scales for fp8 INT4_PER_TOKEN_HEAD = 4 # packed 2×int4/byte, RHT + asymmetric zp NVFP4 = 5 # packed fp4 data + fp8 block scales @property def is_per_token_head(self) -> bool: """True for any per-token-head quantization mode.""" return self in ( KVQuantMode.INT8_PER_TOKEN_HEAD, KVQuantMode.FP8_PER_TOKEN_HEAD, KVQuantMode.INT4_PER_TOKEN_HEAD, ) @property def is_nvfp4(self) -> bool: """True for NVFP4 packed quantization mode.""" return self == KVQuantMode.NVFP4 def get_kv_quant_mode(kv_cache_dtype: str) -> KVQuantMode: """Map a ``kv_cache_dtype`` string to a :class:`KVQuantMode`.""" if kv_cache_dtype == "int4_per_token_head": return KVQuantMode.INT4_PER_TOKEN_HEAD if kv_cache_dtype == "int8_per_token_head": return KVQuantMode.INT8_PER_TOKEN_HEAD if kv_cache_dtype == "fp8_per_token_head": return KVQuantMode.FP8_PER_TOKEN_HEAD if kv_cache_dtype == "nvfp4": return KVQuantMode.NVFP4 if isinstance(kv_cache_dtype, str) and kv_cache_dtype.startswith("fp8"): return KVQuantMode.FP8_PER_TENSOR return KVQuantMode.NONE def is_quantized_kv_cache(kv_cache_dtype: str) -> bool: return get_kv_quant_mode(kv_cache_dtype) != KVQuantMode.NONE def kv_cache_uses_per_token_head_scales(kv_cache_dtype: str) -> bool: """Return True if *kv_cache_dtype* needs per-token-head scales.""" return get_kv_quant_mode(kv_cache_dtype).is_per_token_head class KVCacheSpecKind(str, Enum): FULL_ATTENTION = "full_attention" MLA_ATTENTION = "mla_attention" SLIDING_WINDOW = "sliding_window" SLIDING_WINDOW_MLA = "sliding_window_mla" MAMBA = "mamba" CHUNKED_LOCAL_ATTENTION = "chunked_local_attention" SINK_FULL_ATTENTION = "sink_full_attention" ENCODER_ONLY_ATTENTION = "encoder_only_attention" CROSS_ATTENTION = "cross_attention" UNKNOWN = "unknown" @dataclass(frozen=True) class KVCacheSpec: """ A base class for specifying the KV cache format of one layer. """ # number of tokens in a block block_size: int @property def page_size_bytes(self) -> int: """ The size of a page with `block_size` tokens in bytes. Returns: The page size """ raise NotImplementedError @property def storage_block_size(self) -> int: return self.block_size def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: """ The maximum possible memory usage of this KV cache in bytes. Returns: The KV cache size in bytes """ raise NotImplementedError def max_num_blocks_per_req(self, vllm_config: VllmConfig, max_len: int) -> int: """ The number of block table entries needed per request, i.e. the row length of the worker-side block table for this cache group. Args: vllm_config: The vllm config. max_len: The maximum sequence length to size for, including the encoder length for encoder-decoder models. """ return cdiv(max_len, self.block_size) def copy_with_new_block_size(self, block_size: int) -> Self: """ Create a new KVCacheSpec from self but replacing the block size. """ return replace(self, block_size=block_size) @classmethod def merge(cls, specs: list[Self]) -> Self: """ Merge a list of KVCacheSpec objects into a single KVCacheSpec object. """ assert all(spec == specs[0] for spec in specs[1:]), ( "All layers in the same KV cache group must be the same." ) return copy.deepcopy(specs[0]) def is_uniform_with_collection( self, kv_cache_specs: dict[str, KVCacheSpec] ) -> bool: """ Whether this KVCacheSpec is uniform with all specs of all layers. """ uniform_type_base_spec = KVCacheSpecRegistry.get_uniform_type_base_spec(self) assert uniform_type_base_spec is not None, ( f"Unsupported KV cache spec type: {type(self)}. " "Please register it using @register_kv_cache_spec decorator." ) return all( isinstance(spec, uniform_type_base_spec) for spec in kv_cache_specs.values() ) @dataclass(frozen=True, kw_only=True) class AttentionSpec(KVCacheSpec): num_kv_heads: int head_size: int dtype: torch.dtype kv_quant_mode: KVQuantMode = KVQuantMode.NONE page_size_padded: int | None = None indexes_kv_by_block_stride: bool = False @property def page_size_bytes(self) -> int: real_page_size = self.real_page_size_bytes # Per-token-head scales are stored in separate tensors managed # by the attention backend, but the memory is carved from the # raw KV cache allocation so it must be budgeted here. if self.kv_quant_mode.is_per_token_head: real_page_size += ( 2 * self.block_size * self.num_kv_heads * get_dtype_size(torch.float32) ) if self.page_size_padded is not None: assert self.page_size_padded >= real_page_size return self.page_size_padded return real_page_size @property def real_page_size_bytes(self) -> int: if self.kv_quant_mode.is_nvfp4: # Packed layout: fp4 data + fp8 block scales per head. head_dim = nvfp4_kv_cache_full_dim(self.head_size) elif self.kv_quant_mode == KVQuantMode.INT4_PER_TOKEN_HEAD: head_dim = self.head_size // 2 else: head_dim = self.head_size return ( 2 * self.block_size * self.num_kv_heads * head_dim * get_dtype_size(self.dtype) ) def max_num_blocks_per_req(self, vllm_config: VllmConfig, max_len: int) -> int: # Attention KV is token-interleaved across DCP/PCP ranks, so each rank # only stores max_len // (dcp * pcp) tokens per request. parallel_config = vllm_config.parallel_config total_cp_size = ( parallel_config.decode_context_parallel_size * parallel_config.prefill_context_parallel_size ) return cdiv(max_len, self.block_size * total_cp_size) @dataclass(frozen=True, kw_only=True) class FullAttentionSpec(AttentionSpec): """ When hybrid allocator is disabled and the model contains both full attention layers and sliding window attention layers, sliding window attention are regarded as full attention in KV cache manager (blocks are allocated for all tokens), while computed as sliding window attention in model runner. In this case, we use FullAttentionSpec and record the sliding window size. """ head_size_v: int = None # type: ignore[assignment] sliding_window: int | None = None """ Default to None for not using sliding window attention. """ attention_chunk_size: int | None = None non_causal: bool = False """ Whether the layer attends non-causally (e.g. Prefix LM). Carried on the spec so the engine core, which collects specs from all workers before the scheduler is built, can adjust scheduling policy (chunked prefill / prefix caching) regardless of tensor-parallel layout. It does not affect the KV cache layout itself. """ def __post_init__(self): if self.head_size_v is None: object.__setattr__(self, "head_size_v", self.head_size) def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: max_model_len = vllm_config.model_config.max_model_len dcp_world_size = vllm_config.parallel_config.decode_context_parallel_size pcp_world_size = vllm_config.parallel_config.prefill_context_parallel_size # Note(hc): each dcp rank only need save # (max_model_len//dcp_world_size) tokens locally. if dcp_world_size * pcp_world_size > 1: max_model_len = cdiv(max_model_len, dcp_world_size * pcp_world_size) return cdiv(max_model_len, self.block_size) * self.page_size_bytes @classmethod def merge_window_sizes(cls, window_sizes: set[int]) -> int | None: if len(window_sizes) == 0: return None elif len(window_sizes) == 1: return window_sizes.pop() else: raise ValueError( "All attention layers in the same KV cache group must have the " "same window size." ) @classmethod def merge(cls, specs: list[Self]) -> Self: """ Merge a list of FullAttentionSpec objects into a single FullAttentionSpec object. """ assert all(isinstance(spec, FullAttentionSpec) for spec in specs), ( "All attention layers in the same KV cache group must be FullAttentionSpec." ) sliding_window = set( spec.sliding_window for spec in specs if spec.sliding_window is not None ) attention_chunk_size = set( spec.attention_chunk_size for spec in specs if spec.attention_chunk_size is not None ) assert not any(isinstance(spec, MLAAttentionSpec) for spec in specs), ( "MLAAttentionSpec should be merged in MLAAttentionSpec.merge" ) merged_spec = cls( block_size=specs[0].block_size, num_kv_heads=specs[0].num_kv_heads, head_size=specs[0].head_size, head_size_v=specs[0].head_size_v, dtype=specs[0].dtype, kv_quant_mode=specs[0].kv_quant_mode, page_size_padded=specs[0].page_size_padded, indexes_kv_by_block_stride=specs[0].indexes_kv_by_block_stride, sliding_window=cls.merge_window_sizes(sliding_window), attention_chunk_size=cls.merge_window_sizes(attention_chunk_size), # If any layer in the group is non-causal, treat the group as # non-causal so the engine core disables incompatible scheduling. non_causal=any(spec.non_causal for spec in specs), ) for spec in specs: for f in fields(AttentionSpec): assert getattr(spec, f.name) == getattr(merged_spec, f.name), ( "All attention layers in the same KV cache group must have " "the same attention spec." ) assert (merged_spec.sliding_window is not None) + ( merged_spec.attention_chunk_size is not None ) <= 1, ( "Model with both sliding window layers and chunked local attention " "layers is not supported." ) return merged_spec @property def real_page_size_bytes(self) -> int: if self.kv_quant_mode.is_nvfp4: # Packed layout per head: fp4 data + fp8 block scales. # fp4 data: head_size//2 bytes (2 fp4 values per byte) # fp8 block scale: head_size//16 bytes (1 scale per 16 elements) last_dim = nvfp4_kv_cache_full_dim( self.head_size ) + nvfp4_kv_cache_full_dim(self.head_size_v) elif self.kv_quant_mode == KVQuantMode.INT4_PER_TOKEN_HEAD: last_dim = self.head_size // 2 + self.head_size_v // 2 else: last_dim = self.head_size + self.head_size_v return ( self.block_size * self.num_kv_heads * last_dim * get_dtype_size(self.dtype) ) def _apply_alignment_padding(spec: MLAAttentionSpec | SlidingWindowMLASpec): if spec.alignment is None: return actual_page_size = spec.real_page_size_bytes padded_page_size = round_up(actual_page_size, spec.alignment) if padded_page_size != actual_page_size: object.__setattr__(spec, "page_size_padded", padded_page_size) @dataclass(frozen=True, kw_only=True) class TQFullAttentionSpec(FullAttentionSpec): """FullAttentionSpec with TQ-aware page size. Python equivalent of the C++ TQ4FullAttentionSpec. Overrides real_page_size_bytes to use TQ slot bytes instead of the raw head_size * dtype formula. """ tq_slot_size: int = 0 @property def real_page_size_bytes(self) -> int: if self.tq_slot_size > 0: return self.block_size * self.num_kv_heads * self.tq_slot_size return super().real_page_size_bytes @classmethod def merge(cls, specs: list[Self]) -> Self: merged = super().merge(specs) assert all(s.tq_slot_size == specs[0].tq_slot_size for s in specs), ( "All TQ layers in the same KV cache group must use the same tq_slot_size." ) return replace(merged, tq_slot_size=specs[0].tq_slot_size) @dataclass(frozen=True, kw_only=True) class MLAAttentionSpec(FullAttentionSpec): # TODO(Lucas/Chen): less hacky way to do this cache_dtype_str: str | None = None # DeepseekV4 only fields. Non-DeepseekV4 MLA models leave these at defaults. alignment: int | None = None # Default to None for no padding. compress_ratio: int = 1 # Default to 1 for no compression. model_version: str | None = None def __post_init__(self): super().__post_init__() _apply_alignment_padding(self) @property def storage_block_size(self) -> int: return self.block_size // self.compress_ratio @property def real_page_size_bytes(self) -> int: if self.cache_dtype_str == "fp8_ds_mla": if self.model_version == "deepseek_v4": # DeepseekV4: 448B NoPE + 128B RoPE + 8B fp8 scale = 584B per token. # head_size stays semantic (512); bytes are determined here. return self.storage_block_size * 584 # V3.2 main MLA: 656-byte custom layout (kv_lora_rank=512 + # qk_rope_head_dim=64, head_size=576). See flashmla_sparse.py. return self.block_size * 656 if self.kv_quant_mode == KVQuantMode.INT4_PER_TOKEN_HEAD: head_dim = self.head_size // 2 else: head_dim = self.head_size return ( self.storage_block_size * self.num_kv_heads * head_dim * get_dtype_size(self.dtype) ) @classmethod def merge(cls, specs: list[Self]) -> Self: assert all(isinstance(spec, MLAAttentionSpec) for spec in specs), ( "All attention layers in the same KV cache group must be MLAAttentionSpec." ) cache_dtype_str_set = set(spec.cache_dtype_str for spec in specs) compress_ratio_set = set(spec.compress_ratio for spec in specs) model_version_set = set(spec.model_version for spec in specs) block_stride_set = set(spec.indexes_kv_by_block_stride for spec in specs) assert ( len(cache_dtype_str_set) == 1 and len(compress_ratio_set) == 1 and len(model_version_set) == 1 and len(block_stride_set) == 1 ), ( "All attention layers in the same KV cache group must use the same " "quantization method, compress ratio, model version, and KV block " "stride indexing." ) return cls( block_size=specs[0].block_size, num_kv_heads=specs[0].num_kv_heads, head_size=specs[0].head_size, dtype=specs[0].dtype, kv_quant_mode=specs[0].kv_quant_mode, page_size_padded=specs[0].page_size_padded, indexes_kv_by_block_stride=block_stride_set.pop(), cache_dtype_str=cache_dtype_str_set.pop(), compress_ratio=compress_ratio_set.pop(), model_version=model_version_set.pop(), ) @dataclass(frozen=True, kw_only=True) class HiddenStateCacheSpec(MLAAttentionSpec): """Marker for hidden-state cache layers used by extract_hidden_states.""" pass @dataclass(frozen=True, kw_only=True) class RSWASpec(FullAttentionSpec): """KV cache spec for Reference Sliding Window Attention (R-SWA). Prefill (image + text prompt) tokens are always globally visible. Only the last ``rswa_window`` generated tokens are kept in the KV cache; gap blocks (between the prefill tail and the current decode window) are evicted during each decode step to bound memory at O(prefix_blocks + window_blocks). """ rswa_window: int @classmethod def merge(cls, specs: list[RSWASpec]) -> RSWASpec: assert all(isinstance(spec, RSWASpec) for spec in specs), ( "All attention layers in the same KV cache group must be RSWASpec." ) rswa_windows = {spec.rswa_window for spec in specs} assert len(rswa_windows) == 1, ( f"All R-SWA layers must share the same rswa_window, got {rswa_windows}" ) # Delegate common field merging to the parent, then reattach rswa_window. base = FullAttentionSpec.merge(specs) # type: ignore[arg-type] return cls( block_size=base.block_size, num_kv_heads=base.num_kv_heads, head_size=base.head_size, head_size_v=base.head_size_v, dtype=base.dtype, kv_quant_mode=base.kv_quant_mode, page_size_padded=base.page_size_padded, indexes_kv_by_block_stride=base.indexes_kv_by_block_stride, sliding_window=base.sliding_window, attention_chunk_size=base.attention_chunk_size, non_causal=base.non_causal, rswa_window=rswa_windows.pop(), ) @dataclass(frozen=True, kw_only=True) class ChunkedLocalAttentionSpec(AttentionSpec): attention_chunk_size: int def max_admission_blocks_per_request( self, max_in_flight_tokens: int, max_model_len: int ) -> int: """Per-request admission cap, in blocks. Single source of truth for both startup pool sizing (`max_memory_usage_bytes`) and the runtime admission gate, so requests admitted by startup can also be admitted at runtime. `max_in_flight_tokens` is the max tokens scheduled but not yet settled (one batch per concurrent step); see `VllmConfig.max_in_flight_tokens`. """ # During chunked prefill, we hold KV for at most one chunk window plus # the in-flight tokens, since frees happen on the processed-token basis. num_tokens = min( self.attention_chunk_size + max_in_flight_tokens, max_model_len ) return cdiv(num_tokens, self.block_size) def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: max_blocks = self.max_admission_blocks_per_request( max_in_flight_tokens=vllm_config.max_in_flight_tokens, max_model_len=vllm_config.model_config.max_model_len, ) return max_blocks * self.page_size_bytes def is_uniform_with_collection( self, kv_cache_specs: dict[str, KVCacheSpec] ) -> bool: return all( isinstance(spec, ChunkedLocalAttentionSpec) and spec.attention_chunk_size == self.attention_chunk_size for spec in kv_cache_specs.values() ) @dataclass(frozen=True, kw_only=True) class SlidingWindowSpec(AttentionSpec): sliding_window: int head_size_v: int = None # type: ignore[assignment] def __post_init__(self): if self.head_size_v is None: object.__setattr__(self, "head_size_v", self.head_size) @property def real_page_size_bytes(self) -> int: # Mirror ``FullAttentionSpec.real_page_size_bytes`` for NVFP4 KV cache. if self.kv_quant_mode.is_nvfp4: last_dim = nvfp4_kv_cache_full_dim( self.head_size ) + nvfp4_kv_cache_full_dim(self.head_size_v) return ( self.block_size * self.num_kv_heads * last_dim * get_dtype_size(self.dtype) ) return ( self.block_size * self.num_kv_heads * (self.head_size + self.head_size_v) * get_dtype_size(self.dtype) ) def max_admission_blocks_per_request( self, max_in_flight_tokens: int, max_model_len: int ) -> int: """Per-request admission cap, in blocks. Single source of truth for both startup pool sizing (`max_memory_usage_bytes`) and the runtime admission gate. Per-request real-held blocks plateau at this bound because `SlidingWindowManager.remove_skipped_blocks` runs from `allocate_slots` before each chunk's `get_num_blocks_to_allocate`. `max_in_flight_tokens` is the max tokens scheduled but not yet settled (one batch per concurrent step); see `VllmConfig.max_in_flight_tokens`. """ # During chunked prefill, we hold KV for the last `sliding_window-1` # computed tokens plus the in-flight tokens (frees happen on the # processed-token basis); never more than `max_model_len`. num_tokens = min(self.sliding_window - 1 + max_in_flight_tokens, max_model_len) # +1 because the sliding window may not start from the beginning of # the block. E.g. block size 4 and num_token 4 needs two blocks # [XXCD][EF] to store the 6-token window [CDEF]. return cdiv(num_tokens, self.block_size) + 1 def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: assert vllm_config.parallel_config.decode_context_parallel_size == 1, ( "DCP not support sliding window." ) max_blocks = self.max_admission_blocks_per_request( max_in_flight_tokens=vllm_config.max_in_flight_tokens, max_model_len=vllm_config.model_config.max_model_len, ) return max_blocks * self.page_size_bytes def is_uniform_with_collection( self, kv_cache_specs: dict[str, KVCacheSpec] ) -> bool: return all( isinstance(spec, SlidingWindowSpec) and spec.sliding_window == self.sliding_window for spec in kv_cache_specs.values() ) @dataclass(frozen=True, kw_only=True) class SlidingWindowMLASpec(SlidingWindowSpec): """Sliding window attention with MLA cache format.""" cache_dtype_str: str | None = None # DeepseekV4-only: see MLAAttentionSpec.model_version. alignment: int | None = None # Default to None for no padding. compress_ratio: int = 1 model_version: str | None = None def __post_init__(self): _apply_alignment_padding(self) @property def storage_block_size(self) -> int: return self.block_size // self.compress_ratio @property def real_page_size_bytes(self) -> int: if self.model_version == "deepseek_v4" and self.cache_dtype_str == "fp8_ds_mla": # DeepseekV4 FlashMLA: 448B NoPE + 128B RoPE + 8B fp8 scale = 584B # per token. FlashInfer's contiguous bf16/fp8 cache falls through to # the element-size formula below. return self.storage_block_size * 584 assert self.model_version in (None, "deepseek_v4"), ( f"Unsupported model version: {self.model_version}" ) return ( self.storage_block_size * self.num_kv_heads * self.head_size * get_dtype_size(self.dtype) ) @classmethod def merge(cls, specs: list[Self]) -> Self: assert all(isinstance(spec, SlidingWindowMLASpec) for spec in specs), ( "All attention layers in the same KV cache group must be " "SlidingWindowMLASpec." ) cache_dtype_str_set = set(spec.cache_dtype_str for spec in specs) compress_ratio_set = set(spec.compress_ratio for spec in specs) model_version_set = set(spec.model_version for spec in specs) sliding_window_set = set(spec.sliding_window for spec in specs) block_stride_set = set(spec.indexes_kv_by_block_stride for spec in specs) assert ( len(cache_dtype_str_set) == 1 and len(compress_ratio_set) == 1 and len(model_version_set) == 1 and len(sliding_window_set) == 1 and len(block_stride_set) == 1 ), ( "All attention layers in the same KV cache group must use the same " "quantization method, compress ratio, model version, sliding " "window size, and KV block stride indexing." ) return cls( block_size=specs[0].block_size, num_kv_heads=specs[0].num_kv_heads, head_size=specs[0].head_size, dtype=specs[0].dtype, page_size_padded=specs[0].page_size_padded, indexes_kv_by_block_stride=block_stride_set.pop(), sliding_window=sliding_window_set.pop(), cache_dtype_str=cache_dtype_str_set.pop(), compress_ratio=compress_ratio_set.pop(), model_version=model_version_set.pop(), ) def is_uniform_with_collection( self, kv_cache_specs: dict[str, KVCacheSpec] ) -> bool: return all( isinstance(spec, SlidingWindowMLASpec) and spec.sliding_window == self.sliding_window for spec in kv_cache_specs.values() ) @dataclass(frozen=True) class MambaSpec(KVCacheSpec): shapes: tuple[tuple[int, ...], ...] dtypes: tuple[torch.dtype] page_size_padded: int | None = None mamba_type: MambaAttentionBackendEnum = MambaAttentionBackendEnum.MAMBA2 mamba_cache_mode: str = "none" num_speculative_blocks: int = 0 @property def page_size_bytes(self) -> int: page_size = sum( prod(shape) * get_dtype_size(dtype) for (shape, dtype) in zip(self.shapes, self.dtypes) ) if self.page_size_padded is not None: assert self.page_size_padded >= page_size return self.page_size_padded return page_size def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: if vllm_config.cache_config.mamba_cache_mode == "all": max_model_len = vllm_config.model_config.max_model_len return ( cdiv(max_model_len, self.block_size) + self.num_speculative_blocks ) * self.page_size_bytes elif vllm_config.cache_config.mamba_cache_mode == "align": return self.page_size_bytes * (2 + self.num_speculative_blocks) else: return self.page_size_bytes * (1 + self.num_speculative_blocks) def max_num_blocks_per_req(self, vllm_config: VllmConfig, max_len: int) -> int: # Mamba state is replicated across DCP/PCP ranks, never sharded, so # no CP scaling applies. if vllm_config.cache_config.mamba_cache_mode == "align": # Block table rows are position-indexed over the full sequence # even though only 2 + num_speculative_blocks state blocks are # resident at a time (earlier states are nulled out by # remove_skipped_blocks), so the row length must cover max_len # rather than max_memory_usage_bytes. return cdiv(max_len, self.block_size) + self.num_speculative_blocks return cdiv(self.max_memory_usage_bytes(vllm_config), self.page_size_bytes) def is_uniform_with_collection( self, kv_cache_specs: dict[str, KVCacheSpec] ) -> bool: return all( isinstance(spec, MambaSpec) and spec.num_speculative_blocks == self.num_speculative_blocks for spec in kv_cache_specs.values() ) @dataclass(frozen=True) class EncoderOnlyAttentionSpec(AttentionSpec): def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: # Encoder-only layers do not need KV cache return 0 @dataclass(frozen=True) class CrossAttentionSpec(AttentionSpec): """ KV cache spec for cross-attention layers in encoder-decoder models. """ def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: # For cross-attention, we need to cache encoder states # Get encoder length (e.g., 1500 for Whisper). max_encoder_len = vllm_config.scheduler_config.max_num_encoder_input_tokens return cdiv(max_encoder_len, self.block_size) * self.page_size_bytes @dataclass(frozen=True) class SinkFullAttentionSpec(FullAttentionSpec): sink_len: int | None = None @classmethod def merge(cls, specs: list[Self]) -> Self: """ Merge a list of FullAttentionSpec objects into a single FullAttentionSpec object. """ assert all(isinstance(spec, FullAttentionSpec) for spec in specs), ( "All attention layers in the same KV cache group must be FullAttentionSpec." ) sliding_window = set( spec.sliding_window for spec in specs if spec.sliding_window is not None ) attention_chunk_size = set( spec.attention_chunk_size for spec in specs if spec.attention_chunk_size is not None ) assert not any(isinstance(spec, MLAAttentionSpec) for spec in specs), ( "MLAAttentionSpec should be merged in MLAAttentionSpec.merge" ) merged_spec = cls( block_size=specs[0].block_size, num_kv_heads=specs[0].num_kv_heads, head_size=specs[0].head_size, head_size_v=specs[0].head_size_v, sink_len=specs[0].sink_len, dtype=specs[0].dtype, kv_quant_mode=specs[0].kv_quant_mode, page_size_padded=specs[0].page_size_padded, indexes_kv_by_block_stride=specs[0].indexes_kv_by_block_stride, sliding_window=cls.merge_window_sizes(sliding_window), attention_chunk_size=cls.merge_window_sizes(attention_chunk_size), non_causal=any(spec.non_causal for spec in specs), ) for spec in specs: for f in fields(AttentionSpec): assert getattr(spec, f.name) == getattr(merged_spec, f.name), ( "All attention layers in the same KV cache group must have " "the same attention spec." ) assert (merged_spec.sliding_window is not None) + ( merged_spec.attention_chunk_size is not None ) <= 1, ( "Model with both sliding window layers and chunked local attention " "layers is not supported." ) return merged_spec @dataclass(frozen=True) class UniformTypeKVCacheSpecs(KVCacheSpec): """ A KV cache spec for multiple layers with the same type of attention. Here, same types means always need the same number of token slots. For example, sliding window attentions with different window sizes are not the same type and should not be merged into one UniformTypeKVCacheSpecs. """ kv_cache_specs: dict[str, KVCacheSpec] @property def page_size_bytes(self) -> int: return sum(spec.page_size_bytes for spec in self.kv_cache_specs.values()) def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: max_num_pages = max( cdiv(spec.max_memory_usage_bytes(vllm_config), spec.page_size_bytes) for spec in self.kv_cache_specs.values() ) return max_num_pages * self.page_size_bytes @classmethod def is_uniform_type(cls, kv_cache_specs: dict[str, KVCacheSpec]) -> bool: """ Whether all layers have the same type of KV cache spec. Uses the registry to determine grouping base classes, so custom specs that inherit from FullAttentionSpec are treated as full attention. """ block_sizes = set(spec.block_size for spec in kv_cache_specs.values()) if len(block_sizes) > 1: # Different block sizes, not uniform. return False first_spec = next(iter(kv_cache_specs.values())) return first_spec.is_uniform_with_collection(kv_cache_specs) @classmethod def from_specs(cls, kv_cache_specs: dict[str, KVCacheSpec]) -> Self | None: """ Return a SameTypeKVCacheSpecs object if all layers have the same type of KV cache spec. Return None if not. """ if cls.is_uniform_type(kv_cache_specs): block_size = next(iter(kv_cache_specs.values())).block_size return cls(block_size=block_size, kv_cache_specs=kv_cache_specs) else: return None # NOTE: below util functions are only used by DeepseekV4 for now. def get_page_sizes(self) -> list[int]: return list(set(spec.page_size_bytes for spec in self.kv_cache_specs.values())) def get_num_layer_tuples(self) -> int: return Counter( spec.page_size_bytes for spec in self.kv_cache_specs.values() ).most_common(1)[0][1] def max_memory_usage_pages(self, vllm_config: VllmConfig) -> int: return max( cdiv(spec.max_memory_usage_bytes(vllm_config), spec.page_size_bytes) for spec in self.kv_cache_specs.values() ) def get_kv_cache_spec_kind(kv_cache_spec: KVCacheSpec) -> KVCacheSpecKind: if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs): inner_kinds = { get_kv_cache_spec_kind(spec) for spec in kv_cache_spec.kv_cache_specs.values() } if len(inner_kinds) == 1: return next(iter(inner_kinds)) return KVCacheSpecKind.UNKNOWN # Keep subclass checks before base classes so specialized specs keep their # more precise kind. if isinstance(kv_cache_spec, SlidingWindowMLASpec): return KVCacheSpecKind.SLIDING_WINDOW_MLA if isinstance(kv_cache_spec, MLAAttentionSpec): return KVCacheSpecKind.MLA_ATTENTION if isinstance(kv_cache_spec, SinkFullAttentionSpec): return KVCacheSpecKind.SINK_FULL_ATTENTION if isinstance(kv_cache_spec, FullAttentionSpec): return KVCacheSpecKind.FULL_ATTENTION if isinstance(kv_cache_spec, ChunkedLocalAttentionSpec): return KVCacheSpecKind.CHUNKED_LOCAL_ATTENTION if isinstance(kv_cache_spec, SlidingWindowSpec): return KVCacheSpecKind.SLIDING_WINDOW if isinstance(kv_cache_spec, MambaSpec): return KVCacheSpecKind.MAMBA if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec): return KVCacheSpecKind.ENCODER_ONLY_ATTENTION if isinstance(kv_cache_spec, CrossAttentionSpec): return KVCacheSpecKind.CROSS_ATTENTION return KVCacheSpecKind.UNKNOWN def get_kv_cache_spec_sliding_window(kv_cache_spec: KVCacheSpec) -> int | None: if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs): inner_windows = { get_kv_cache_spec_sliding_window(spec) for spec in kv_cache_spec.kv_cache_specs.values() } return next(iter(inner_windows)) if len(inner_windows) == 1 else None if isinstance(kv_cache_spec, SlidingWindowSpec): return kv_cache_spec.sliding_window return None @dataclass class KVCacheTensor: """ A class for specifying how the workers should initialize the KV cache. """ size: int # size of the KV cache tensor in bytes shared_by: list[str] # layer names that share the same KV cache tensor offset: int = 0 # byte offset of this layer within a contiguous block block_stride: int = 0 # total bytes per block in a packed layout (0 = not packed) @dataclass class KVCacheGroupSpec: """ Represents a group of model layers that share the same KV cache block table. These layers are regarded as one layer in the KV cache manager. """ # The names of model layers in this group layer_names: list[str] # The KV cache spec of this manager layer kv_cache_spec: KVCacheSpec # Whether this group contains EAGLE/MTP draft attention layers. is_eagle_group: bool = False @dataclass class KVCacheConfig: """ The KV cache configuration of a model. """ num_blocks: int """The number of KV cache blocks""" kv_cache_tensors: list[KVCacheTensor] """How should model runner initialize the KV cache tensors for each layer""" kv_cache_groups: list[KVCacheGroupSpec] """ The kv cache groups of the model. For models with only one type of attention, there is only one group that contains all layers. For models with multiple types of attention, there will be multiple groups, see `_get_kv_cache_config_uniform_page_size` for more details. """ @property def has_mamba_layers(self) -> bool: return any(isinstance(g.kv_cache_spec, MambaSpec) for g in self.kv_cache_groups) @property def needs_kv_cache_zeroing(self) -> bool: return self.has_mamba_layers