Skip to main content

/

DocsCode

Mojo struct

BlackwellBlockwiseFP8MatmulKernel

struct BlackwellBlockwiseFP8MatmulKernel[a_type: DType, b_type: DType, c_type: DType, a_scales_type: DType, b_scales_type: DType, a_layout: Layout, b_layout: Layout, c_layout: Layout, a_scales_layout: Layout, b_scales_layout: Layout, a_desc_layout: Layout, b_desc_layout: Layout, c_desc_layout: Layout, a_scales_desc_layout: Layout, transpose_b: Bool, config: MatmulConfig[a_type, b_type, c_type, transpose_b], cluster_shape: StaticTuple[Int32, 3] = StaticTuple[Int32, 3](1)]

Blockwise FP8 matmul kernel with register-based accumulation.

This kernel implements per-K-iteration scaling in CUDA cores:

  1. Load warp: TMA loads A, B, A-scales to SMEM
  2. MMA warp: Standard MMA (partial to TMEM)
  3. Epilogue warp: TMEM read → scale → register accumulate → output

Implemented traits​

AnyType, ImplicitlyDestructible

comptime members​

__del__is_trivial​

comptime __del__is_trivial = True

a_expected_bytes​

comptime a_expected_bytes = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].a_smem_layout.size() * size_of[a_type]())

a_scales_expected_bytes​

comptime a_scales_expected_bytes = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].a_scales_smem_layout.size() * size_of[a_scales_type]())

a_scales_smem_layout​

comptime a_scales_smem_layout = Layout.row_major(1, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BM)

a_smem_layout​

comptime a_smem_layout = tile_layout_k_major[a_type, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BM, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BK, config.a_swizzle]()

a_tma_load_size​

comptime a_tma_load_size = a_desc_layout.size()

a_tma_rows​

comptime a_tma_rows = a_desc_layout.shape[0].value()

accum_layout​

comptime accum_layout = get_accumulator_layout[c_smem_layout=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].c_smem_layout, block_tile_shape=config.block_tile_shape, mma_shape=config.mma_shape, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]()

accum_pipeline_consumer_arv_count​

comptime accum_pipeline_consumer_arv_count = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group * BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].EPILOGUE_THREADS)

accum_pipeline_producer_arv_count​

comptime accum_pipeline_producer_arv_count = 1

accum_type​

comptime accum_type = DType.float32

AccumTensor​

comptime AccumTensor = TmemTensor[DType.float32, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].tmem_accum_layout, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

Accumulator​

comptime Accumulator = BlockwiseFP8Accumulator[DType.float32, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].accum_layout, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].is_lower_required, config.block_tile_shape, config.mma_shape, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].CLUSTER_SIZE]

AScalesLoaderType​

comptime AScalesLoaderType = ScalesTileLoader[?, ?, ?, ?, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

ATileLoaderType​

comptime ATileLoaderType = TileLoaderTMA[?, ?, ?, ?, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

b_expected_bytes​

comptime b_expected_bytes = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].b_smem_layout.size() * size_of[b_type]())

b_smem_layout​

comptime b_smem_layout = tile_layout_k_major[b_type, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BN, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BK, config.b_swizzle]() if transpose_b else tile_layout_mn_major[b_type, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BN, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].BK, config.b_swizzle]()

b_tma_load_size​

comptime b_tma_load_size = b_desc_layout.size()

b_tma_rows​

comptime b_tma_rows = b_desc_layout.shape[0].value()

BK​

comptime BK = config.block_tile_shape.__getitem__[3, DType.int64, Int](2)

BM​

comptime BM = config.block_tile_shape.__getitem__[3, DType.int64, Int](0)

BN​

comptime BN = config.block_tile_shape.__getitem__[3, DType.int64, Int](1)

BTileLoaderType​

comptime BTileLoaderType = TileLoaderTMA[?, ?, ?, ?, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

c_smem_layout​

comptime c_smem_layout = Layout.row_major(BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].OutputM, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].OutputN)

clc_consumer_arv_count​

comptime clc_consumer_arv_count = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SCHEDULER_THREADS + (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].CLUSTER_SIZE * ((BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].TMA_LOAD_THREADS + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].MMA_THREADS) + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].EPILOGUE_THREADS)))

clc_producer_arv_count​

comptime clc_producer_arv_count = 1

clc_throttle_consumer_arv_count​

comptime clc_throttle_consumer_arv_count = BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SCHEDULER_THREADS

clc_throttle_producer_arv_count​

comptime clc_throttle_producer_arv_count = BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].TMA_LOAD_THREADS

CLUSTER_M​

comptime CLUSTER_M = Int.__init__[Int](config.cluster_shape.__getitem__[3, DType.int64, Int](0))

CLUSTER_N​

comptime CLUSTER_N = Int.__init__[Int](config.cluster_shape.__getitem__[3, DType.int64, Int](1))

CLUSTER_SIZE​

comptime CLUSTER_SIZE = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].CLUSTER_M * BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].CLUSTER_N)

Context​

comptime Context = KernelContext[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_clc_pipeline_stages, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].CLUSTER_M, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].CLUSTER_N]

cta_group​

comptime cta_group = config.cta_group

EPILOGUE_THREADS​

comptime EPILOGUE_THREADS = (4 * WARP_SIZE)

EpilogueCtx​

comptime EpilogueCtx = EpilogueWarpContext[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_accum_pipeline_stages, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].stage_stride_cols, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].MMA_THREADS, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].EPILOGUE_THREADS]

input_expected_bytes​

comptime input_expected_bytes = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group * ((BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].a_expected_bytes + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].b_expected_bytes) + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].a_scales_expected_bytes))

InputTilePipeline​

comptime InputTilePipeline = InputTilePipeline[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].TilePayload, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.num_group_pipeline_stages, Int.__init__[Int](config.k_group_size)]

is_lower_required​

comptime is_lower_required = is_lower_fragment_required[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, config.block_tile_shape]()

max_tmem_cols​

comptime max_tmem_cols = 512

MMA_K​

comptime MMA_K = config.mma_shape.__getitem__[3, DType.int64, Int](2)

MMA_M​

comptime MMA_M = config.mma_shape.__getitem__[3, DType.int64, Int](0)

MMA_N​

comptime MMA_N = config.mma_shape.__getitem__[3, DType.int64, Int](1)

MMA_THREADS​

comptime MMA_THREADS = WARP_SIZE

MmaCtx​

comptime MmaCtx = MmaWarpContext[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_accum_pipeline_stages, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].stage_stride_cols, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].MMA_THREADS, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].EPILOGUE_THREADS]

MmaOp​

comptime MmaOp = MmaOpSM100_SS[c_type, a_type, b_type, config.block_tile_shape, config.mma_shape, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, cluster_shape=config.cluster_shape, a_swizzle=config.a_swizzle, b_swizzle=config.b_swizzle, transpose_b=transpose_b]

num_accum_pipeline_stages​

comptime num_accum_pipeline_stages = config.num_accum_pipeline_stages

num_clc_pipeline_stages​

comptime num_clc_pipeline_stages = config.num_clc_pipeline_stages

num_group_pipeline_stages​

comptime num_group_pipeline_stages = (BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_pipeline_stages // config)

num_output_stages​

comptime num_output_stages = Int.__init__[Int](config.num_output_stages)

num_output_warps​

comptime num_output_warps = 4

num_pipeline_stages​

comptime num_pipeline_stages = config.num_pipeline_stages

NUM_THREADS​

comptime NUM_THREADS = (((BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SCHEDULER_THREADS + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].TMA_LOAD_THREADS) + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].MMA_THREADS) + BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].EPILOGUE_THREADS)

NUM_TMEM_COLS​

comptime NUM_TMEM_COLS = 512

OutputM​

comptime OutputM = config.output_tile_shape.__getitem__[2, DType.int64, Int](0)

OutputN​

comptime OutputN = config.output_tile_shape.__getitem__[2, DType.int64, Int](1)

OutputPipeline​

comptime OutputPipeline = OutputTilePipeline[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_accum_pipeline_stages, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].stage_stride_cols, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

Scheduler​

comptime Scheduler = TileScheduler[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_clc_pipeline_stages, Index[dtype=DType.uint32](config.cluster_shape.__getitem__[3, DType.int64, Int](0), config.cluster_shape.__getitem__[3, DType.int64, Int](1), config.cluster_shape.__getitem__[3, DType.int64, Int](2)), config.raster_order, config.block_swizzle_size]

SCHEDULER_THREADS​

comptime SCHEDULER_THREADS = WARP_SIZE

SmemType​

comptime SmemType = BlockwiseFP8Smem[a_type, b_type, c_type, a_scales_type, transpose_b, config=config]

stage_stride_cols​

comptime stage_stride_cols = (512 // config)

TilePayload​

comptime TilePayload = BlockwiseFP8TilePayload[a_type, b_type, a_scales_type, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.a_smem_layout, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.b_smem_layout, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.a_scales_smem_layout, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.num_pipeline_stages]

TileWriterType​

comptime TileWriterType = BlockwiseFP8TileWriter[c_type, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].c_smem_layout, DType.float32, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].accum_layout, block_tile_shape=config.block_tile_shape, mma_shape=config.mma_shape, is_lower_frag_required=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].is_lower_required, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, num_output_stages=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_output_stages, num_output_warps=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].num_output_warps, c_swizzle=config.c_swizzle]

TMA_LOAD_THREADS​

comptime TMA_LOAD_THREADS = WARP_SIZE

Tmem​

comptime Tmem = TmemAllocation[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

tmem_accum_layout​

comptime tmem_accum_layout = Layout.row_major(BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].MMA_M, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].MMA_N)

TmemDealloc​

comptime TmemDealloc = TmemDeallocBarrier[BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group]

Methods​

load_input_tiles​

static load_input_tiles[a_tma_origin: ImmutOrigin, b_tma_origin: ImmutOrigin, a_scales_tma_origin: ImmutOrigin, tiles_origin: MutOrigin, //](a_loader: TileLoaderTMA[a_tma_origin, a_type, a_layout, a_desc_layout, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group], b_loader: TileLoaderTMA[b_tma_origin, b_type, b_layout, b_desc_layout, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group], a_scales_loader: ScalesTileLoader[a_scales_tma_origin, a_scales_type, a_scales_layout, a_scales_desc_layout, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group], tiles: InputProducerStage[tiles_origin, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].TilePayload, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.num_group_pipeline_stages, Int.__init__[Int](config.k_group_size)], peer_cta_coord: Tuple[UInt, UInt, UInt], work_tile_coord: Tuple[UInt, UInt], iter_idx: Scalar[DType.uindex], elect_one_cta: Bool)

Load A, B, and A-scales tiles using TMA.

Args:

  • ​a_loader (TileLoaderTMA): TileLoaderTMA for A matrix.
  • ​b_loader (TileLoaderTMA): TileLoaderTMA for B matrix.
  • ​a_scales_loader (ScalesTileLoader): ScalesTileLoader for A-scales.
  • ​tiles (InputProducerStage): InputProducerStage context with encapsulated tile access.
  • ​peer_cta_coord (Tuple): Peer CTA coordinates for multicast.
  • ​work_tile_coord (Tuple): Current work tile M/N coordinates.
  • ​iter_idx (Scalar): K iteration index.
  • ​elect_one_cta (Bool): Whether this is the elected CTA in the cluster.

mma​

static mma[tiles_origin: MutOrigin, //](tiles: InputConsumerStage[tiles_origin, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].TilePayload, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].SmemType.num_group_pipeline_stages, Int.__init__[Int](config.k_group_size)], mma_op: MmaOpSM100_SS[c_type, a_type, b_type, config.block_tile_shape, config.mma_shape, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group, cluster_shape=config.cluster_shape, a_swizzle=config.a_swizzle, b_swizzle=config.b_swizzle, transpose_b=transpose_b], accum_tensor: TmemTensor[DType.float32, BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].tmem_accum_layout, cta_group=BlackwellBlockwiseFP8MatmulKernel[a_type, b_type, c_type, a_scales_type, b_scales_type, a_layout, b_layout, c_layout, a_scales_layout, b_scales_layout, a_desc_layout, b_desc_layout, c_desc_layout, a_scales_desc_layout, transpose_b, config, cluster_shape].cta_group])

Execute standard MMA operations (partial results to TMEM).

For blockwise FP8, each K iteration writes a fresh partial to TMEM. The epilogue accumulates across K in registers, not TMEM. Therefore init_c is always True (unlike standard matmul).

Args:

  • ​tiles (InputConsumerStage): Input consumer stage with A, B, A-scales tiles.
  • ​mma_op (MmaOpSM100_SS): The MMA operator.
  • ​accum_tensor (TmemTensor): Typed TMEM tensor view for the accumulator stage.

validate_config​

static validate_config()

Validate configuration constraints at compile time.

run​

static run(a_tma_op: TMATensorTile[a_type, a_layout, a_desc_layout], b_tma_op: TMATensorTile[b_type, b_layout, b_desc_layout], c_tma_op: TMATensorTile[c_type, c_layout, c_desc_layout], a_scales_tma_op: TMATensorTile[a_scales_type, a_scales_layout, a_scales_desc_layout], cluster_dim: StaticTuple[Int32, 3], num_iters: Scalar[DType.uindex], b_scales: LayoutTensor[b_scales_type, b_scales_layout, MutAnyOrigin], problem_shape: StaticTuple[Int32, 3])

Kernel entry point for blockwise FP8 matmul.

View source

View source

Was this page helpful?

Models

Cookbook

Puzzles

Blog

Community

Contact