vllm/vllm/v1/kv_cache_interface.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import copy
from dataclasses import dataclass, fields
from math import prod
from typing import Optional

import torch
from typing_extensions import Self

from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.utils import cdiv, get_dtype_size

logger = init_logger(__name__)


@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

    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

    @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])


@dataclass(frozen=True)
class AttentionSpec(KVCacheSpec):
    num_kv_heads: int
    head_size: int
    dtype: torch.dtype
    use_mla: bool

    @property
    def page_size_bytes(self) -> int:
        # For MLA we only store a single latent vector
        coef = 1 if self.use_mla else 2
        return coef * self.block_size * self.num_kv_heads * self.head_size \
                * get_dtype_size(self.dtype)


@dataclass(frozen=True)
class FullAttentionSpec(AttentionSpec):
    sliding_window: Optional[int] = None
    attention_chunk_size: Optional[int] = None
    """
    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.
    Default to None for not using sliding window attention.
    """

    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
        # Note(hc): each dcp rank only need save
        # (max_model_len//dcp_world_size) tokens locally.
        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]) -> Optional[int]:
        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)
        merged_spec = 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,
            use_mla=specs[0].use_mla,
            sliding_window=cls.merge_window_sizes(sliding_window),
            attention_chunk_size=cls.merge_window_sizes(attention_chunk_size),
        )
        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 ChunkedLocalAttentionSpec(AttentionSpec):
    attention_chunk_size: int

    def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
        max_model_len = vllm_config.model_config.max_model_len
        max_num_batched_tokens = (
            vllm_config.scheduler_config.max_num_batched_tokens)

        # During chunked prefill, we allocate KV cache for at most
        # `self.attention_chunk_size` computed tokens plus the newly scheduled
        # tokens. And we won't allocate KV cache for more than `max_model_len`
        # tokens.
        num_tokens = min(self.attention_chunk_size + max_num_batched_tokens,
                         max_model_len)

        return cdiv(num_tokens, self.block_size) * self.page_size_bytes


@dataclass(frozen=True)
class SlidingWindowSpec(AttentionSpec):
    sliding_window: int

    def __post_init__(self):
        assert not self.use_mla, "MLA is not supported for sliding window"

    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_model_len = vllm_config.model_config.max_model_len
        max_num_batched_tokens = (
            vllm_config.scheduler_config.max_num_batched_tokens)

        # During chunked prefill, we allocate KV cache for the last
        # `self.sliding_window-1` computed tokens plus the newly scheduled
        # tokens. And we won't allocate KV cache for more than `max_model_len`
        # tokens.
        num_tokens = min(self.sliding_window - 1 + max_num_batched_tokens,
                         max_model_len)

        # +1 here because the sliding window may not start from the beginning
        # of the block. For example, if the block size is 4 and num_token
        # is 4, we need two blocks [XXCD] [EF] to store the sliding
        # window [CDEF] of 6 tokens.
        return (cdiv(num_tokens, self.block_size) + 1) * self.page_size_bytes


@dataclass(frozen=True)
class MambaSpec(KVCacheSpec):
    shapes: tuple[tuple[int, ...], ...]
    dtypes: tuple[torch.dtype]
    page_size_padded: Optional[int] = None
    mamba_type: str = "mamba2"
    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:
        # We allocate 1 block for each request now, so max_memory_usage_bytes is
        # the same as page_size_bytes.
        # Need to update this when supporting prefix caching.
        return self.page_size_bytes


@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 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.
        """
        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
        one_spec = next(iter(kv_cache_specs.values()))
        if isinstance(one_spec, (FullAttentionSpec, CrossAttentionSpec)):
            return all(
                isinstance(spec, type(one_spec))
                for spec in kv_cache_specs.values())
        elif isinstance(one_spec, SlidingWindowSpec):
            return all(
                isinstance(spec, SlidingWindowSpec)
                and spec.sliding_window == one_spec.sliding_window
                for spec in kv_cache_specs.values())
        elif isinstance(one_spec, ChunkedLocalAttentionSpec):
            return all(
                isinstance(spec, ChunkedLocalAttentionSpec)
                and spec.attention_chunk_size == one_spec.attention_chunk_size
                for spec in kv_cache_specs.values())
        elif isinstance(one_spec, MambaSpec):
            return all(
                isinstance(spec, MambaSpec) and spec.num_speculative_blocks ==
                one_spec.num_speculative_blocks
                for spec in kv_cache_specs.values())
        else:
            # NOTE(Chen): Please add new branches for new KV cache spec types.
            raise NotImplementedError(
                f"Unsupported KV cache spec type: {type(one_spec)}")

    @classmethod
    def from_specs(cls, kv_cache_specs: dict[str,
                                             KVCacheSpec]) -> Optional[Self]:
        """
        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


@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


@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


@dataclass
class KVCacheConfig:
    """
    The KV cache configuration of a model.
    """
    """The number of KV cache blocks"""
    num_blocks: int
    """How should model runner initialize the KV cache tensors for each layer"""
    kv_cache_tensors: list[KVCacheTensor]
    """
    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.
    """
    kv_cache_groups: list[KVCacheGroupSpec]