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Mojo struct

SplitKTileScheduler

struct SplitKTileScheduler[locks_origin: MutOrigin, //, problem_shape_nk: IndexList[Int(2)], tile_shape: IndexList[Int(3)], splits: UInt32, num_consumer: UInt32, num_pipeline_stages: UInt32, cluster_shape: IndexList[Int(2)], raster_order: RasterOrder, reduction_mode: ReductionMode = ReductionMode.Deterministic]

Fields​

  • ​prob_shape (IndexList[Int(3)]):
  • ​block_id_in_cluster (IndexList[Int(2)]):
  • ​blocks_per_problem (UInt32):
  • ​current_work_linear_idx (UInt32):
  • ​log_cluster_shape_major (UInt32):
  • ​log_cluster_shape_minor (UInt32):
  • ​cluster_blk_major (UInt32):
  • ​locks_ptr (UnsafePointer[Int32, locks_origin]):

Implemented traits​

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

comptime members​

k_tiles_per_output_tile​

comptime k_tiles_per_output_tile = SIMD(ceildiv(problem_shape_nk[Int(1)], tile_shape[Int(2)]))

k_tiles_per_split​

comptime k_tiles_per_split = (SIMD(ceildiv(problem_shape_nk[Int(1)], tile_shape[Int(2)])) // splits)

log_cluster_size​

comptime log_cluster_size = log2_floor(Int((mul cluster_shape[Int(0)], cluster_shape[Int(1)])))

WorkTileType​

comptime WorkTileType[dtype: DType, layout: Layout] = LayoutTensor[dtype, layout, MutAnyOrigin]

Parameters​

Methods​

__init__​

def __init__(prob_shape: IndexList[Int(3)], block_id_in_cluster: IndexList[Int(2)], locks_ptr: UnsafePointer[UInt8, locks_origin]) -> Self

get_sm_num​

def get_sm_num(self) -> UInt32

Returns:

UInt32

get_problem_blocks_shape​

static def get_problem_blocks_shape(problem_shape: IndexList[Int(3)], dyn_tile_shape: IndexList[Int(3)], dyn_cluster_shape: IndexList[Int(2)]) -> IndexList[Int(2)]

Returns:

IndexList[Int(2)]

initial_work_tile_info​

def initial_work_tile_info(mut self) -> WorkInfo

Returns:

WorkInfo

get_current_work_info​

def get_current_work_info(mut self) -> WorkInfo

Returns:

WorkInfo

get_worktile_m_n_idx​

def get_worktile_m_n_idx(mut self, mut work_tile_info: WorkInfo, linear_tile_id: UInt32)

assign_work​

def assign_work(mut self, mut work_tile_info: WorkInfo, linear_idx: UInt32)

get_k_start_and_linear_tile_id​

def get_k_start_and_linear_tile_id(mut self, mut work_tile_info: WorkInfo, linear_idx: UInt32) -> UInt32

Returns:

UInt32

fetch_next_work​

def fetch_next_work(mut self, mut work_tile_info: WorkInfo) -> WorkInfo

Returns:

WorkInfo

requires_reduction​

def requires_reduction(self, work_tile_info: WorkInfo) -> Bool

Returns:

Bool

advance_to_next_work​

def advance_to_next_work(mut self)

is_last_split​

def is_last_split(self, work_tile_info: WorkInfo) -> Bool

Returns:

Bool

get_grid_shape​

static def get_grid_shape(dyn_cluster_shape: IndexList[Int(3)], dyn_raster_order: RasterOrder = RasterOrder.AlongN) -> IndexList[Int(3)]

Returns:

IndexList[Int(3)]

get_num_tiles​

static def get_num_tiles(problem_shape: IndexList[Int(3)], dyn_tile_shape: IndexList[Int(3)], dyn_cluster_shape: IndexList[Int(2)]) -> Int

Returns:

Int

get_required_locks_buffer_size_bytes​

static def get_required_locks_buffer_size_bytes[accum_type: DType, dyn_num_consumer: UInt32](problem_shape: IndexList[Int(3)], dyn_tile_shape: IndexList[Int(3)], dyn_cluster_shape: IndexList[Int(2)]) -> Int

Returns:

Int

get_linear_idx_from_m_and_n​

def get_linear_idx_from_m_and_n(self, tile_m: UInt32, tile_n: UInt32) -> UInt32

Returns:

UInt32

output_tile_index​

def output_tile_index(self, work_tile_info: WorkInfo) -> UInt32

Returns:

UInt32

reduction​

def reduction[accum_type: DType, c_reg_layout: Layout, workspace_layout: TensorLayout](self, reduction_workspace: TileTensor[accum_type, workspace_layout], c_reg_tile: LayoutTensor[accum_type, c_reg_layout, MutAnyOrigin, address_space=AddressSpace.LOCAL], work_tile_info: WorkInfo, num_barriers: UInt32, warp_group_local_idx: UInt32)

def reduction[accum_type: DType, c_reg_layout: Layout, workspace_layout: Layout](self, reduction_workspace: LayoutTensor[accum_type, workspace_layout, MutAnyOrigin], c_reg_tile: LayoutTensor[accum_type, c_reg_layout, MutAnyOrigin, address_space=AddressSpace.LOCAL], work_tile_info: WorkInfo, num_barriers: UInt32, warp_group_local_idx: UInt32)

wait_eq​

static def wait_eq(lock_ptr: UnsafePointer[Int32], barrier_id: Int32, barrier_group_thread_idx: Int, lock_idx: UInt32, val: UInt32)

wait_lt​

static def wait_lt(lock_ptr: UnsafePointer[Int32], barrier_id: Int32, barrier_group_thread_idx: Int, lock_idx: UInt32, count: UInt32)

arrive_set​

static def arrive_set(lock_ptr: UnsafePointer[Int32], barrier_id: Int32, barrier_group_thread_idx: Int, lock_idx: UInt32, increment: UInt32)

store_accumulator​

def store_accumulator[accum_type: DType, c_reg_layout: Layout, workspace_layout: Layout](self, reduction_workspace: LayoutTensor[accum_type, workspace_layout, MutAnyOrigin], c_reg_tile: LayoutTensor[accum_type, c_reg_layout, MutAnyOrigin, address_space=AddressSpace.LOCAL], reduction_tile_idx: UInt32, warp_group_local_idx: UInt32, warp_group_thread_idx: UInt32)

reduce_add​

def reduce_add[accum_type: DType, c_reg_layout: Layout, workspace_layout: Layout, //, *, write_back: Bool](self, reduction_workspace: LayoutTensor[accum_type, workspace_layout, MutAnyOrigin], c_reg_tile: LayoutTensor[accum_type, c_reg_layout, MutAnyOrigin, address_space=AddressSpace.LOCAL], reduction_tile_idx: UInt32, warp_group_local_idx: UInt32, warp_group_thread_idx: UInt32)

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