Mojo struct
ContinuousBatchingKVCache
@register_passable(trivial)
struct ContinuousBatchingKVCache[dtype_: DType, kv_params_: KVCacheStaticParams]
Wrapper for the ContinuousKVCache of a given layer in the transformer model.
This abstracts the Pointer indirection for accessing the ContinuousKVCache for a given batch entry.
THIS IS THE TYPE THAT IS PASSED TO KV PROJECTION AND FLASH ATTENTION KERNELS.
Parameters
- dtype_ (
DType): The dtype of the kv-cache. - kv_params_ (
KVCacheStaticParams): The kv-cache static parameters.
Fields
- blocks (
NDBuffer[dtype_, 4, MutableAnyOrigin, DimList(Dim(-31337), Dim(-31337), Dim(kv_params_.num_heads), Dim(kv_params_.head_size)), _strides_from_shape[::DimList,::Int]()]): - cache_lengths (
NDBuffer[uint32, 1, MutableAnyOrigin]): - lookup_table (
NDBuffer[uint32, 1, MutableAnyOrigin]): - max_seq_length (
SIMD[uint32, 1]): - max_cache_length (
SIMD[uint32, 1]):
Implemented traits
AnyType,
Copyable,
ExplicitlyCopyable,
KVCacheT,
Movable,
UnknownDestructibility
Aliases
blocks_shape
alias blocks_shape = DimList(Dim(-31337), Dim(-31337), Dim(kv_params_.num_heads), Dim(kv_params_.head_size))
blocks_stride
alias blocks_stride = _strides_from_shape[::DimList,::Int]()
blocks_type
alias blocks_type = NDBuffer[dtype_, 4, MutableAnyOrigin, DimList(Dim(-31337), Dim(-31337), Dim(kv_params_.num_heads), Dim(kv_params_.head_size)), _strides_from_shape[::DimList,::Int]()]
dtype
alias dtype = dtype_
kv_params
alias kv_params = kv_params_
Methods
__init__
__init__(blocks: NDBuffer[dtype_, 4, MutableAnyOrigin, DimList(Dim(-31337), Dim(-31337), Dim(kv_params_.num_heads), Dim(kv_params_.head_size)), _strides_from_shape[::DimList,::Int]()], cache_lengths: NDBuffer[uint32, 1, MutableAnyOrigin], lookup_table: NDBuffer[uint32, 1, MutableAnyOrigin], max_seq_length: SIMD[uint32, 1], max_cache_length: SIMD[uint32, 1]) -> Self
max_tile_size
cache_lengths_nd
cache_length
load
load[width: Int](self, bs: Int, head_idx: Int, tok_idx: Int, head_dim_idx: Int) -> SIMD[dtype_, width]
Returns:
store
store(self, bs: Int, head_idx: Int, tok_idx: Int, head_dim_idx: Int, val: SIMD[dtype_, size])
empty_cache
empty_cache(self) -> Bool
Returns true if the cache_lengths for all requests is 0, false otherwise.
Returns:
max_prompt_length
max_prompt_length(self) -> SIMD[uint32, 1]
Returns the maximum sequence length across all batches of the current request.
Returns:
max_context_length
max_context_length(self) -> SIMD[uint32, 1]
Returns the maximum cache length used across all batches of the current request.
Returns:
row_idx
row_idx(self, batch_idx: SIMD[uint32, 1], tok_idx: SIMD[uint32, 1]) -> SIMD[uint32, 1]
Returns the row idx when viewing the memory as a matrix.
Returns:
col_idx
col_idx(self, head_idx: SIMD[uint32, 1]) -> SIMD[uint32, 1]
Returns the col idx when viewing the memory as a matrix.
Returns:
create_tma_tile
create_tma_tile[tile_m: Int, tile_n: Int, swizzle_mode: TensorMapSwizzle, *, is_k_major: Bool](self, ctx: DeviceContext) -> TMATensorTile[dtype_, tile_layout_k_major[::DType,::Int,::Int,::TensorMapSwizzle]() if is_k_major else tile_layout_mn_major[::DType,::Int,::Int,::TensorMapSwizzle](), _tma_desc_tile_layout[::DType,::Int,::IndexList[$1, ::DType(), is_k_major]
Creates a TMA tile for this KV cache.
Returns:
TMATensorTile
block_paged_ptr
block_paged_ptr[tile_size: Int](self, batch_idx: Int, start_tok_idx: Int, head_idx: Int, head_dim_idx: Int = 0) -> UnsafePointer[SIMD[dtype_, 1]]
Returns:
UnsafePointer
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