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Mojo struct
TileScheduler
struct TileScheduler[problem_shape: IndexList[Int(3)], tile_shape: IndexList[Int(3)], grid_shape: IndexList[Int(2)], cluster: IndexList[Int(3)] = Index[Int, Int, Int](Int(1), Int(1), Int(1)), raster_dim: UInt32 = UInt32(1), schedule: MatmulSchedule = MatmulSchedule.TILE2D]
Fields
- idx (
UInt32): - prob_shape (
IndexList[Int(3)]): - num_waves_m (
UInt32): - num_waves_n (
UInt32): - log_num_waves_n (
FastDiv[DType.uint32]): - current_iter (
Int): - num_aligned_m_blocks (
UInt32): - num_blocks (
UInt32):
Implemented traits
AnyType,
Copyable,
ImplicitlyCopyable,
ImplicitlyDeletable,
Movable,
RegisterPassable,
TrivialRegisterPassable
comptime members
kNum1DBlocksPerGroup
comptime kNum1DBlocksPerGroup = UInt32(16)
kNumNBlocks
comptime kNumNBlocks = SIMD(ceildiv(problem_shape[Int(1)], tile_shape[Int(1)]))
num_grids
comptime num_grids = SIMD(Int((mul grid_shape[Int(0)], grid_shape[Int(1)])))
wave_shape
comptime wave_shape = Index[Int, Int, dtype=DType.uint32](Int((mul grid_shape[Int(1)], tile_shape[Int(0)])), Int((mul grid_shape[Int(0)], tile_shape[Int(1)])))
Methods
__init__
def __init__(prob_shape: IndexList[Int(3)]) -> Self
get_current_work_info
def get_current_work_info(mut self) -> WorkInfo
Returns:
WorkInfo
advance
def advance(mut self)
fetch_next_work
def fetch_next_work(mut self) -> WorkInfo
Returns:
WorkInfo
num_output_tiles
fetch_next_work_ds
def fetch_next_work_ds(mut self) -> WorkInfo
Returns:
WorkInfo
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