vllm.v1.core.kv_cache_utils ¶
KV-Cache Utilities.
BlockHashWithGroupId module-attribute
¶
FreeKVCacheBlockQueue ¶
This class organizes a list of KVCacheBlock objects to a doubly linked list of free blocks. We implement this class instead of using Python builtin deque to support removing a block in the middle of the queue in O(1) time. To close the performance gap to the builtin deque which is implemented in C++, this class does not allocate any Python objects when manipulating the linked list. Instead, this class manipulates the prev_free_block and next_free_block attributes of the given blocks.
The queue is ordered by block ID in the beginning. When a block is allocated and then freed, it will be appended back with the eviction order: 1. The least recent used block is at the front (LRU). 2. If two blocks have the same last accessed time (allocated by the same sequence), the one with more hash tokens (the tail of a block chain) is at the front. Note that we maintain this order by reversing the block order when free blocks of a request. This operation is outside of this class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks | list[KVCacheBlock] | A list of KVCacheBlock objects. | required |
Source code in vllm/v1/core/kv_cache_utils.py
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__init__ ¶
__init__(blocks: list[KVCacheBlock]) -> None
Source code in vllm/v1/core/kv_cache_utils.py
append ¶
append(block: KVCacheBlock) -> None
Put a block back into the free list and increase num_free_blocks by 1.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
block | KVCacheBlock | The block to append. | required |
Source code in vllm/v1/core/kv_cache_utils.py
append_n ¶
append_n(blocks: list[KVCacheBlock]) -> None
Put a list of blocks back into the free list
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks | list[KVCacheBlock] | The blocks to append. | required |
Source code in vllm/v1/core/kv_cache_utils.py
get_all_free_blocks ¶
get_all_free_blocks() -> list[KVCacheBlock]
Get all free blocks in the free list. Mainly used for testing.
Returns:
Type | Description |
---|---|
list[KVCacheBlock] | A list of free blocks. |
Source code in vllm/v1/core/kv_cache_utils.py
popleft ¶
popleft() -> KVCacheBlock
Pop the first free block and reduce num_free_blocks by 1.
Returns:
Type | Description |
---|---|
KVCacheBlock | The first free block. |
Source code in vllm/v1/core/kv_cache_utils.py
popleft_n ¶
popleft_n(n: int) -> list[KVCacheBlock]
Pop the first n free blocks and reduce num_free_blocks by n.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n | int | The number of blocks to pop. | required |
Returns:
Type | Description |
---|---|
list[KVCacheBlock] | A list of n free blocks. |
Source code in vllm/v1/core/kv_cache_utils.py
remove ¶
remove(block: KVCacheBlock) -> None
Remove a block in the free list and reduce num_free_blocks by 1.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
block | KVCacheBlock | The block to remove. | required |
Source code in vllm/v1/core/kv_cache_utils.py
KVCacheBlock dataclass
¶
KV-cache block metadata.
Source code in vllm/v1/core/kv_cache_utils.py
__init__ ¶
__init__(
block_id: int,
ref_cnt: int = 0,
_block_hash: Optional[BlockHashWithGroupId] = None,
prev_free_block: Optional[KVCacheBlock] = None,
next_free_block: Optional[KVCacheBlock] = None,
is_null: bool = False,
) -> None
__repr__ ¶
__repr__() -> str
Source code in vllm/v1/core/kv_cache_utils.py
PrefixCachingMetrics ¶
Metrics for prefix caching with a hit rate of the max recent N requests.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
max_recent_requests | int | The number of the max recent requests to aggregate. Defaults to 1000. | 1000 |
Source code in vllm/v1/core/kv_cache_utils.py
__init__ ¶
__init__(max_recent_requests: int = 1000)
Source code in vllm/v1/core/kv_cache_utils.py
observe ¶
observe(stats: PrefixCacheStats)
Observe the prefix caching for a set of requests.
This function is called with information gathered when new requests are being scheduled and are looking for computed blocks.
When there are more than max_recent_requests
requests, the oldest set of requests are removed from the metrics.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
stats | PrefixCacheStats | The prefix cache stats. | required |
Source code in vllm/v1/core/kv_cache_utils.py
_gen_lora_extra_hash_keys ¶
Generate extra keys related to LoRA for block hash computation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
request | Request | The request object. | required |
Returns:
Type | Description |
---|---|
list[int] | Return LoRA id of the request if it is a LoRA request. Return empty |
list[int] | list otherwise. |
Source code in vllm/v1/core/kv_cache_utils.py
_gen_mm_extra_hash_keys ¶
_gen_mm_extra_hash_keys(
request: Request,
start_token_idx: int,
end_token_idx: int,
start_mm_idx: int,
) -> tuple[list[Any], int]
Generate extra keys related to MultiModal request for block hash computation. For multi-modal inputs, the extra keys are (mm_hash, start_offset) that indicate a mm input contained in the block and its starting offset in the block tokens.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
request | Request | The request object. | required |
start_token_idx | int | The start token index of the block. | required |
end_token_idx | int | The end token index of the block. | required |
start_mm_idx | int | The start multi-modal index of the block. | required |
Returns:
Type | Description |
---|---|
tuple[list[Any], int] | A tuple of extra keys and the next multi-modal index. |
Source code in vllm/v1/core/kv_cache_utils.py
_get_kv_cache_groups_uniform_page_size ¶
_get_kv_cache_groups_uniform_page_size(
kv_cache_spec: dict[str, KVCacheSpec],
) -> list[KVCacheGroupSpec]
Generates the KV cache groups for hybrid models with multiple attention types but still with a uniform page size (physical memory per block per layer) for all layers.
Detailed explanation about kv cache management of hybrid models: The layers in the models are repeated with some patterns, e.g., a model with 10 full attention layers and 20 sliding window attention layers can be regarded as repeating the pattern (1 * full, 2 * sw) 10 times. The KVCacheManager allocates different block tables for each of the 3 layers in the pattern, and repeats each of them 10 times to generate the block_table for the 30 layers in the model. Therefore, we can group the layers in the model into 3 kv_cache_groups, each of which contains 10 layers in the model. The KVCacheManager allocates the block_table for each group based on its kv_cache spec, and the model runner applies the block table to each layer in the group. For example: 1. A model only uses full attention. The pattern is (num_hidden_layers * full), so there is only one group and the block table is shared by all layers. It is already handled by _get_kv_cache_config_uniform_type
. 2. A model with 10 full attention layers and 20 sliding window attention layers. There are 3 layers in the pattern (1 * full, 2 * sw), so there are 3 kv_cache_groups, each of which represents 10 layers.
To simplify the implementation, we make the following assumptions: 1. Physical memory per block: Must be the same across all KV cache groups. Breaking this assumption is non-trivial due to memory fragmentation concerns when allocating blocks of different sizes. 2. Tokens per block (block_size): Currently, we directly use CacheConfig.block_size
for all layers. It can be extended to vary by KV cache group, but within each KV cache group, all layers must share the same block size. 3. Physical memory per token per layer: This property is decided by model config. Currently we only support models that have the same physical memory per token per layer for all layers. Can be relaxed with a simple extension, but still need to keep physical memory per block the same for all groups. 4. Number of layers per group: Currently assumed the same for all layers. Can be relaxed with a simple extension, but still need to keep physical memory per block the same for all groups. 5. Attention type within groups: All layers in a group must share the same attention type. One exception is that, when --disable-hybrid-kv-cache-manager
is true, the single group for full attention layers may also include attention layers using sliding window or LLaMA 4 local attention. See unify_hybrid_kv_cache_specs
for more details. 6. Support for multiple attention types: The design for most components is general to an arbitrary number of attention types. But find_longest_cache_hit
only supports one attention type or two types of full-attention plus exactly one another type. The general implementation of this function is feasible but we don't know how to implement it cleanly yet.
As we assume tokens per block, physical memory per token per layer, and number of layers per group are the same now, we can ensure that physical memory per block is the same for all groups.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
kv_cache_spec | dict[str, KVCacheSpec] | The KVCacheSpec of each attention layer in the model | required |
Returns: The generated KVCacheGroupSpecs
Source code in vllm/v1/core/kv_cache_utils.py
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_get_kv_cache_groups_uniform_spec ¶
_get_kv_cache_groups_uniform_spec(
kv_cache_specs: dict[str, KVCacheSpec],
) -> list[KVCacheGroupSpec]
Generates the KV cache configuration for a model with the same KV cache spec for all layers.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
kv_cache_specs | dict[str, KVCacheSpec] | The kv cache spec of each attention layer in the model | required |
Returns:
Type | Description |
---|---|
list[KVCacheGroupSpec] | The generated KVCacheGroupSpecs |
Source code in vllm/v1/core/kv_cache_utils.py
_get_kv_cache_groups_uniform_type ¶
_get_kv_cache_groups_uniform_type(
spec: UniformTypeKVCacheSpecs,
) -> list[KVCacheGroupSpec]
Generates the KV cache configuration for a model with one type of KV cache but different hidden sizes. All layers are merged into one group.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
spec | UniformTypeKVCacheSpecs | The UniformTypeKVCacheSpecs of the model | required |
Returns:
Type | Description |
---|---|
list[KVCacheGroupSpec] | The generated KVCacheGroupSpecs |
Source code in vllm/v1/core/kv_cache_utils.py
check_enough_kv_cache_memory ¶
check_enough_kv_cache_memory(
vllm_config: VllmConfig,
kv_cache_spec: dict[str, KVCacheSpec],
available_memory: int,
)
Checks whether available_memory
is enough for the KV cache to hold at least one request with the model's max_model_len.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vllm_config | VllmConfig | The global VllmConfig | required |
kv_cache_spec | dict[str, KVCacheSpec] | The kv cache spec of each attention layer in the model | required |
available_memory | int | Memory available for KV cache in bytes. | required |
Raises:
Type | Description |
---|---|
ValueError | If there is not enough memory available for the KV cache. |
Source code in vllm/v1/core/kv_cache_utils.py
create_kv_cache_group_specs ¶
create_kv_cache_group_specs(
kv_cache_spec: dict[str, KVCacheSpec],
grouped_layer_names: list[list[str]],
) -> list[KVCacheGroupSpec]
Create KVCacheGroupSpec object for each kv cache group layer. The layers in the same group should share the same KVCacheSpec.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
kv_cache_spec | dict[str, KVCacheSpec] | A mapping from each layer name to its corresponding KVCacheSpec. | required |
grouped_layer_names | list[list[str]] | A list of kv cache groups, where each element is a list of layer names that belong to the same group and should share the same KVCacheSpec. | required |
Returns: A list of KVCacheGroupSpec objects, one for each group.
Source code in vllm/v1/core/kv_cache_utils.py
estimate_max_model_len ¶
estimate_max_model_len(
vllm_config: VllmConfig,
kv_cache_spec: dict[str, KVCacheSpec],
available_memory: int,
) -> int
Estimates the maximum model length that can fit in the available memory using binary search.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vllm_config | VllmConfig | The global VllmConfig | required |
kv_cache_spec | dict[str, KVCacheSpec] | The kv cache spec of each attention layer in the model | required |
available_memory | int | Memory available for KV cache in bytes. | required |
Returns:
Type | Description |
---|---|
int | The estimated maximum model length that can fit in the available memory. |
Source code in vllm/v1/core/kv_cache_utils.py
generate_block_hash_extra_keys ¶
generate_block_hash_extra_keys(
request: Request,
start_token_idx: int,
end_token_idx: int,
start_mm_idx: int,
) -> tuple[Optional[tuple[Any, ...]], int]
Generate extra keys for the block hash. The extra keys can come from the multi-modal inputs and request specific metadata (e.g., LoRA ID).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
request | Request | The request object. | required |
start_token_idx | int | The start token index of the block. | required |
end_token_idx | int | The end token index of the block. | required |
start_mm_idx | int | The start multi-modal index of the block. | required |
Returns:
Type | Description |
---|---|
tuple[Optional[tuple[Any, ...]], int] | A tuple of extra keys and the next multi-modal index. |
Source code in vllm/v1/core/kv_cache_utils.py
generate_scheduler_kv_cache_config ¶
generate_scheduler_kv_cache_config(
kv_cache_configs: list[KVCacheConfig],
) -> KVCacheConfig
Generate the KV cache configuration for the scheduler.
Source code in vllm/v1/core/kv_cache_utils.py
get_block_hash ¶
get_block_hash(key: BlockHashWithGroupId) -> BlockHash
get_group_id ¶
get_group_id(key: BlockHashWithGroupId) -> int
get_kv_cache_config_from_groups ¶
get_kv_cache_config_from_groups(
vllm_config: VllmConfig,
kv_cache_groups: list[KVCacheGroupSpec],
kv_cache_specs: dict[str, KVCacheSpec],
available_memory: int,
) -> KVCacheConfig
Generate the KV cache configuration from the KV cache groups and spec of each layer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vllm_config | VllmConfig | The global VllmConfig | required |
kv_cache_groups | list[KVCacheGroupSpec] | The KV cache groups | required |
kv_cache_specs | dict[str, KVCacheSpec] | The KV cache spec of each attention layer in the model | required |
available_memory | int | Memory available for KV cache in bytes | required |
Returns: The generated KVCacheConfig
Source code in vllm/v1/core/kv_cache_utils.py
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get_kv_cache_configs ¶
get_kv_cache_configs(
vllm_config: VllmConfig,
kv_cache_specs: list[dict[str, KVCacheSpec]],
available_memory: list[int],
) -> list[KVCacheConfig]
Generates the KV cache configurations for a model. Since we use a shared centralized controller for all workers, we need the kv_cache_config
to be consistent across all workers to make sure the KV cache allocation can be applied to all workers. However, different workers may have different memory available, and different type of layers (when pipeline parallel is enabled). To handle the difference between workers, the current implementation is: 1. Merge the KV cache specs of all workers to get the KVCacheSpecs for the whole model. 2. Generate the KV cache groups based on the layer ratio of the whole model. 3. Generate the KV cache configs for each worker based on the KV cache grouping strategy. (This is reasonable because the layer ratio of different PP stages are similar.) 4. Change the num_blocks of each worker to the smallest among all workers.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vllm_config | VllmConfig | The global VllmConfig | required |
kv_cache_specs | list[dict[str, KVCacheSpec]] | List of dict[layer_name, KVCacheSpec] for each worker. | required |
available_memory | list[int] | Memory available for KV cache in bytes for each worker. | required |
Returns:
Type | Description |
---|---|
list[KVCacheConfig] | The generated KVCacheConfigs for each worker. |
Source code in vllm/v1/core/kv_cache_utils.py
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get_kv_cache_groups ¶
get_kv_cache_groups(
vllm_config: VllmConfig,
kv_cache_spec: dict[str, KVCacheSpec],
) -> list[KVCacheGroupSpec]
Split the layers in the model into groups with the same KV cache spec.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vllm_config | VllmConfig | The global VllmConfig | required |
kv_cache_spec | dict[str, KVCacheSpec] | The kv cache spec of each attention layer in the model | required |
Returns:
Type | Description |
---|---|
list[KVCacheGroupSpec] | The generated KVCacheGroups |
Source code in vllm/v1/core/kv_cache_utils.py
get_max_concurrency_for_kv_cache_config ¶
get_max_concurrency_for_kv_cache_config(
vllm_config: VllmConfig, kv_cache_config: KVCacheConfig
) -> float
Get the maximum concurrency for the given KV cache configuration.
Source code in vllm/v1/core/kv_cache_utils.py
get_num_blocks ¶
get_num_blocks(
vllm_config: VllmConfig,
num_layers: int,
available_memory: int,
page_size: int,
) -> int
Get the number of kv cache blocks.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vllm_config | VllmConfig | The global VllmConfig | required |
num_layers | int | The number of layers | required |
available_memory | int | Memory available for KV cache in bytes. | required |
page_size | int | The page size of the KV cache. | required |
Source code in vllm/v1/core/kv_cache_utils.py
get_request_block_hasher ¶
get_request_block_hasher(
block_size: int, caching_hash_fn: Callable[[Any], bytes]
) -> Callable[[Request], list[BlockHash]]
Returns a function which computes the list of un-computed block hashes of a request.
Source code in vllm/v1/core/kv_cache_utils.py
get_uniform_page_size ¶
get_uniform_page_size(
kv_cache_spec: dict[str, KVCacheSpec],
) -> int
Get the page size of the KV cache.
Source code in vllm/v1/core/kv_cache_utils.py
hash_block_tokens ¶
hash_block_tokens(
hash_function: Callable[[Any], bytes],
parent_block_hash: Optional[BlockHash],
curr_block_token_ids: Sequence[int],
extra_keys: Optional[tuple[Any, ...]] = None,
) -> BlockHash
Computes a hash value corresponding to the contents of a block and the contents of the preceding block(s). The hash value is used for prefix caching. We use LRU cache for this function to avoid recomputing hash values for the same block contents. Args: hash_function: The hash function used to compute block hash. parent_block_hash: The hash of the parent block. None if this is the first block. curr_block_token_ids: A list of token ids in the current block. The current block is assumed to be full. extra_keys: Extra keys for the block. Returns: The hash value of the block and the token ids in the block. The entire tuple is used as the hash key of the block.
Source code in vllm/v1/core/kv_cache_utils.py
init_none_hash ¶
Source code in vllm/v1/core/kv_cache_utils.py
is_kv_cache_page_size_uniform ¶
is_kv_cache_page_size_uniform(
kv_cache_spec: dict[str, KVCacheSpec],
) -> bool
Whether all layers in the given KVCacheSpec have the same page size. Args: kv_cache_spec: The KVCacheSpec of each attention layer in the model
Returns:
Type | Description |
---|---|
bool | True if all layers have the same page size, False otherwise. |
Source code in vllm/v1/core/kv_cache_utils.py
is_kv_cache_spec_uniform ¶
is_kv_cache_spec_uniform(
kv_cache_spec: dict[str, KVCacheSpec],
) -> bool
Whether all layers in the given KVCacheSpec have the same KV cache spec. Note that we regard FullAttentionSpec with and without sliding window as the same type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
kv_cache_spec | dict[str, KVCacheSpec] | The kv cache spec of each attention layer in the model | required |
Returns:
Type | Description |
---|---|
bool | True if all layers have the same type, False otherwise. |
Source code in vllm/v1/core/kv_cache_utils.py
is_kv_cache_type_attention_free ¶
is_kv_cache_type_attention_free(
kv_cache_spec: dict[str, KVCacheSpec],
) -> bool
make_block_hash_with_group_id ¶
make_block_hash_with_group_id(
block_hash: BlockHash, group_id: int
) -> BlockHashWithGroupId
Pack a BlockHash
and group id into a BlockHashWithGroupId
.
The group id is encoded using 4 bytes in big-endian order and appended to the block hash bytes. This representation avoids creating tuples while still allowing us to recover both components when needed.
Source code in vllm/v1/core/kv_cache_utils.py
max_memory_usage_bytes ¶
max_memory_usage_bytes(
vllm_config: VllmConfig,
kv_cache_specs: Iterable[KVCacheSpec],
) -> int
Get the maximum memory usage in bytes for the given KV cache specs.
Source code in vllm/v1/core/kv_cache_utils.py
may_override_num_blocks ¶
may_override_num_blocks(
vllm_config: VllmConfig, num_blocks: int
) -> int
Override the number of kv cache blocks if num_gpu_blocks_override
is set.
Source code in vllm/v1/core/kv_cache_utils.py
maybe_convert_block_hash ¶
maybe_convert_block_hash(
hash_bytes: BlockHash,
) -> ExternalBlockHash
need_extra_keys ¶
Check whether the blocks allocated to this request need extra hash keys.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
request | Request | The request. | required |
Returns:
Name | Type | Description |
---|---|---|
bool | bool | Whether blocks allocated to this request need extra hash keys. |
Source code in vllm/v1/core/kv_cache_utils.py
unify_hybrid_kv_cache_specs ¶
unify_hybrid_kv_cache_specs(
kv_cache_spec: dict[str, KVCacheSpec],
)
This function tries to convert the KV cache specs to one type if the model is a hybrid model with multiple type of KV cache. It will convert all SlidingWindowSpec to FullAttentionSpec if both types are present.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
kv_cache_spec | dict[str, KVCacheSpec] | The kv cache spec of each attention layer in the model | required |