vllm.v1.core.kv_cache_utils ¶
KV-Cache Utilities.
Classes:
-
BlockHashListWithBlockSize–Convert block-hash granularity from
hash_block_sizetotarget_block_size. -
FreeKVCacheBlockQueue–This class organizes a list of KVCacheBlock objects to a doubly linked
-
KVCacheBlock–KV-cache block metadata.
Functions:
-
check_enough_kv_cache_memory–Checks whether
available_memoryis enough for the KV cache to hold at -
create_kv_cache_group_specs–Create KVCacheGroupSpec object for each kv cache group layer.
-
estimate_max_model_len–Estimates the maximum model length that can fit in the available memory
-
generate_block_hash_extra_keys–Generate extra keys for the block hash. The extra keys can come from
-
generate_scheduler_kv_cache_config–Generate the KV cache configuration for the scheduler.
-
get_block_hash–Extract the
BlockHashfrom aBlockHashWithGroupId. -
get_group_id–Extract the group id from a
BlockHashWithGroupId. -
get_kv_cache_capacity–Get the group-aware KV cache token capacity and max concurrency.
-
get_kv_cache_config_from_groups–Generate the KV cache configuration from the KV cache groups and spec
-
get_kv_cache_configs–Generates the KV cache configurations for a model.
-
get_kv_cache_groups–Split the layers in the model into groups with the same KV cache spec.
-
get_max_concurrency_for_kv_cache_config–Get the maximum concurrency for the given KV cache configuration.
-
get_none_hash_seed–Return the seed NONE_HASH was derived from.
-
get_request_block_hasher–Returns a function which computes the list of un-computed block hashes
-
get_uniform_page_size–Get the page size of the KV cache.
-
group_and_unify_kv_cache_specs–Group the KV cache specs and unify each group into one UniformTypeKVCacheSpecs.
-
hash_block_tokens–Computes a hash value corresponding to the contents of a block and
-
is_kv_cache_spec_uniform–Whether all layers in the given KVCacheSpec have the same KV cache spec.
-
make_block_hash_with_group_id–Pack a
BlockHashand group id into aBlockHashWithGroupId. -
max_memory_usage_bytes–Get the maximum memory usage in bytes for the given KV cache specs.
-
may_override_num_blocks–Override the number of kv cache blocks if
num_gpu_blocks_overrideis set. -
resolve_block_hashes–Resolve the block-hash view at
block_size. -
resolve_kv_cache_block_sizes–Resolve (scheduler_block_size, hash_block_size).
-
resolve_none_hash_seed–Resolve the seed to derive NONE_HASH from.
-
unify_hybrid_kv_cache_specs–This function tries to convert the KV cache specs to one type if the model
-
unify_kv_cache_spec_page_size–Unify the page size of the given KVCacheSpec. If the page size of all layers
-
update_kv_cache_capacity–Store and log the resolved KV cache capacity.
-
validate_kv_cache_layout–Validate that the resolved layout can express this model's packing.
BlockHashListWithBlockSize ¶
Convert block-hash granularity from hash_block_size to target_block_size. Used when KV cache groups have different block sizes: hash_block_size is the size used to compute the original block_hashes; target_block_size is the group's actual block size.
Currently, only scaling up by an integer factor is supported (i.e., target_block_size is a multiple of hash_block_size). Conversion is performed lazily on access for efficiency. Each hash_block_size hash is already chained over its entire prefix, so the hash at the last hash_block_size boundary of a target_block_size block uniquely fingerprints that block's prefix; we use it directly.
Example (hash_block_size = 16, target_block_size = 32): the second 16-size hash already covers tokens 0-31, so it is the 32-size hash:
Block hashes with block_size 16: | Token Range | 0-15 | 16-31 | 32-47 | 48-63 | |-------------|------|-------|-------|-------| | Hash | A | B | C | D |
Block hashes with block_size 32: | Token Range | 0-31 | 32-63 | |-------------|------|-------| | Hash | B | D |
Parameters:
-
(block_hashes¶list[BlockHash]) –Block hashes to convert, computed at
hash_block_size. -
(hash_block_size¶int) –Block size at which
block_hasheswere computed. -
(target_block_size¶int) –Desired block size; must be a multiple of
hash_block_size.
Source code in vllm/v1/core/kv_cache_utils.py
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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:
-
(blocks¶list[KVCacheBlock]) –A list of KVCacheBlock objects.
Methods:
-
append–Put a block back into the free list and increase
-
append_n–Put a list of blocks back into the free list
-
get_all_free_blocks–Get all free blocks in the free list. Mainly used for testing.
-
iter_blocks_after–Iterate free blocks in eviction order after the cursor.
-
popleft–Pop the first free block and reduce num_free_blocks by 1.
-
popleft_n–Pop the first n free blocks and reduce num_free_blocks by n.
-
prepend_n–Put a list of blocks at the front of the free list.
-
remove–Remove a block in the free list and reduce num_free_blocks by 1.
Source code in vllm/v1/core/kv_cache_utils.py
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append(block) ¶
Put a block back into the free list and increase num_free_blocks by 1.
Parameters:
-
(block¶KVCacheBlock) –The block to append.
Source code in vllm/v1/core/kv_cache_utils.py
append_n(blocks) ¶
Put a list of blocks back into the free list
Parameters:
-
(blocks¶list[KVCacheBlock]) –The blocks to append.
Source code in vllm/v1/core/kv_cache_utils.py
get_all_free_blocks() ¶
Get all free blocks in the free list. Mainly used for testing.
Returns:
-
list[KVCacheBlock]–A list of free blocks.
Source code in vllm/v1/core/kv_cache_utils.py
iter_blocks_after(cursor) ¶
Iterate free blocks in eviction order after the cursor.
Source code in vllm/v1/core/kv_cache_utils.py
popleft() ¶
Pop the first free block and reduce num_free_blocks by 1.
Returns:
-
KVCacheBlock–The first free block.
Source code in vllm/v1/core/kv_cache_utils.py
popleft_n(n) ¶
Pop the first n free blocks and reduce num_free_blocks by n.
Parameters:
Returns:
-
list[KVCacheBlock]–A list of n free blocks.
Source code in vllm/v1/core/kv_cache_utils.py
prepend_n(blocks) ¶
Put a list of blocks at the front of the free list.
Source code in vllm/v1/core/kv_cache_utils.py
remove(block) ¶
Remove a block in the free list and reduce num_free_blocks by 1.
Parameters:
-
(block¶KVCacheBlock) –The block to remove.
Source code in vllm/v1/core/kv_cache_utils.py
KVCacheBlock dataclass ¶
KV-cache block metadata.
Methods:
-
reset_hash–Reset the block hash when the block is evicted.
Source code in vllm/v1/core/kv_cache_utils.py
_approximate_gcd(values, *, lower_bound=None) ¶
Pick a chunk size that minimizes total upward padding.
Each x is rounded up to a multiple of d:
x -> ceil(x / d) * d
Total padding is:
pad(d) = sum_i (ceil(x_i / d) * d - x_i)
We brute-force d in [lower_bound, max(values)] (fine for small lists / small maxima) and return the d with minimum padding. Ties prefer larger d.
Source code in vllm/v1/core/kv_cache_utils.py
_auto_fit_max_model_len(vllm_config, projected_groups_per_worker, available_memory) ¶
When max_model_len is set to -1, this function estimates the largest context length that can be supported with the available GPU memory. It uses binary search to find the maximum length that fits across all workers.
Parameters:
-
(vllm_config¶VllmConfig) –The global VllmConfig (will be modified in-place)
-
(projected_groups_per_worker¶list[list[KVCacheGroupSpec]]) –KV cache groups projected to each worker.
-
(available_memory¶list[int]) –Memory available for KV cache in bytes for each worker.
Source code in vllm/v1/core/kv_cache_utils.py
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_estimate_max_model_len_from_groups(vllm_config, kv_cache_groups, available_memory) ¶
Binary search for the maximum model length that fits in available memory. Returns 0 if even 1 token doesn't fit.
Source code in vllm/v1/core/kv_cache_utils.py
_gen_lora_extra_hash_keys(request) ¶
Generate extra keys related to LoRA for block hash computation.
Parameters:
Returns:
-
list[str]–Return LoRA name of the request if it is a LoRA request. Return empty
-
list[str]–list otherwise.
Source code in vllm/v1/core/kv_cache_utils.py
_gen_mm_extra_hash_keys(request, start_token_idx, end_token_idx, start_mm_idx) ¶
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:
-
(request¶Request) –The request object.
-
(start_token_idx¶int) –The start token index of the block.
-
(end_token_idx¶int) –The end token index of the block.
-
(start_mm_idx¶int) –The start multi-modal index of the block.
Returns:
Source code in vllm/v1/core/kv_cache_utils.py
_gen_prompt_embeds_extra_hash_keys(request, start_token_idx, end_token_idx) ¶
Generate extra keys related to prompt embeds for block hash computation.
Parameters:
-
(request¶Request) –The request object.
-
(start_token_idx¶int) –The start token index of the block.
-
(end_token_idx¶int) –The end token index of the block.
Returns:
-
list[bytes]–Return a stable hash of the block prompt embeddings if prompt embeds
-
list[bytes]–are present. Return empty list otherwise.
Source code in vllm/v1/core/kv_cache_utils.py
_get_kv_cache_bytes_per_block(kv_cache_groups) ¶
Return the largest cache group's bytes per block.
Source code in vllm/v1/core/kv_cache_utils.py
_get_kv_cache_groups_uniform_groups(grouped_specs) ¶
Generate the KV cache groups from the grouped specs.
Source code in vllm/v1/core/kv_cache_utils.py
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_get_kv_cache_groups_uniform_page_size(kv_cache_spec) ¶
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:
-
(kv_cache_spec¶dict[str, KVCacheSpec]) –The KVCacheSpec of each attention layer in the model
Returns: The generated KVCacheGroupSpecs
Source code in vllm/v1/core/kv_cache_utils.py
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_get_kv_cache_groups_uniform_spec(kv_cache_specs) ¶
Generates the KV cache configuration for a model with the same KV cache spec for all layers.
Parameters:
-
(kv_cache_specs¶dict[str, KVCacheSpec]) –The kv cache spec of each attention layer in the model
Returns:
-
list[KVCacheGroupSpec]–The generated KVCacheGroupSpecs
Source code in vllm/v1/core/kv_cache_utils.py
_get_kv_cache_groups_uniform_type(spec) ¶
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:
-
(spec¶UniformTypeKVCacheSpecs) –The UniformTypeKVCacheSpecs of the model
Returns:
-
list[KVCacheGroupSpec]–The generated KVCacheGroupSpecs
Source code in vllm/v1/core/kv_cache_utils.py
_max_memory_usage_bytes_from_groups(vllm_config, kv_cache_groups) ¶
Calculate maximum memory usage in bytes from KV cache groups.
This correctly accounts for padding in hybrid models. For example, if a model has 8 full attention layers and 9 sliding window layers, they will be padded to 9 full + 9 sliding window for uniform group sizes.
Each group independently claims blocks from the shared pool, so a request consumes the sum of the per-group block counts, i.e. bytes_per_block * total_blocks.
Source code in vllm/v1/core/kv_cache_utils.py
_pool_bytes_per_block(kv_cache_groups) ¶
Bytes consumed by one block in the worker's shared KV cache pool, mirroring the divisor used by get_kv_cache_config_from_groups to convert available_memory into num_blocks. Used to compute the effective KV cache capacity once num_gpu_blocks_override is applied.
Source code in vllm/v1/core/kv_cache_utils.py
_project_kv_cache_groups_to_worker(global_kv_cache_groups, worker_spec) ¶
Projects global KV cache groups onto a single worker's assigned layers.
In pipeline parallelism, each worker only owns a subset of layers. This function filters the global groups to include only layers present on the given worker, adjusting UniformTypeKVCacheSpecs accordingly.
Parameters:
-
(global_kv_cache_groups¶list[KVCacheGroupSpec]) –The global KV cache groups for the whole model.
-
(worker_spec¶dict[str, KVCacheSpec]) –The KV cache spec of each layer on this worker.
Returns:
-
list[KVCacheGroupSpec]–The projected KV cache groups containing only this worker's layers.
Source code in vllm/v1/core/kv_cache_utils.py
_promote_local_kv_cache_specs(kv_cache_spec) ¶
Use full-attention allocation for local-attention cache specs.
The returned specs affect KV cache management only. Attention modules keep their original sliding-window or chunked-local compute behavior.
Source code in vllm/v1/core/kv_cache_utils.py
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_try_get_full_allocation_fallback_groups(kv_cache_spec) ¶
Try a supported full-allocation fallback for local-attention layers.
Source code in vllm/v1/core/kv_cache_utils.py
check_enough_kv_cache_memory(vllm_config, kv_cache_spec, available_memory) ¶
Checks whether available_memory is enough for the KV cache to hold at least one request with the model's max_model_len.
Parameters:
-
(vllm_config¶VllmConfig) –The global VllmConfig
-
(kv_cache_spec¶dict[str, KVCacheSpec]) –The kv cache spec of each attention layer in the model
-
(available_memory¶int) –Memory available for KV cache in bytes.
Raises:
-
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(kv_cache_spec, grouped_layer_names) ¶
Create KVCacheGroupSpec object for each kv cache group layer. The layers in the same group should share the same KVCacheSpec.
Parameters:
-
(kv_cache_spec¶dict[str, KVCacheSpec]) –A mapping from each layer name to its corresponding KVCacheSpec.
-
(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.
Returns: A list of KVCacheGroupSpec objects, one for each group.
Source code in vllm/v1/core/kv_cache_utils.py
estimate_max_model_len(vllm_config, kv_cache_spec, available_memory) ¶
Estimates the maximum model length that can fit in the available memory using binary search.
This function temporarily modifies max_model_len during estimation but restores the original value before returning, ensuring no side effects.
Parameters:
-
(vllm_config¶VllmConfig) –The global VllmConfig
-
(kv_cache_spec¶dict[str, KVCacheSpec]) –The kv cache spec of each attention layer in the model
-
(available_memory¶int) –Memory available for KV cache in bytes.
Returns:
-
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(request, start_token_idx, end_token_idx, start_mm_idx) ¶
Generate extra keys for the block hash. The extra keys can come from the multi-modal inputs, request specific metadata (e.g., LoRA names), and hashed data from prompt embeddings.
Parameters:
-
(request¶Request) –The request object.
-
(start_token_idx¶int) –The start token index of the block.
-
(end_token_idx¶int) –The end token index of the block.
-
(start_mm_idx¶int) –The start multi-modal index of the block.
Returns:
Source code in vllm/v1/core/kv_cache_utils.py
generate_scheduler_kv_cache_config(kv_cache_configs) ¶
Generate the KV cache configuration for the scheduler.
Source code in vllm/v1/core/kv_cache_utils.py
get_block_hash(key) ¶
get_group_id(key) ¶
get_kv_cache_capacity(vllm_config, kv_cache_config) ¶
Get the group-aware KV cache token capacity and max concurrency.
Source code in vllm/v1/core/kv_cache_utils.py
get_kv_cache_config_from_groups(vllm_config, kv_cache_groups, available_memory) ¶
Generate the KV cache configuration from the KV cache groups and spec of each layer.
Parameters:
-
(vllm_config¶VllmConfig) –The global VllmConfig
-
(kv_cache_groups¶list[KVCacheGroupSpec]) –The KV cache groups
-
(available_memory¶int) –Memory available for KV cache in bytes
Returns: The generated KVCacheConfig
Source code in vllm/v1/core/kv_cache_utils.py
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get_kv_cache_configs(vllm_config, kv_cache_specs, available_memory) ¶
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. This also handles spec unification for hybrid models. 3. Handle auto-fit max_model_len and memory checks using per-worker projected groups to account for PP sharding. 4. 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.) 5. Change the num_blocks of each worker to the smallest among all workers and shrink tensor sizes proportionally to avoid allocating unused memory.
Parameters:
-
(vllm_config¶VllmConfig) –The global VllmConfig
-
(kv_cache_specs¶list[dict[str, KVCacheSpec]]) –List of dict[layer_name, KVCacheSpec] for each worker.
-
(available_memory¶list[int]) –Memory available for KV cache in bytes for each worker.
Returns:
-
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(vllm_config, kv_cache_spec) ¶
Split the layers in the model into groups with the same KV cache spec.
Parameters:
-
(vllm_config¶VllmConfig) –The global VllmConfig
-
(kv_cache_spec¶dict[str, KVCacheSpec]) –The kv cache spec of each attention layer in the model
Returns:
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list[KVCacheGroupSpec]–The generated KVCacheGroups
Source code in vllm/v1/core/kv_cache_utils.py
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get_max_concurrency_for_kv_cache_config(vllm_config, kv_cache_config) ¶
Get the maximum concurrency for the given KV cache configuration.
A request at max_model_len consumes whole blocks from each group's block table — cdiv(per-request bytes, page bytes) of the group's spec — and all groups draw those block ids from one shared pool, so the per-request total is the sum over groups. The memory/page ratio is identical whether a group carries an aggregated UniformTypeKVCacheSpecs (worker config) or a representative per-layer spec (scheduler config), so both capacity call sites agree.
Source code in vllm/v1/core/kv_cache_utils.py
get_none_hash_seed() ¶
Return the seed NONE_HASH was derived from.
Components that must agree on NONE_HASH across processes (the P2P tier advertises this during its connect handshake) read the resolved seed here instead of re-deriving it, so they observe the random seed too. Falls back to the deterministic seed before init_none_hash has run.
Source code in vllm/v1/core/kv_cache_utils.py
get_request_block_hasher(hash_block_size, caching_hash_fn) ¶
Returns a function which computes the list of un-computed block hashes of a request.
Hashes are computed at hash_block_size granularity and chained over the full prefix, so each hash uniquely fingerprints the prefix ending at its boundary. Coarser group block sizes and partial-cache boundaries reuse these hashes directly (see BlockHashListWithBlockSize).
Source code in vllm/v1/core/kv_cache_utils.py
get_uniform_page_size(kv_cache_specs) ¶
Get the page size of the KV cache.
Source code in vllm/v1/core/kv_cache_utils.py
group_and_unify_kv_cache_specs(kv_cache_spec) ¶
Group the KV cache specs and unify each group into one UniformTypeKVCacheSpecs. Currently, this is only used for DeepseekV4.
Source code in vllm/v1/core/kv_cache_utils.py
hash_block_tokens(hash_function, parent_block_hash, curr_block_token_ids, extra_keys=None) ¶
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
is_kv_cache_spec_uniform(kv_cache_spec) ¶
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:
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(kv_cache_spec¶dict[str, KVCacheSpec]) –The kv cache spec of each attention layer in the model
Returns:
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bool–True if all layers have the same type, False otherwise.
Source code in vllm/v1/core/kv_cache_utils.py
make_block_hash_with_group_id(block_hash, group_id) ¶
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(vllm_config, kv_cache_specs) ¶
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(vllm_config, num_blocks) ¶
Override the number of kv cache blocks if num_gpu_blocks_override is set. The override is logged once, at the call site in get_kv_cache_configs.
Source code in vllm/v1/core/kv_cache_utils.py
resolve_block_hashes(block_hashes, hash_block_size, block_size, *, supports_fine_grained_hash_lookup=False, alignment_tokens=None) ¶
Resolve the block-hash view at block_size.
When block_size equals hash_block_size, reuse the precomputed block hashes directly; otherwise view them at block_size granularity. Fine-grained lookup keeps the original hashes for partial cache hits.
Source code in vllm/v1/core/kv_cache_utils.py
resolve_kv_cache_block_sizes(kv_cache_config, vllm_config) ¶
Resolve (scheduler_block_size, hash_block_size).
scheduler_block_sizeis the token-alignment invariant used by the scheduler (e.g. fornum_computed_tokensrounding). Single group:cache_config.block_size * dcp. Multiple groups: LCM of every group's effective block size. Attention groups are scaled by DCP; Mamba groups keep their full per-rank state and are not scaled.hash_block_sizeis the granularity at whichRequest.block_hashesis computed. Single group: equals scheduler block size. Multiple groups:cache_config.prefix_match_unitoverride if set, else the GCD of group block sizes; every group's block size must be divisible by it. Returns the scheduler block size (i.e. disables finer hashing) if block hashing is inactive or a mamba group is not using cache mode "align".
Source code in vllm/v1/core/kv_cache_utils.py
resolve_none_hash_seed(hash_fn) ¶
Resolve the seed to derive NONE_HASH from.
PYTHONHASHSEED wins if set. Otherwise cryptographic algorithms get the fixed default (shareable across processes) and non-cryptographic ones get fresh random bytes, keeping the seed unpredictable where collision resistance depends on it.
Source code in vllm/v1/core/kv_cache_utils.py
unify_hybrid_kv_cache_specs(kv_cache_spec) ¶
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:
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(kv_cache_spec¶dict[str, KVCacheSpec]) –The kv cache spec of each attention layer in the model
Source code in vllm/v1/core/kv_cache_utils.py
unify_kv_cache_spec_page_size(kv_cache_spec) ¶
Unify the page size of the given KVCacheSpec. If the page size of all layers are the same, return the original KVCacheSpec. If not same, unify the page size by increasing the block size of layers with smaller page size. Two cases cannot be unified by block size alone and pad their physical page to the maximum instead: Mamba layers, whose page size comes from state shapes and is independent of block size; and non-MLA attention layers whose page does not evenly divide the maximum (the padded page is read through a strided view). MLA is excluded because sparse MLA indexes the cache in whole token rows (see flat_kv_row_view), so its block stride can only be padded by its own row-aligned alignment, not to an arbitrary page size. Raise NotImplementedError if failed to unify the page size; get_kv_cache_groups catches it to try the full-allocation fallback (e.g. MLA next to an incompatible sliding-window draft).
Parameters:
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(kv_cache_spec¶dict[str, KVCacheSpec]) –The KVCacheSpec of each attention layer in the model
Returns:
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dict[str, KVCacheSpec]–The updated KVCacheSpec with the same page_size_bytes.
Source code in vllm/v1/core/kv_cache_utils.py
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update_kv_cache_capacity(vllm_config, kv_cache_config) ¶
Store and log the resolved KV cache capacity.
Source code in vllm/v1/core/kv_cache_utils.py
validate_kv_cache_layout(layout, kv_cache_groups) ¶
Validate that the resolved layout can express this model's packing.
The layout was chosen once in the engine core from the backends' supported sets; a backend whose model packs pages side by side (e.g. the DeepSeek-V4 indexer) declares block-outermost layouts there, so an inexpressible layout reaching this point is an error.