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Mojo struct

StandardTilePayload

struct StandardTilePayload[a_type: DType, b_type: DType, a_shape: IndexList[Int(2)], b_shape: IndexList[Int(2)], num_pipeline_stages: Int]

Tile payload for standard matmul (A and B tiles).

Uses explicit dimensions for tile arrays. The tiles are stored as TileTensor with row_major layout. TileTensors are passed directly to TMA/MMA. at TMA/MMA boundaries.

Parameters

  • a_type (DType): Element type of the A operand tiles.
  • b_type (DType): Element type of the B operand tiles.
  • a_shape (IndexList[Int(2)]): A tile dimensions as (BM, BK).
  • b_shape (IndexList[Int(2)]): B tile dimensions as (BN, BK).
  • num_pipeline_stages (Int): Number of pipeline buffer stages.

Fields

  • a_tiles (StandardTilePayload[a_type, b_type, a_shape, b_shape, num_pipeline_stages].ATileArray):
  • b_tiles (StandardTilePayload[a_type, b_type, a_shape, b_shape, num_pipeline_stages].BTileArray):

Implemented traits

AnyType, Copyable, Deinitable, ImplicitlyCopyable, Movable, RegisterPassable, TilePayload, TrivialRegisterPassable

comptime members

ATile

comptime ATile = StandardTilePayload[a_type, b_type, a_shape, b_shape, num_pipeline_stages].ATileArray.Tile

ATileArray

comptime ATileArray = SMemTileArray2D[a_type, a_shape[Int(0)], a_shape[Int(1)], num_pipeline_stages]

BTile

comptime BTile = StandardTilePayload[a_type, b_type, a_shape, b_shape, num_pipeline_stages].BTileArray.Tile

BTileArray

comptime BTileArray = SMemTileArray2D[b_type, b_shape[Int(0)], b_shape[Int(1)], num_pipeline_stages]

Methods

__init__

def __init__(a_tiles: SMemTileArray2D[a_type, a_shape[Int(0)], a_shape[Int(1)], num_pipeline_stages], b_tiles: SMemTileArray2D[b_type, b_shape[Int(0)], b_shape[Int(1)], num_pipeline_stages]) -> Self

get_tile

def get_tile[k_group_size: Int](self, stage: UInt32, k_idx: Int) -> Tuple[TileTensor[a_type, Layout[*?, *?], MutAnyOrigin, address_space=AddressSpace.SHARED], TileTensor[b_type, Layout[*?, *?], MutAnyOrigin, address_space=AddressSpace.SHARED]]

Get A and B tiles at the specified stage and k-group index.

Parameters:

  • k_group_size (Int): Number of K-group tiles stored per pipeline stage.

Args:

  • stage (UInt32): Pipeline stage index into the circular buffer.
  • k_idx (Int): K-group index within the current pipeline stage.

Returns:

Tuple[TileTensor[a_type, Layout[*?, *?], MutAnyOrigin, address_space=AddressSpace.SHARED], TileTensor[b_type, Layout[*?, *?], MutAnyOrigin, address_space=AddressSpace.SHARED]]

get_a_tile

def get_a_tile[k_group_size: Int](self, stage: UInt32, k_idx: Int) -> Self.ATile

Get A tile at the specified stage and k-group index.

Parameters:

  • k_group_size (Int): Number of K-group tiles stored per pipeline stage.

Args:

  • stage (UInt32): Pipeline stage index into the circular buffer.
  • k_idx (Int): K-group index within the current pipeline stage.

Returns:

Self.ATile

get_b_tile

def get_b_tile[k_group_size: Int](self, stage: UInt32, k_idx: Int) -> Self.BTile

Get B tile at the specified stage and k-group index.

Parameters:

  • k_group_size (Int): Number of K-group tiles stored per pipeline stage.

Args:

  • stage (UInt32): Pipeline stage index into the circular buffer.
  • k_idx (Int): K-group index within the current pipeline stage.

Returns:

Self.BTile