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Mojo struct

MLAPrefillSparseFP8

struct MLAPrefillSparseFP8[KVLUTType: MHAOperand, output_dtype: DType, config: MLASparseConfig[config.qkv_dtype, config.b_topk_, config.num_mbars_, config.q_smem_depth_, config.q_tmem_depth_, config.cta_group_]]

Implemented traits​

AnyType, Copyable, ImplicitlyCopyable, ImplicitlyDeletable, Movable, RegisterPassable, TrivialRegisterPassable

comptime members​

accum_dtype​

comptime accum_dtype = DType.float32

B_TOPK_PER_CTA​

comptime B_TOPK_PER_CTA = (config.B_TOPK // config.cta_group)

Common​

comptime Common = MLAPrefillSparseCommon[KVLUTType, output_dtype, config]

CTA_MASK​

comptime CTA_MASK = UInt16(3) if (config.cta_group == Int(2)) else UInt16(1)

FP8_K_SWIZZLE​

comptime FP8_K_SWIZZLE = TensorMapSwizzle.SWIZZLE_NONE

FP8_V_SWIZZLE​

comptime FP8_V_SWIZZLE = TensorMapSwizzle.SWIZZLE_NONE

FULL_Q_TYPE​

comptime FULL_Q_TYPE = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].SMemType.FULL_Q_TYPE

k_desc_shape​

comptime k_desc_shape = Index[Int, Int](Int(1), _gather4_box_width[qkv_dtype, config.qk_depth, TensorMapSwizzle(Int32(3))]())

k_gather_box​

comptime k_gather_box = _gather4_box_width[MLAPrefillSparseFP8[KVLUTType, output_dtype, config].qkv_dtype, config.qk_depth, MLAPrefillSparseFP8[KVLUTType, output_dtype, config].k_swizzle_mode]()

k_swizzle_mode​

comptime k_swizzle_mode = config.k_swizzle_mode

k_tile_height​

comptime k_tile_height = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].B_TOPK_PER_CTA

k_tile_shape​

comptime k_tile_shape = Index[Int, Int](MLAPrefillSparseFP8[KVLUTType, output_dtype, config].k_tile_height, _gather4_box_width[qkv_dtype, config.qk_depth, TensorMapSwizzle(Int32(3))]())

k_tile_width​

comptime k_tile_width = config.qk_depth

k_tma_desc_shape_fp8​

comptime k_tma_desc_shape_fp8 = Index[Int, Int](Int(1), _gather4_box_width[DType.int64, (config // Int(8)), TensorMapSwizzle(Int32(0))]())

k_tma_dtype_fp8​

comptime k_tma_dtype_fp8 = DType.int64

k_tma_gather_box_fp8​

comptime k_tma_gather_box_fp8 = _gather4_box_width[DType.int64, (config // Int(8)), MLAPrefillSparseFP8[KVLUTType, output_dtype, config].k_tma_swizzle_fp8]()

k_tma_swizzle_fp8​

comptime k_tma_swizzle_fp8 = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].FP8_K_SWIZZLE

k_tma_tile_shape_fp8​

comptime k_tma_tile_shape_fp8 = Index[Int, Int](MLAPrefillSparseFP8[KVLUTType, output_dtype, config].k_tile_height, _gather4_box_width[DType.int64, (config // Int(8)), TensorMapSwizzle(Int32(0))]())

k_tma_tile_width_fp8​

comptime k_tma_tile_width_fp8 = (config // Int(8))

NUM_Q_HEADS_PER_CTA​

comptime NUM_Q_HEADS_PER_CTA = (config // config.cta_group)

NUM_SV_ATOMS​

comptime NUM_SV_ATOMS = 2

O_ATOM_PHYS_COLS​

comptime O_ATOM_PHYS_COLS = ((config // Int(2)) // Int(2))

o_desc_shape​

comptime o_desc_shape = Index[Int, Int]((config // cta_group_), Int(64))

o_tile_shape​

comptime o_tile_shape = Index[Int, Int]((config // cta_group_), config)

O_TMEM_ADDR​

comptime O_TMEM_ADDR = 0

O_TMEM_ADDR_ATOM2​

comptime O_TMEM_ADDR_ATOM2 = (Int(0) + ((config // Int(2)) // Int(2)))

O_TYPE​

comptime O_TYPE = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].SMemType.O_TYPE

P_TMEM_ADDR​

comptime P_TMEM_ADDR = 256

PADDED_HEADS_PER_CTA​

comptime PADDED_HEADS_PER_CTA = (config // config.cta_group)

q_desc_shape​

comptime q_desc_shape = _default_desc_shape[Int(3), MLAPrefillSparseFP8[KVLUTType, output_dtype, config].qkv_dtype, MLAPrefillSparseFP8[KVLUTType, output_dtype, config].q_tile_shape, config.q_swizzle_mode]()

q_smem_depth​

comptime q_smem_depth = config.q_smem_depth

q_tile_shape​

comptime q_tile_shape = Index[Int, Int, Int](Int(1), (config // cta_group_), config)

Q_TMEM_ADDR​

comptime Q_TMEM_ADDR = (Int(512) - (MLAPrefillSparseFP8[KVLUTType, output_dtype, config].q_tmem_depth // Int(2)))

q_tmem_depth​

comptime q_tmem_depth = config.q_tmem_depth

QKMMAOpType​

comptime QKMMAOpType = QKMMAOp[MLAPrefillSparseFP8[KVLUTType, output_dtype, config].qkv_dtype, DType.float32, config]

qkv_dtype​

comptime qkv_dtype

qkv_dtype_size​

comptime qkv_dtype_size = size_of[MLAPrefillSparseFP8[KVLUTType, output_dtype, config].qkv_dtype]()

SHARED_QKV_TYPE​

comptime SHARED_QKV_TYPE = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].SMemType.SHARED_QKV_TYPE

SMemType​

comptime SMemType = MLASparseSharedMemory[config]

SV_ATOM_MMA_N​

comptime SV_ATOM_MMA_N = (config // Int(2))

SVMMAType​

comptime SVMMAType = SVMMAType[MLAPrefillSparseFP8[KVLUTType, output_dtype, config].qkv_dtype, DType.float32, config]

V_BMN_PER_ATOM​

comptime V_BMN_PER_ATOM = ((config // Int(2)) // config.cta_group)

V_DEPTH_PER_CTA​

comptime V_DEPTH_PER_CTA = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].SV_ATOM_MMA_N

v_desc_shape​

comptime v_desc_shape = Index[Int, Int](Int(1), _gather4_box_width[qkv_dtype, Int((mul ((config // Int(2)) // cta_group_), 2)), TensorMapSwizzle(Int32(3))]())

v_gather_box​

comptime v_gather_box = _gather4_box_width[MLAPrefillSparseFP8[KVLUTType, output_dtype, config].qkv_dtype, Int((mul ((config // Int(2)) // cta_group_), 2)), MLAPrefillSparseFP8[KVLUTType, output_dtype, config].v_swizzle_mode]()

V_SMEM_COLS_PER_CTA​

comptime V_SMEM_COLS_PER_CTA = (((config // Int(2)) // cta_group_) * Int(2))

v_swizzle_mode​

comptime v_swizzle_mode = TensorMapSwizzle.SWIZZLE_128B

v_tile_height​

comptime v_tile_height = (config.B_TOPK // Int(2))

v_tile_shape​

comptime v_tile_shape = Index[Int, Int](MLAPrefillSparseFP8[KVLUTType, output_dtype, config].v_tile_height, _gather4_box_width[qkv_dtype, Int((mul ((config // Int(2)) // cta_group_), 2)), TensorMapSwizzle(Int32(3))]())

v_tile_width​

comptime v_tile_width = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].V_SMEM_COLS_PER_CTA

v_tma_desc_shape_fp8​

comptime v_tma_desc_shape_fp8 = Index[Int, Int](Int(1), _gather4_box_width[DType.int64, (((config // Int(2)) // cta_group_) // Int(8)), TensorMapSwizzle(Int32(0))]())

v_tma_dtype_fp8​

comptime v_tma_dtype_fp8 = DType.int64

v_tma_gather_box_fp8​

comptime v_tma_gather_box_fp8 = _gather4_box_width[DType.int64, (((config // Int(2)) // cta_group_) // Int(8)), MLAPrefillSparseFP8[KVLUTType, output_dtype, config].v_tma_swizzle_fp8]()

v_tma_swizzle_fp8​

comptime v_tma_swizzle_fp8 = MLAPrefillSparseFP8[KVLUTType, output_dtype, config].FP8_V_SWIZZLE

v_tma_tile_height_fp8​

comptime v_tma_tile_height_fp8 = config.B_TOPK

v_tma_tile_shape_fp8​

comptime v_tma_tile_shape_fp8 = Index[Int, Int](MLAPrefillSparseFP8[KVLUTType, output_dtype, config].v_tma_tile_height_fp8, _gather4_box_width[DType.int64, (((config // Int(2)) // cta_group_) // Int(8)), TensorMapSwizzle(Int32(0))]())

v_tma_tile_width_fp8​

comptime v_tma_tile_width_fp8 = (((config // Int(2)) // cta_group_) // Int(8))

Methods​

load_k_fp8_tma​

static def load_k_fp8_tma(k_tma_op_fp8: TMATensorTile[DType.int64, Int(2), Self.k_tma_tile_shape_fp8, Self.k_tma_desc_shape_fp8], indices: TileTensor[DType.uint32, Storage=indices.Storage, linear_idx_type=indices.linear_idx_type], kv_lut: KVLUTType, k_smem_fp8_ptr: Pointer[Float8_e4m3fn, address_space=AddressSpace.SHARED, _safe=False], k_fp8_tma_done: Pointer[SharedMemBarrier, address_space=AddressSpace.SHARED, _safe=False], k: UInt32, cta_id: UInt32, warp_idx: UInt32, indices_base: UInt32)

load_k_scales_to_smem​

static def load_k_scales_to_smem[scale_block_size: Int](scales_ptr: Pointer[Float32, ImmutAnyOrigin, _safe=False], indices: TileTensor[DType.uint32, Storage=indices.Storage, linear_idx_type=indices.linear_idx_type], kv_lut: KVLUTType, k_scales_smem_ptr: Pointer[Float32, address_space=AddressSpace.SHARED, _safe=False], k: UInt32, cta_id: UInt32, indices_base: UInt32, num_kv_rows: Int32)

convert_k_fp8_to_bf16​

static def convert_k_fp8_to_bf16[scale_block_size: Int](k_smem_fp8_ptr: Pointer[Float8_e4m3fn, address_space=AddressSpace.SHARED, _safe=False], k_smem_bf16_ptr: Pointer[Scalar[Self.qkv_dtype], address_space=AddressSpace.SHARED, _safe=False], k_scales_smem_ptr: Pointer[Float32, address_space=AddressSpace.SHARED, _safe=False])

load_v_fp8_tma​

static def load_v_fp8_tma(v_tma_op_fp8: TMATensorTile[DType.int64, Int(2), Self.v_tma_tile_shape_fp8, Self.v_tma_desc_shape_fp8], indices: TileTensor[DType.uint32, Storage=indices.Storage, linear_idx_type=indices.linear_idx_type], kv_lut: KVLUTType, v_smem_fp8_ptr: Pointer[Float8_e4m3fn, address_space=AddressSpace.SHARED, _safe=False], v_fp8_tma_done: Pointer[SharedMemBarrier, address_space=AddressSpace.SHARED, _safe=False], k: UInt32, cta_id: UInt32, warp_idx: UInt32, indices_base: UInt32)

load_v_scales_to_smem​

static def load_v_scales_to_smem[scale_block_size: Int](scales_ptr: Pointer[Float32, ImmutAnyOrigin, _safe=False], indices: TileTensor[DType.uint32, Storage=indices.Storage, linear_idx_type=indices.linear_idx_type], kv_lut: KVLUTType, v_scales_smem_ptr: Pointer[Float32, address_space=AddressSpace.SHARED, _safe=False], k: UInt32, indices_base: UInt32, num_kv_rows: Int32)

convert_v_fp8_to_bf16​

static def convert_v_fp8_to_bf16[scale_block_size: Int](v_smem_fp8_ptr: Pointer[Float8_e4m3fn, address_space=AddressSpace.SHARED, _safe=False], v_smem_bf16_ptr: Pointer[Scalar[Self.qkv_dtype], address_space=AddressSpace.SHARED, _safe=False], v_scales_smem_ptr: Pointer[Float32, address_space=AddressSpace.SHARED, _safe=False], cta_id: UInt32)

kernel_fp8​

static def kernel_fp8[TopKLengthLayout: TensorLayout, IndicesLayout: TensorLayout, scale_block_size: Int](q_tma_op: TMATensorTile[Self.qkv_dtype, Int(3), Self.q_tile_shape, Self.q_desc_shape], k_tma_op_fp8: TMATensorTile[DType.int64, Int(2), Self.k_tma_tile_shape_fp8, Self.k_tma_desc_shape_fp8], v_tma_op_fp8: TMATensorTile[DType.int64, Int(2), Self.v_tma_tile_shape_fp8, Self.v_tma_desc_shape_fp8], o_tma_op: TMATensorTile[output_dtype, Int(2), Self.o_tile_shape, Self.o_desc_shape], topk_lengths: TileTensor[DType.uint32, TopKLengthLayout, MutAnyOrigin], indices: TileTensor[DType.uint32, IndicesLayout, MutAnyOrigin], kv_lut: KVLUTType, scale: Float32, attn_sink_ptr: Optional[Pointer[Float32, ImmutAnyOrigin, _safe=False]], indices_stride: Int32, output_gmem_ptr: Pointer[Scalar[output_dtype], MutAnyOrigin, _safe=False], scales_ptr: Pointer[Float32, ImmutAnyOrigin, _safe=False]) where (Int(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(IndicesLayout.__shape_types), [idx: __mlir_type.index] IndicesLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType)))) == Int(1)) if (Int(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType)))) == Int(1)) else (Int(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(TopKLengthLayout.__shape_types), [idx: __mlir_type.index] TopKLengthLayout.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType)))) == Int(1))

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