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Mojo struct
KVCacheScalesMHAOperand
struct KVCacheScalesMHAOperand[cache_t: KVCacheT]
An MHAOperand that accesses the scales field of a KVCache.
This is useful for MLA attention where k_s (per-token scales) are stored in the scales field of the k cache with quantization_granularity = head_size. The scales have shape [num_blocks, page_size, num_heads, head_dim_granularity].
Parameters
- cache_t (
KVCacheT): The concreteKVCacheTtype whose scales field this operand accesses.
Fields
- cache (
cache_t):
Implemented traits
AnyType,
Copyable,
Deinitable,
DevicePassable,
ImplicitlyCopyable,
MHAOperand,
Movable,
RegisterPassable,
TrivialRegisterPassable
comptime members
device_type
comptime device_type = KVCacheScalesMHAOperand[cache_t]
dtype
comptime dtype = cache_t.scale_dtype
page_size
comptime page_size = cache_t.page_size_
quantization_enabled
comptime quantization_enabled = cache_t.quantization_enabled
quantization_granularity
comptime quantization_granularity = cache_t.quantization_granularity
scale_dtype
comptime scale_dtype = KVCacheScalesMHAOperand[cache_t].dtype
Methods
__init__
def __init__(cache: cache_t) -> Self
get_type_name
block_paged_ptr
def block_paged_ptr[tile_size: Int](self, batch_idx: UInt32, start_tok_idx: UInt32, head_idx: UInt32, head_dim_idx: UInt32 = UInt32(0)) -> Pointer[Scalar[Self.dtype], ImmutAnyOrigin]
Returns:
Pointer[Scalar[Self.dtype], ImmutAnyOrigin]
scales_block_paged_ptr
def scales_block_paged_ptr(self, batch_idx: Int, start_tok_idx: Int, head_idx: Int, head_dim_idx: Int = Int(0)) -> Pointer[Scalar[Self.scale_dtype], ImmutAnyOrigin]
Returns:
Pointer[Scalar[Self.scale_dtype], ImmutAnyOrigin]
load_scale
def load_scale[width: Int](self, batch_idx: Int, start_tok_idx: Int, head_idx: Int, head_dim_idx: Int) -> SIMD[Self.scale_dtype, width]
Returns:
cache_length
max_context_length
num_kv_rows
def num_kv_rows(self) -> Int
Returns the total number of virtual rows in the KV memory view.
Returns:
row_idx
def row_idx(self, batch_idx: UInt32, start_tok_idx: UInt32) -> UInt32
Returns the row idx when viewing the memory as a matrix.
Returns:
get_tma_row
def get_tma_row(self, encoded_index: Int32) -> Int32
Convert an encoded sparse index to a physical TMA row.
Returns:
create_tma_tile
def create_tma_tile[swizzle_mode: TensorMapSwizzle, *, BN: Int, depth: Int, BK: Int = padded_depth[cache_t.scale_dtype, swizzle_mode, depth](), fold_chunks: Int = Int(1), row_major: Bool = False](self, ctx: DeviceContext, out tma: TMATensorTile[Self.dtype, Int(3), _padded_shape[Int(3), Self.dtype, IndexList(BN, Int(1), BK, __list_literal__=NoneType(None)), swizzle_mode](), _ragged_shape[Int(3), Self.dtype, IndexList(BN, Int(1), BK, __list_literal__=NoneType(None)), swizzle_mode]()])
TMA not supported for KVCacheScalesMHAOperand.
Returns:
create_scale_tma_tile
def create_scale_tma_tile[BMN: Int](self, ctx: DeviceContext, out tma: TMATensorTile[Self.scale_dtype, Int(2), Index[Int, Int](Int(1), BMN)])
Returns:
TMATensorTile[Self.scale_dtype, Int(2), Index[Int, Int](Int(1), BMN)]
create_rope_tma_tile
def create_rope_tma_tile[swizzle_mode: TensorMapSwizzle, *, BN: Int, BK: Int, padded_depth: Int](self, ctx: DeviceContext, out tma: TMATensorTile[DType.bfloat16, Int(3), _padded_shape[Int(3), DType.bfloat16, IndexList(BN, Int(1), BK, __list_literal__=NoneType(None)), swizzle_mode](), _ragged_shape[Int(3), DType.bfloat16, IndexList(BN, Int(1), BK, __list_literal__=NoneType(None)), swizzle_mode]()])
Not supported for KVCacheScalesMHAOperand.
Returns:
create_gather4_tma_tile
def create_gather4_tma_tile[tile_width: Int, tile_stride: Int = tile_width, swizzle_mode: TensorMapSwizzle = TensorMapSwizzle.SWIZZLE_NONE, tile_height: Int = Int(4), tma_dtype: DType = KVCacheScalesMHAOperand[cache_t].dtype, l2_promotion: TensorMapL2Promotion = TensorMapL2Promotion.NONE](self, ctx: DeviceContext, out tma: TMATensorTile[tma_dtype, Int(2), IndexList(tile_height, _gather4_box_width[tma_dtype, tile_width, swizzle_mode](), __list_literal__=NoneType(None)), IndexList(Int(1), _gather4_box_width[tma_dtype, tile_width, swizzle_mode](), __list_literal__=NoneType(None))])
Not supported for KVCacheScalesMHAOperand.
Parameters:
- tile_width (
Int): Number of elements per row to load (box width) intma_dtypeelements. - tile_stride (
Int): Row stride in elements in global memory. Defaults totile_width. - swizzle_mode (
TensorMapSwizzle): TMA swizzle mode for shared memory access pattern. Defaults toSWIZZLE_NONE. - tile_height (
Int): Number of rows in the tile. Must be a multiple of 4. Defaults to 4. - tma_dtype (
DType): Data type used for the TMA descriptor. Defaults toSelf.dtype. - l2_promotion (
TensorMapL2Promotion): L2 cache promotion hint for TMA loads. Defaults toNONE.
Args:
- ctx (
DeviceContext): The CUDA device context used to create the TMA descriptor.
Returns:
create_rope_gather4_tma_tile
def create_rope_gather4_tma_tile[tile_width: Int, padded_depth: Int, swizzle_mode: TensorMapSwizzle = TensorMapSwizzle.SWIZZLE_NONE, tile_height: Int = Int(4), l2_promotion: TensorMapL2Promotion = TensorMapL2Promotion.NONE](self, ctx: DeviceContext, out tma: TMATensorTile[DType.bfloat16, Int(2), IndexList(tile_height, _gather4_box_width[DType.bfloat16, tile_width, swizzle_mode](), __list_literal__=NoneType(None)), IndexList(Int(1), _gather4_box_width[DType.bfloat16, tile_width, swizzle_mode](), __list_literal__=NoneType(None))])
Not supported for KVCacheScalesMHAOperand.
Returns:
scales_raw_ptr
def scales_raw_ptr(self) -> Pointer[Float32, MutAnyOrigin]
Returns a dangling pointer. KVCacheScalesMHAOperand already points to the scales pointer.
Returns:
Pointer[Float32, MutAnyOrigin]