vllm.v1.kv_cache_interface ¶

@dataclass(frozen=True, kw_only=True)
class AttentionSpec(KVCacheSpec):
    num_kv_heads: int
    head_size: int
    dtype: torch.dtype
    head_size_v: int = None  # type: ignore[assignment]
    kv_quant_mode: KVQuantMode = KVQuantMode.NONE
    page_size_padded: int | None = None
    indexes_kv_by_block_stride: bool = False
    num_head_slots: int | None = None
    """H of the logical ``[B, H, N, C]`` page when packing diverges from one
    slot per KV head. None means one slot per KV head. Published by the backend.
    """
    state_content_bytes: int | None = None
    """C in bytes when packed; None means dense K/V content."""

    def __post_init__(self):
        if self.head_size_v is None:
            object.__setattr__(self, "head_size_v", self.head_size)

    @property
    def num_heads(self) -> int:
        if self.num_head_slots is not None:
            return self.num_head_slots
        return self.num_kv_heads

    @property
    def state_content_size_bytes(self) -> int:
        """Bytes per (head slot, stored state) cell of the page."""
        if self.state_content_bytes is not None:
            return self.state_content_bytes
        return (self.head_size + self.head_size_v) * get_dtype_size(self.dtype)

    @property
    def unpadded_page_size_bytes(self) -> int:
        return self.num_heads * self.storage_block_size * self.state_content_size_bytes

    @property
    def page_size_bytes(self) -> int:
        if self.page_size_padded is not None:
            assert self.page_size_padded >= self.unpadded_page_size_bytes
            return self.page_size_padded
        return self.unpadded_page_size_bytes

    @property
    def real_page_size_bytes(self) -> int:
        """Alias of ``unpadded_page_size_bytes``
        TODO(lucas): follow up with TPU backend to see if we can remove this property.
        """
        return self.unpadded_page_size_bytes

    def max_num_blocks_per_req(self, vllm_config: VllmConfig, max_len: int) -> int:
        parallel_config = vllm_config.parallel_config
        kv_shard_count = parallel_config.decode_context_parallel_size
        return cdiv(max_len, self.block_size * kv_shard_count)

@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)
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, 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.
    """

    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 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
        if dcp_world_size > 1:
            max_model_len = cdiv(max_model_len, dcp_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,
            num_head_slots=specs[0].num_head_slots,
            state_content_bytes=specs[0].state_content_bytes,
            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

@dataclass(frozen=True, kw_only=True)
class HiddenStateCacheSpec(MLAAttentionSpec):
    """Marker for hidden-state cache layers used by extract_hidden_states."""

    pass

@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 has_mixed_precision_kv_cache(self) -> bool:
        """Whether attention groups store their KV cache at more than one precision."""
        kv_cache_precisions: set[tuple[torch.dtype, KVQuantMode]] = set()
        for group in self.kv_cache_groups:
            group_spec = group.kv_cache_spec
            group_specs = (
                list(group_spec.kv_cache_specs.values())
                if isinstance(group_spec, UniformTypeKVCacheSpecs)
                else [group_spec]
            )
            kv_cache_precisions.update(
                (spec.dtype, spec.kv_quant_mode)
                for spec in group_specs
                if isinstance(spec, AttentionSpec)
            )
        return len(kv_cache_precisions) > 1

    @property
    def needs_kv_cache_zeroing(self) -> bool:
        """Whether newly allocated KV cache blocks must be zeroed before use.

        Required for Mamba layers, whose state is read before it is fully written
        (#35219), and for mixed-precision caches, where a block reused across
        groups can be reinterpreted under a different precision and decode stale
        bytes to NaN/Inf. Uniform-precision caches skip zeroing.
        """
        return self.has_mamba_layers or self.has_mixed_precision_kv_cache

@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(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
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)

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
    # Hadamard-rotated Lloyd-Max quant, packed K+V per slot.
    TURBOQUANT_K8V4 = 6
    TURBOQUANT_4BIT_NC = 7
    TURBOQUANT_K3V4_NC = 8
    TURBOQUANT_3BIT_NC = 9

    @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

    @property
    def is_turboquant(self) -> bool:
        """True for any turboquant quantization mode."""
        return self in (
            KVQuantMode.TURBOQUANT_K8V4,
            KVQuantMode.TURBOQUANT_4BIT_NC,
            KVQuantMode.TURBOQUANT_K3V4_NC,
            KVQuantMode.TURBOQUANT_3BIT_NC,
        )

@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,
            num_head_slots=base.num_head_slots,
            state_content_bytes=base.state_content_bytes,
            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)
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,
            num_head_slots=specs[0].num_head_slots,
            state_content_bytes=specs[0].state_content_bytes,
            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, 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
    # MLA stores a single latent vector per state; there is no separate V.
    head_size_v: int = 0

    def __post_init__(self):
        assert self.model_version in (None, "deepseek_v4"), (
            f"Unsupported model version: {self.model_version}"
        )
        super().__post_init__()
        _apply_alignment_padding(self)

    @property
    def storage_block_size(self) -> int:
        return self.block_size // self.compress_ratio

    @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)
        extra_retained_set = set(spec.extra_retained_tokens 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
            and len(extra_retained_set) == 1
        ), (
            "All attention layers in the same KV cache group must use the same "
            "quantization method, compress ratio, model version, sliding "
            "window size, KV block stride indexing, and retained token count."
        )
        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,
            num_head_slots=specs[0].num_head_slots,
            state_content_bytes=specs[0].state_content_bytes,
            indexes_kv_by_block_stride=block_stride_set.pop(),
            sliding_window=sliding_window_set.pop(),
            extra_retained_tokens=extra_retained_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, kw_only=True)
class SlidingWindowSpec(AttentionSpec):
    sliding_window: int
    # The trailing edge of the window is extended by ``extra_retained_tokens``
    # so that those extra trailing tokens' blocks are retained (but not
    # attended). This is needed for multi-module spec decoding which can
    # re-prefill the last num_spec_prefill_tokens - 1 tokens from the end
    # of the sequence, and thus needs to delay freeing/caching of blocks.
    extra_retained_tokens: int = 0

    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`. An additional
        # `extra_retained_tokens` trailing tokens are kept alive below the
        # window for multi-module spec decoding, and must be accounted here too.
        num_tokens = min(
            self.sliding_window - 1 + self.extra_retained_tokens + 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()
        )