# Based on the cute-dsl example: # https://github.com/NVIDIA/cutlass/blob/main/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py import argparse from typing import Optional, Type, Tuple, Union, Callable, Literal from functools import partial import cuda.bindings.driver as cuda import torch import cutlass import cutlass.cute as cute from cutlass.cute.nvgpu import cpasync, tcgen05 import cutlass.torch as cutlass_torch import cutlass.pipeline as pipeline import cutlass.utils.blackwell_helpers as sm100_utils import cutlass.utils.blockscaled_layout as blockscaled_utils from cutlass.cute.nvgpu.warp import ( LdMatrix8x8x16bOp, LdMatrix16x16x8bOp, StMatrix8x8x16bOp, StMatrix16x8x8bOp, ) from cutlass import Int32, Float32, Boolean, const_expr from cutlass.utils import LayoutEnum from cutlass.cute.runtime import from_dlpack, make_ptr from quack.pipeline import PipelineTmaCpAsyncUmma from quack.cute_dsl_utils import ParamsBase, ArgumentsBase from quack.tile_scheduler import TileSchedulerOptions from quack.varlen_utils import VarlenArguments, VarlenManager from quack.gemm_sm90 import GemmSm90, NamedBarrierGemm import quack.copy_utils as copy_utils import quack.sm100_utils as quack_sm100_utils # return PipelineStateWAdvance instead of PipelineState """ A high-performance persistent batched dense GEMM example for the NVIDIA Blackwell SM100 architecture using CUTE DSL. - Matrix A is MxKxL, L is batch dimension, A can be row-major("K") or column-major("M") - Matrix B is NxKxL, L is batch dimension, B can be row-major("N") or column-major("K") - Matrix C is MxNxL, L is batch dimension, C can be row-major("N") or column-major("M") This GEMM kernel supports the following features: - Utilizes Tensor Memory Access (TMA) for efficient memory operations - Utilizes Blackwell's tcgen05.mma for matrix multiply-accumulate (MMA) operations (including 2cta mma instructions) - Implements TMA multicast with cluster to reduce L2 memory traffic - Support persistent tile scheduling to better overlap memory load/store with mma between tiles - Support warp specialization to avoid explicit pipelining between mainloop load and mma This GEMM works as follows: 1. DMA warp: Load A and B matrices from global memory (GMEM) to shared memory (SMEM) using TMA operations. 2. MMA warp: Perform matrix multiply-accumulate (MMA) operations using tcgen05.mma instruction. 3. EPILOGUE warp: - Load completed accumulator from tensor memory (TMEM) to registers (RMEM) using tcgen05.ld. - Type convert C matrix to output type. - Optionally store C matrix from registers (RMEM) to shared memory (SMEM) to global memory (GMEM) with TMA operations, or directly store C matrix from registers (RMEM) to global memory (GMEM) without TMA operations. SM100 tcgen05.mma instructions operate as follows: - Read matrix A from SMEM - Read matrix B from SMEM - Write accumulator to TMEM The accumulator in TMEM must then be loaded to registers before writing back to GMEM. Input arguments to this example is same as dense_gemm.py. .. code-block:: bash python examples/blackwell/dense_gemm_persistent.py \ --ab_dtype Float16 --d_dtype Float16 --acc_dtype Float32 \ --mma_tiler_mn 256,128 --cluster_shape_mn 2,1 \ --mnkl 8192,8192,8192,1 \ --use_2cta_instrs To collect performance with NCU profiler: .. code-block:: bash ncu python examples/blackwell/dense_gemm_persistent.py \ --ab_dtype Float16 --d_dtype Float16 --acc_dtype Float32 \ --mma_tiler_mn 256,128 --cluster_shape_mn 2,1 \ --mnkl 8192,8192,8192,1 \ --use_2cta_instrs \ --warmup_iterations 1 --iterations 10 --skip_ref_check Constraints are same as dense_gemm.py: * Supported input data types: fp16, bf16, tf32, int8, uint8, fp8 (e4m3fn, e5m2), see detailed valid dtype combinations in below GemmSm100 class documentation * A/B tensor must have the same data type * Mma tiler M must be 64/128 (use_2cta_instrs=False) or 128/256 (use_2cta_instrs=True) * Mma tiler N must be 32-256, step 32 * Cluster shape M/N must be positive and power of 2, total cluster size <= 16 * Cluster shape M must be multiple of 2 if use_2cta_instrs=True * The contiguous dimension of A/B/C tensors must be at least 16 bytes aligned, i.e, number of elements is a multiple of 4, 8, and 16 for TFloat32, Float16/BFloat16, and Int8/Uint8/Float8, respectively. * OOB tiles are not allowed when TMA store is disabled """ class GemmSm100(GemmSm90): """This class implements batched matrix multiplication (C = A x B) with support for various data types and architectural features specific to Blackwell GPUs with persistent tile scheduling and warp specialization. :param acc_dtype: Data type for accumulation during computation :type acc_dtype: type[cutlass.Numeric] :param mma_tiler_mn: Shape of the Matrix Multiply-Accumulate (MMA) tile (M,N) :type mma_tiler_mn: Tuple[int, int] :param cluster_shape_mn: Cluster dimensions (M,N) for parallel processing :type cluster_shape_mn: Tuple[int, int] :note: In current version, A and B tensor must have the same data type - i.e., Float8E4M3FN for A and Float8E5M2 for B is not supported :note: Supported A/B data types: - TFloat32 - Float16/BFloat16 - Int8/Uint8 - Float8E4M3FN/Float8E5M2 :note: Supported accumulator data types: - Float32 (for all floating point A/B data types) - Float16 (only for fp16 and fp8 A/B data types) - Int32 (only for uint8/int8 A/B data types) :note: Supported C data types: - Float32 (for float32 and int32 accumulator data types) - Int32 (for float32 and int32 accumulator data types) - Float16/BFloat16 (for fp16 and fp8 accumulator data types) - Int8/Uint8 (for uint8/int8 accumulator data types) - Float8E4M3FN/Float8E5M2 (for float32 accumulator data types) :note: Constraints: - MMA tiler M must be 64/128 (use_2cta_instrs=False) or 128/256 (use_2cta_instrs=True) - MMA tiler N must be 32-256, step 32 - Cluster shape M must be multiple of 2 if use_2cta_instrs=True - Cluster shape M/N must be positive and power of 2, total cluster size <= 16 Example: >>> gemm = GemmSm100( ... acc_dtype=Float32, ... mma_tiler_mn=(128, 128), ... cluster_shape_mn=(2, 2) ... ) >>> gemm(mA, mB, mD, max_active_clusters, stream) """ arch = 100 num_epi_tensormaps = GemmSm90.num_epi_tensormaps EpilogueArguments = GemmSm90.EpilogueArguments EpilogueParams = GemmSm90.EpilogueParams def __init__( self, acc_dtype: Type[cutlass.Numeric], a_dtype: Type[cutlass.Numeric], # ignored for now mma_tiler_mn: Tuple[int, int], cluster_shape_mnk: Tuple[int, int, int], sf_vec_size: Optional[int] = None, gather_A: bool = False, ): """Initializes the configuration for a Blackwell dense GEMM kernel. This configuration includes several key aspects: 1. MMA Instruction Settings (tcgen05): - acc_dtype: Data types for MMA accumulator. - mma_tiler_mn: The (M, N) shape of the MMA instruction tiler. - use_2cta_instrs: Boolean indicating if the tcgen05 MMA variant with cta_group=2 should be used. 2. Cluster Shape: - cluster_shape_mnk: The (ClusterM, ClusterN) shape of the CTA cluster. :param acc_dtype: Data type of the accumulator. :type acc_dtype: type[cutlass.Numeric] :param mma_tiler_mn: Tuple (M, N) shape of the MMA instruction. :type mma_tiler_mn: Tuple[int, int] :param cluster_shape_mnk: Tuple (ClusterM, ClusterN) shape of the cluster. :type cluster_shape_mnk: Tuple[int, int] """ self.acc_dtype: Type[cutlass.Numeric] = acc_dtype self.use_2cta_instrs = cluster_shape_mnk[0] == 2 and mma_tiler_mn[0] in (256,) self.cluster_shape_mnk = cluster_shape_mnk assert cluster_shape_mnk[2] == 1, "Cluster shape K must be 1" # K dimension is deferred in _setup_attributes self.mma_tiler = (*mma_tiler_mn, 1) self.sf_vec_size = sf_vec_size self.blockscaled = sf_vec_size is not None self.is_persistent = True self.pingpong = False # for compatibility with GemmSm90 self.gather_A = gather_A if gather_A: assert cluster_shape_mnk[1] == 1, "Cluster shape N must be 1 for gather A " self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE self.num_ab_load_warps = 1 if not self.gather_A else 5 self.occupancy = 1 # Set specialized warp ids self.epilog_warp_id = (0, 1, 2, 3) self.mma_warp_id = 4 self.ab_load_warp_id = 5 self.epi_load_warp_id = self.ab_load_warp_id + self.num_ab_load_warps self.scheduler_warp_id = self.epi_load_warp_id + 1 self.num_epi_warps = len(self.epilog_warp_id) self.threads_per_cta = cute.arch.WARP_SIZE * ( self.num_ab_load_warps + len( ( self.mma_warp_id, self.epi_load_warp_id, self.scheduler_warp_id, *self.epilog_warp_id, ) ) ) def _setup_attributes(self, epilogue_args: EpilogueArguments, varlen_args: VarlenArguments): """Set up configurations that are dependent on GEMM inputs This method configures various attributes based on the input tensor properties (data types, leading dimensions) and kernel settings: - Configuring tiled MMA - Computing MMA/cluster/tile shapes - Computing cluster layout - Computing multicast CTAs for A/B - Computing epilogue subtile - Setting up A/B/C stage counts in shared memory - Computing A/B/C shared memory layout - Computing tensor memory allocation columns """ # Compute mma instruction shapes mma_inst_bits_k = 256 # (MMA_Tile_Shape_M, MMA_Tile_Shape_N, MMA_Inst_Shape_K) self.mma_inst_shape_mnk = ( self.mma_tiler[0], self.mma_tiler[1], mma_inst_bits_k // self.a_dtype.width, ) # (CTA_Tile_Shape_M, Round_Up(MMA_Tile_Shape_N, 128), MMA_Inst_Shape_K) self.mma_inst_shape_mnk_sfb = ( self.mma_inst_shape_mnk[0] // (2 if self.use_2cta_instrs else 1), cute.round_up(self.mma_inst_shape_mnk[1], 128), self.mma_inst_shape_mnk[2], ) # Configure tiled mma if const_expr(not self.blockscaled): self.tiled_mma = sm100_utils.make_trivial_tiled_mma( self.a_dtype, self.a_major_mode, self.b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2], ) self.tiled_mma_sfb = None else: self.tiled_mma = sm100_utils.make_blockscaled_trivial_tiled_mma( self.a_dtype, self.a_major_mode, self.b_major_mode, self.sf_dtype, self.sf_vec_size, self.cta_group, self.mma_inst_shape_mnk[:2], ) self.tiled_mma_sfb = sm100_utils.make_blockscaled_trivial_tiled_mma( self.a_dtype, self.a_major_mode, self.b_major_mode, self.sf_dtype, self.sf_vec_size, tcgen05.CtaGroup.ONE, self.mma_inst_shape_mnk_sfb[:2], ) # Compute mma/cluster/tile shapes mma_inst_tile_k = 4 self.mma_tiler = ( self.mma_inst_shape_mnk[0], self.mma_inst_shape_mnk[1], self.mma_inst_shape_mnk[2] * mma_inst_tile_k, ) if const_expr(self.blockscaled): self.mma_tiler_sfb = ( self.mma_inst_shape_mnk_sfb[0], self.mma_inst_shape_mnk_sfb[1], self.mma_inst_shape_mnk_sfb[2] * mma_inst_tile_k, ) else: self.mma_tiler_sfb = None self.cta_tile_shape_mnk = ( self.mma_tiler[0] // cute.size(self.tiled_mma.thr_id.shape), self.mma_tiler[1], self.mma_tiler[2], ) # Compute cluster layout self.cluster_layout_vmnk = cute.tiled_divide( cute.make_layout(self.cluster_shape_mnk), (self.tiled_mma.thr_id.shape,), ) if const_expr(self.blockscaled): self.cluster_layout_sfb_vmnk = cute.tiled_divide( cute.make_layout(self.cluster_shape_mnk), (self.tiled_mma_sfb.thr_id.shape,), ) else: self.cluster_layout_sfb_vmnk = None # Compute number of multicast CTAs for A/B self.num_mcast_ctas_a = cute.size(self.cluster_layout_vmnk.shape[2]) if self.gather_A: assert self.num_mcast_ctas_a == 1 self.num_mcast_ctas_b = cute.size(self.cluster_layout_vmnk.shape[1]) self.is_a_mcast = self.num_mcast_ctas_a > 1 self.is_b_mcast = self.num_mcast_ctas_b > 1 if const_expr(self.blockscaled): self.num_mcast_ctas_sfb = cute.size(self.cluster_layout_sfb_vmnk.shape[1]) self.is_sfb_mcast = self.num_mcast_ctas_sfb > 1 # Compute epilogue subtile self.epi_tile = sm100_utils.compute_epilogue_tile_shape( self.cta_tile_shape_mnk, self.use_2cta_instrs, self.d_layout if self.d_layout is not None else LayoutEnum.ROW_MAJOR, self.d_dtype if self.d_dtype is not None else cutlass.BFloat16, layout_c=self.c_layout, elem_ty_c=self.c_dtype, ) # Setup A/B/C stage count in shared memory and ACC stage count in tensor memory prefetch_A_idx = ( None if not self.gather_A else ("varlen_m" if varlen_args.mCuSeqlensM is not None else "varlen_k") ) ( self.num_acc_stage, self.ab_stage, self.epi_stage, self.epi_c_stage, ) = self._compute_stages( self.tiled_mma, self.mma_tiler, self.cta_tile_shape_mnk, self.epi_tile, self.a_dtype, self.b_dtype, self.sf_dtype, self.sf_vec_size, self.d_dtype, self.c_dtype, self.d_layout, self.c_layout, epilogue_args, prefetch_A_idx, cutlass.utils.get_smem_capacity_in_bytes(f"sm_{self.arch}"), # smem_capacity self.occupancy, ) self.sched_stage = 1 self.a_prefetch_stage = ( 0 if not self.gather_A else (2 if varlen_args.mCuSeqlensM is not None else self.ab_stage) ) # Compute A/B/SFA/SFB/C shared memory layout self.a_smem_layout_staged = sm100_utils.make_smem_layout_a( self.tiled_mma, self.mma_tiler, self.a_dtype, self.ab_stage ) self.a_smem_load_layout_staged = self.a_smem_layout_staged if const_expr(self.gather_A): self.a_smem_load_layout_staged = quack_sm100_utils.make_smem_layout_cpasync_a( self.tiled_mma, self.mma_tiler, self.a_dtype, self.ab_stage ) self.b_smem_layout_staged = sm100_utils.make_smem_layout_b( self.tiled_mma, self.mma_tiler, self.b_dtype, self.ab_stage ) self.epi_smem_layout_staged = None if const_expr(self.d_dtype is not None): self.epi_smem_layout_staged = sm100_utils.make_smem_layout_epi( self.d_dtype, self.d_layout, self.epi_tile, self.epi_stage ) self.epi_c_smem_layout_staged = None if const_expr(self.c_dtype is not None): self.epi_c_smem_layout_staged = sm100_utils.make_smem_layout_epi( self.c_dtype, self.c_layout, self.epi_tile, self.epi_c_stage ) if const_expr(self.blockscaled): self.sfa_smem_layout_staged = blockscaled_utils.make_smem_layout_sfa( self.tiled_mma, self.mma_tiler, self.sf_vec_size, self.ab_stage, ) self.sfb_smem_layout_staged = blockscaled_utils.make_smem_layout_sfb( self.tiled_mma, self.mma_tiler, self.sf_vec_size, self.ab_stage, ) else: self.sfa_smem_layout_staged, self.sfb_smem_layout_staged = None, None # Compute the number of tensor memory allocation columns if const_expr(not self.blockscaled): self.num_tmem_alloc_cols = self._compute_num_tmem_alloc_cols( self.tiled_mma, self.mma_tiler, self.num_acc_stage ) else: SM100_TMEM_CAPACITY_COLUMNS = 512 self.num_tmem_alloc_cols = SM100_TMEM_CAPACITY_COLUMNS @cute.jit def __call__( self, mA: cute.Tensor, mB: cute.Tensor, mD: Optional[cute.Tensor], mC: Optional[cute.Tensor], epilogue_args: ArgumentsBase, scheduler_args: TileSchedulerOptions, varlen_args: Optional[VarlenArguments], stream: cuda.CUstream, mSFA: Optional[cute.Tensor] = None, mSFB: Optional[cute.Tensor] = None, ): """Execute the GEMM operation in steps: - Setup static attributes before smem/grid/tma computation - Setup TMA load/store atoms and tensors - Compute grid size with regard to hardware constraints - Define shared storage for kernel - Launch the kernel synchronously :param mA: Input tensor A :type mA: cute.Tensor :param mB: Input tensor B :type mB: cute.Tensor :param mD: Output tensor D :type mD: cute.Tensor :param max_active_clusters: Maximum number of active clusters :type max_active_clusters: cutlass.Constexpr :param stream: CUDA stream for asynchronous execution :type stream: cuda.CUstream :raises TypeError: If input data types are incompatible with the MMA instruction. :raises AssertionError: If OOB (Out-Of-Bounds) tiles are present when TMA store is disabled. """ if const_expr(self.blockscaled): assert mSFA is not None and mSFB is not None # Setup static attributes before smem/grid/tma computation self.a_dtype = mA.element_type self.b_dtype = mB.element_type self.d_dtype = mD.element_type if mD is not None else None self.c_dtype = mC.element_type if mC is not None else None self.sf_dtype: Optional[Type[cutlass.Numeric]] = ( mSFA.element_type if mSFA is not None else None ) self.a_layout = LayoutEnum.from_tensor(mA) self.b_layout = LayoutEnum.from_tensor(mB) self.d_layout = LayoutEnum.from_tensor(mD) if mD is not None else None self.c_layout = LayoutEnum.from_tensor(mC) if mC is not None else None self.a_major_mode = LayoutEnum.from_tensor(mA).mma_major_mode() self.b_major_mode = LayoutEnum.from_tensor(mB).mma_major_mode() # Check if input data types are compatible with MMA instruction if const_expr(self.a_dtype != self.b_dtype): raise TypeError(f"Type must match: {self.a_dtype} != {self.b_dtype}") if const_expr(varlen_args is None): varlen_args = VarlenArguments() assert (varlen_args.mAIdx is not None) == self.gather_A # Assume all strides are divisible by 128 bits except the last stride new_stride = lambda t: tuple( cute.assume(s, divby=128 // t.element_type.width) if not cute.is_static(s) else s for s in t.stride ) mA, mD = [ cute.make_tensor(t.iterator, cute.make_layout(t.shape, stride=new_stride(t))) if t is not None else None for t in (mA, mD) ] # Setup attributes that dependent on gemm inputs self._setup_attributes(epilogue_args, varlen_args) if const_expr(self.blockscaled): # Setup sfa/sfb tensor by filling A/B tensor to scale factor atom layout # ((Atom_M, Rest_M),(Atom_K, Rest_K),RestL) sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(mA.shape, self.sf_vec_size) mSFA = cute.make_tensor(mSFA.iterator, sfa_layout) # ((Atom_N, Rest_N),(Atom_K, Rest_K),RestL) sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(mB.shape, self.sf_vec_size) mSFB = cute.make_tensor(mSFB.iterator, sfb_layout) atom_thr_size = cute.size(self.tiled_mma.thr_id.shape) # Setup TMA load for A & B a_smem_layout = cute.slice_(self.a_smem_layout_staged, (None, None, None, 0)) b_smem_layout = cute.slice_(self.b_smem_layout_staged, (None, None, None, 0)) tma_atom_a, tma_tensor_a = None, None if const_expr(not self.gather_A): a_op = sm100_utils.cluster_shape_to_tma_atom_A( self.cluster_shape_mnk, self.tiled_mma.thr_id ) tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A( a_op, mA, a_smem_layout, self.mma_tiler, self.tiled_mma, self.cluster_layout_vmnk.shape, internal_type=(cutlass.TFloat32 if mA.element_type is Float32 else None), ) b_op = sm100_utils.cluster_shape_to_tma_atom_B( self.cluster_shape_mnk, self.tiled_mma.thr_id ) tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B( b_op, mB, b_smem_layout, self.mma_tiler, self.tiled_mma, self.cluster_layout_vmnk.shape, internal_type=(cutlass.TFloat32 if mB.element_type is Float32 else None), ) tma_atom_sfa, tma_tensor_sfa = None, None tma_atom_sfb, tma_tensor_sfb = None, None if const_expr(self.blockscaled): # Setup TMA load for SFA sfa_op = sm100_utils.cluster_shape_to_tma_atom_A( self.cluster_shape_mnk, self.tiled_mma.thr_id ) sfa_smem_layout = cute.slice_(self.sfa_smem_layout_staged, (None, None, None, 0)) tma_atom_sfa, tma_tensor_sfa = cute.nvgpu.make_tiled_tma_atom_A( sfa_op, mSFA, sfa_smem_layout, self.mma_tiler, self.tiled_mma, self.cluster_layout_vmnk.shape, internal_type=cutlass.Int16, ) # Setup TMA load for SFB sfb_op = sm100_utils.cluster_shape_to_tma_atom_SFB( self.cluster_shape_mnk, self.tiled_mma.thr_id ) sfb_smem_layout = cute.slice_(self.sfb_smem_layout_staged, (None, None, None, 0)) tma_atom_sfb, tma_tensor_sfb = cute.nvgpu.make_tiled_tma_atom_B( sfb_op, mSFB, sfb_smem_layout, self.mma_tiler_sfb, self.tiled_mma_sfb, self.cluster_layout_sfb_vmnk.shape, internal_type=cutlass.Int16, ) self.num_tma_load_bytes = cute.size_in_bytes(self.b_dtype, b_smem_layout) if const_expr(not self.gather_A): self.num_tma_load_bytes += cute.size_in_bytes(self.a_dtype, a_smem_layout) if const_expr(self.blockscaled): sfa_copy_size = cute.size_in_bytes(self.sf_dtype, sfa_smem_layout) sfb_copy_size = cute.size_in_bytes(self.sf_dtype, sfb_smem_layout) self.num_tma_load_bytes += sfa_copy_size + sfb_copy_size self.num_tma_load_bytes *= atom_thr_size # Setup TMA store for D tma_atom_d, tma_tensor_d = None, None if const_expr(mD is not None): tma_atom_d, tma_tensor_d = self._make_tma_epi_atoms_and_tensors( mD, self.epi_smem_layout_staged, self.epi_tile, op_type="store" if not (hasattr(epilogue_args, "add_to_output") and epilogue_args.add_to_output) else "add", ) tma_atom_c, tma_tensor_c = None, None if const_expr(mC is not None): tma_atom_c, tma_tensor_c = self._make_tma_epi_atoms_and_tensors( mC, self.epi_c_smem_layout_staged, self.epi_tile, op_type="load" ) epilogue_params = self.epi_to_underlying_arguments(epilogue_args) varlen_params = VarlenManager.to_underlying_arguments(varlen_args) TileSchedulerCls = self.get_scheduler_class(varlen_m=varlen_args.mCuSeqlensM is not None) tile_sched_args = self.get_scheduler_arguments(mA, mB, mD, scheduler_args, varlen_args) tile_sched_params = TileSchedulerCls.to_underlying_arguments(tile_sched_args) grid = TileSchedulerCls.get_grid_shape( tile_sched_params, scheduler_args.max_active_clusters ) self.buffer_align_bytes = 1024 epi_smem_size = cute.cosize(self.epi_smem_layout_staged) if mD is not None else 0 epi_c_smem_size = cute.cosize(self.epi_c_smem_layout_staged) if mC is not None else 0 sf_dtype = self.sf_dtype if const_expr(self.blockscaled) else cutlass.Float8E8M0FNU sfa_smem_size = ( cute.cosize(self.sfa_smem_layout_staged) if const_expr(self.blockscaled) else 0 ) sfb_smem_size = ( cute.cosize(self.sfb_smem_layout_staged) if const_expr(self.blockscaled) else 0 ) a_idx_smem_size = 0 if const_expr(self.gather_A): a_idx_smem_size = self.a_prefetch_stage * ( self.cta_tile_shape_mnk[0] if varlen_args.mCuSeqlensM is not None else self.cta_tile_shape_mnk[2] ) # Define shared storage for kernel @cute.struct class SharedStorage: ab_pipeline_array_ptr: cute.struct.MemRange[cutlass.Int64, self.ab_stage * 2] epi_pipeline_array_ptr: cute.struct.MemRange[cutlass.Int64, self.epi_c_stage * 2] acc_pipeline_array_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] sched_pipeline_array_ptr: cute.struct.MemRange[cutlass.Int64, self.sched_stage * 2] a_prefetch_pipeline_array_ptr: cute.struct.MemRange[ cutlass.Int64, self.a_prefetch_stage * 2 ] tile_count: cute.struct.MemRange[Int32, self.sched_stage] tmem_dealloc_mbar_ptr: cutlass.Int64 tmem_holding_buf: Int32 sAIdx: cute.struct.Align[cute.struct.MemRange[Int32, a_idx_smem_size], 16] # (EPI_TILE_M, EPI_TILE_N, STAGE) sD: cute.struct.Align[ cute.struct.MemRange[ self.d_dtype if self.d_dtype is not None else Int32, epi_smem_size ], self.buffer_align_bytes, ] sC: cute.struct.Align[ cute.struct.MemRange[ self.c_dtype if self.c_dtype is not None else Int32, epi_c_smem_size ], self.buffer_align_bytes, ] epi: self.epi_get_smem_struct(epilogue_params) # (MMA, MMA_M, MMA_K, STAGE) sA: cute.struct.Align[ cute.struct.MemRange[self.a_dtype, cute.cosize(self.a_smem_layout_staged.outer)], self.buffer_align_bytes, ] # (MMA, MMA_N, MMA_K, STAGE) sB: cute.struct.Align[ cute.struct.MemRange[self.b_dtype, cute.cosize(self.b_smem_layout_staged.outer)], self.buffer_align_bytes, ] # (MMA, MMA_M, MMA_K, STAGE) sSFA: cute.struct.Align[ cute.struct.MemRange[sf_dtype, sfa_smem_size], self.buffer_align_bytes, ] # (MMA, MMA_N, MMA_K, STAGE) sSFB: cute.struct.Align[ cute.struct.MemRange[sf_dtype, sfb_smem_size], self.buffer_align_bytes, ] self.shared_storage = SharedStorage # Launch the kernel synchronously self.kernel( self.tiled_mma, self.tiled_mma_sfb, tma_atom_a, tma_tensor_a if const_expr(not self.gather_A) else mA, tma_atom_b, tma_tensor_b, tma_atom_sfa, tma_tensor_sfa, tma_atom_sfb, tma_tensor_sfb, tma_atom_d, tma_tensor_d, tma_atom_c, tma_tensor_c, epilogue_params, varlen_params, self.cluster_layout_vmnk, self.cluster_layout_sfb_vmnk, self.a_smem_layout_staged, self.a_smem_load_layout_staged, self.b_smem_layout_staged, self.sfa_smem_layout_staged, self.sfb_smem_layout_staged, self.epi_smem_layout_staged, self.epi_c_smem_layout_staged, self.epi_tile, tile_sched_params, TileSchedulerCls, ).launch( grid=grid, block=[self.threads_per_cta, 1, 1], cluster=self.cluster_shape_mnk, stream=stream, min_blocks_per_mp=1, ) return # GPU device kernel @cute.kernel def kernel( self, tiled_mma: cute.TiledMma, tiled_mma_sfb: Optional[cute.TiledMma], tma_atom_a: Optional[cute.CopyAtom], mA_mkl: cute.Tensor, tma_atom_b: cute.CopyAtom, mB_nkl: cute.Tensor, tma_atom_sfa: Optional[cute.CopyAtom], mSFA_mkl: Optional[cute.Tensor], tma_atom_sfb: Optional[cute.CopyAtom], mSFB_nkl: Optional[cute.Tensor], tma_atom_d: Optional[cute.CopyAtom], mD_mnl: Optional[cute.Tensor], tma_atom_c: Optional[cute.CopyAtom], mC_mnl: Optional[cute.Tensor], epilogue_params: ParamsBase, varlen_params: VarlenManager.Params, cluster_layout_vmnk: cute.Layout, cluster_layout_sfb_vmnk: Optional[cute.Layout], a_smem_layout: cute.ComposedLayout, a_smem_load_layout: cute.ComposedLayout, b_smem_layout: cute.ComposedLayout, sfa_smem_layout: Optional[cute.Layout], sfb_smem_layout: Optional[cute.Layout], epi_smem_layout: Union[cute.Layout, cute.ComposedLayout, None], epi_c_smem_layout: Union[cute.Layout, cute.ComposedLayout, None], epi_tile: cute.Tile, tile_sched_params: ParamsBase, TileSchedulerCls: cutlass.Constexpr[Callable], ): """ GPU device kernel performing the Persistent batched GEMM computation. """ varlen_m = const_expr(varlen_params.cu_seqlens_m is not None) varlen_k = const_expr(varlen_params.cu_seqlens_k is not None) assert not (varlen_m and varlen_k) if const_expr(self.gather_A): assert varlen_m or varlen_k has_D = const_expr(mD_mnl is not None) has_C = const_expr(mC_mnl is not None) warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) # ///////////////////////////////////////////////////////////////////////////// # Prefetch Tma desc # ///////////////////////////////////////////////////////////////////////////// if warp_idx == self.ab_load_warp_id: for tma_atom in ( tma_atom_a, tma_atom_b, tma_atom_sfa, tma_atom_sfb, tma_atom_d, tma_atom_c, ): if const_expr(tma_atom is not None): cpasync.prefetch_descriptor(tma_atom) use_2cta_instrs = cute.size(tiled_mma.thr_id.shape) == 2 # # Setup cta/thread coordinates # # Coords inside cluster bidx, _, _ = cute.arch.block_idx() mma_tile_coord_v = bidx % cute.size(tiled_mma.thr_id.shape) is_leader_cta = mma_tile_coord_v == 0 cta_rank_in_cluster = cute.arch.make_warp_uniform(cute.arch.block_idx_in_cluster()) # Coord inside cta tidx, _, _ = cute.arch.thread_idx() # # Alloc and init: a+b full/empty, accumulator full/empty, tensor memory dealloc barrier # smem = cutlass.utils.SmemAllocator() storage = smem.allocate(self.shared_storage) tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr tmem_holding_buf = storage.tmem_holding_buf # Tensor memory dealloc barrier init if use_2cta_instrs: if warp_idx == self.ab_load_warp_id: num_tmem_dealloc_threads = 32 cute.arch.mbarrier_init(tmem_dealloc_mbar_ptr, num_tmem_dealloc_threads) # Initialize pipelines and states ab_pipeline = self.make_ab_pipeline( tiled_mma=tiled_mma, cluster_layout_vmnk=cluster_layout_vmnk, ab_pipeline_mbar_ptr=storage.ab_pipeline_array_ptr.data_ptr(), is_leader_cta=is_leader_cta, ) epi_pipeline = None if const_expr(has_C): epi_pipeline = self.make_epi_pipeline( c_smem_layout=cute.slice_(epi_c_smem_layout, (None, None, 0)), epi_pipeline_mbar_ptr=storage.epi_pipeline_array_ptr.data_ptr(), ) acc_pipeline = self.make_acc_pipeline( cluster_layout_vmnk=cluster_layout_vmnk, acc_pipeline_mbar_ptr=storage.acc_pipeline_array_ptr.data_ptr(), ) sched_pipeline = None tile_count = None if const_expr(tile_sched_params.tile_count_semaphore is not None): # Dynamic persistent scheduler sched_pipeline = self.make_sched_pipeline( self.cluster_shape_mnk, sched_pipeline_mbar_ptr=storage.sched_pipeline_array_ptr.data_ptr(), has_C=has_C, ) tile_count = storage.tile_count.get_tensor((self.sched_stage,)) a_prefetch_pipeline = None if const_expr(self.gather_A): a_prefetch_pipeline = self.make_a_prefetch_pipeline( storage.a_prefetch_pipeline_array_ptr.data_ptr(), ) # Setup smem tensor A/B/D # (MMA, MMA_M, MMA_K, STAGE) sA_mma = storage.sA.get_tensor(a_smem_layout.outer, swizzle=a_smem_layout.inner) sA = storage.sA.get_tensor(a_smem_load_layout.outer, swizzle=a_smem_load_layout.inner) # (MMA, MMA_N, MMA_K, STAGE) sB = storage.sB.get_tensor(b_smem_layout.outer, swizzle=b_smem_layout.inner) sAIdx = None if const_expr(self.gather_A): a_idx_smem_dim = self.cta_tile_shape_mnk[0] if varlen_m else self.cta_tile_shape_mnk[2] a_idx_smem_layout = cute.make_layout((a_idx_smem_dim, self.a_prefetch_stage)) sAIdx = storage.sAIdx.get_tensor(a_idx_smem_layout) sSFA, sSFB = None, None if const_expr(self.blockscaled): # (MMA, MMA_M, MMA_K, STAGE) sSFA = storage.sSFA.get_tensor(sfa_smem_layout) # (MMA, MMA_N, MMA_K, STAGE) sSFB = storage.sSFB.get_tensor(sfb_smem_layout) sD = None if const_expr(has_D): # (EPI_TILE_M, EPI_TILE_N, STAGE) sD = storage.sD.get_tensor(epi_smem_layout.outer, swizzle=epi_smem_layout.inner) sC = None if const_expr(has_C): sC = storage.sC.get_tensor(epi_c_smem_layout.outer, swizzle=epi_c_smem_layout.inner) epi_smem_tensors = self.epi_get_smem_tensors(epilogue_params, storage) thr_mma = tiled_mma.get_slice(mma_tile_coord_v) thr_mma_sfb = ( tiled_mma_sfb.get_slice(mma_tile_coord_v) if const_expr(self.blockscaled) else None ) # (MMA, MMA_M, MMA_N) acc_shape = tiled_mma.partition_shape_C(self.mma_tiler[:2]) # (MMA, MMA_M, MMA_N, STAGE) tCtAcc_fake = tiled_mma.make_fragment_C(cute.append(acc_shape, self.num_acc_stage)) varlen_manager = VarlenManager.create( varlen_params, has_D, self.num_epi_tensormaps, # Only used if not varlen_m len_m_static=Int32( mA_mkl.shape[0] if varlen_k or varlen_params.mAIdx is None else varlen_params.mAIdx.shape[0] ), len_k_static=Int32(mA_mkl.shape[1]), ) TileSchedulerCls = partial( TileSchedulerCls.create, tile_sched_params, tile_count, sched_pipeline ) tmem_alloc_barrier = pipeline.NamedBarrier( barrier_id=int(NamedBarrierGemm.TmemPtr), num_threads=cute.arch.WARP_SIZE * len((self.mma_warp_id, *self.epilog_warp_id)), ) epi_load_barrier = None if const_expr(has_C): epi_load_barrier = pipeline.NamedBarrier( barrier_id=int(NamedBarrierGemm.EpilogueLoad), num_threads=2 * cute.arch.WARP_SIZE ) # # Specialized AB load warps # if warp_idx == self.ab_load_warp_id: is_tma_warp = True # initialize tensormap for A & B varlen_manager.init_tensormap_AB(tma_atom_a, tma_atom_b, is_tma_warp) tma_desc_a_ptr = varlen_manager.get_tma_desc_a_ptr() tma_desc_b_ptr = varlen_manager.get_tma_desc_b_ptr() # Compute multicast mask for A/B buffer full block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) block_in_cluster_coord_sfb_vmnk = None if const_expr(self.blockscaled): block_in_cluster_coord_sfb_vmnk = cluster_layout_sfb_vmnk.get_flat_coord( cta_rank_in_cluster ) a_mcast_mask, b_mcast_mask = None, None sfa_mcast_mask, sfb_mcast_mask = None, None if const_expr(self.is_a_mcast or self.is_b_mcast or use_2cta_instrs): a_mcast_mask = cpasync.create_tma_multicast_mask( cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2 ) b_mcast_mask = cpasync.create_tma_multicast_mask( cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=1 ) if const_expr(self.blockscaled): sfa_mcast_mask = cpasync.create_tma_multicast_mask( cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2 ) sfb_mcast_mask = cpasync.create_tma_multicast_mask( cluster_layout_sfb_vmnk, block_in_cluster_coord_sfb_vmnk, mcast_mode=1 ) # Persistent tile scheduling loop tile_scheduler = TileSchedulerCls() work_tile = tile_scheduler.initial_work_tile_info() ab_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.ab_stage ) if const_expr(varlen_k): # wait tensormap initialization complete before update varlen_manager.fence_tensormap_init() do_epi_load_barrier_arrive = Boolean(True) while work_tile.is_valid_tile: tile_coord_mnkl = work_tile.tile_idx batch_idx = tile_coord_mnkl[3] varlen_manager.update_tensormap_AB( batch_idx, self.a_layout, self.b_layout, is_tma_warp, ) # /////////////////////////////////////////////////////////////////////////// # Local_tile partition global tensors # /////////////////////////////////////////////////////////////////////////// mma_tile_coord_mnl = ( tile_coord_mnkl[0] // cute.size(tiled_mma.thr_id.shape), tile_coord_mnkl[1], tile_coord_mnkl[3], ) gA_mk = None if const_expr(not self.gather_A): mA_mk = varlen_manager.offset_batch_A(mA_mkl, batch_idx) # (bM, bK, RestK) gA_mk = cute.local_tile( mA_mk, cute.select(self.mma_tiler, [0, 2]), (mma_tile_coord_mnl[0], None), ) # (bN, bK, RestK) gB_nk = cute.local_tile( varlen_manager.offset_batch_B(mB_nkl, batch_idx), cute.select(self.mma_tiler, [1, 2]), (mma_tile_coord_mnl[1], None), ) if const_expr(self.blockscaled): # (bM, bK) gSFA_mkl = cute.local_tile( varlen_manager.offset_batch_A(mSFA_mkl, batch_idx), cute.select(self.mma_tiler, [0, 2]), (mma_tile_coord_mnl[0], None), ) # (bN, bK) gSFB_nkl = cute.local_tile( varlen_manager.offset_batch_B(mSFB_nkl, batch_idx), cute.select(self.mma_tiler, [1, 2]), (mma_tile_coord_mnl[1], None), ) # Partition global tensor for TiledMMA_A/B/D # Then partition global/shared tensor for TMA load A/B varlen_manager.fence_tensormap_update_AB(is_tma_warp) len_k = varlen_manager.len_k(batch_idx) # TMA load A partition_S/D a_cta_layout = cute.make_layout( cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape ) copy_A = None if const_expr(not self.gather_A): # (MMA, MMA_M, MMA_K, RestK) tCgA = thr_mma.partition_A(gA_mk) copy_A, _, _ = copy_utils.tma_get_copy_fn( tma_atom_a, cta_coord=block_in_cluster_coord_vmnk[2], cta_layout=a_cta_layout, src_tensor=tCgA, dst_tensor=sA, mcast_mask=a_mcast_mask, tma_desc_ptr=tma_desc_a_ptr, ) # (MMA, MMA_N, MMA_K, RestK) tCgB = thr_mma.partition_B(gB_nk) if const_expr(self.blockscaled): # (MMA, MMA_M, MMA_K) tCgSFA = thr_mma.partition_A(gSFA_mkl) # (MMA, MMA_N, MMA_K) tCgSFB = thr_mma_sfb.partition_B(gSFB_nkl) # TMA load B partition_S/D copy_B, _, _ = copy_utils.tma_get_copy_fn( tma_atom_b, cta_coord=block_in_cluster_coord_vmnk[1], cta_layout=cute.make_layout( cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape ), src_tensor=tCgB, dst_tensor=sB, mcast_mask=b_mcast_mask, tma_desc_ptr=tma_desc_b_ptr, ) copy_SFA, copy_SFB = None, None if const_expr(self.blockscaled): # TMA load SFA partition_S/D copy_SFA, _, _ = copy_utils.tma_get_copy_fn( tma_atom_sfa, cta_coord=block_in_cluster_coord_vmnk[2], cta_layout=a_cta_layout, src_tensor=tCgSFA, dst_tensor=sSFA, filter_zeros=True, mcast_mask=sfa_mcast_mask, # tma_desc_ptr=tma_desc_sfa_ptr, ) # TMA load SFB partition_S/D sfb_cta_layout = cute.make_layout( cute.slice_(cluster_layout_sfb_vmnk, (0, None, 0, 0)).shape ) copy_SFB, _, _ = copy_utils.tma_get_copy_fn( tma_atom_sfb, cta_coord=block_in_cluster_coord_sfb_vmnk[1], cta_layout=sfb_cta_layout, src_tensor=tCgSFB, dst_tensor=sSFB, filter_zeros=True, mcast_mask=sfb_mcast_mask, # tma_desc_ptr=tma_desc_sfa_ptr, ) k_tile_cnt = cute.ceil_div(len_k, self.cta_tile_shape_mnk[2]) ab_producer_state = self.load_AB( ab_pipeline, ab_producer_state, copy_A, copy_B, k_tile_cnt, copy_SFA, copy_SFB, ) if const_expr(epi_load_barrier is not None): # In the first work tile, the epi load warp will wait for the signal # from the mainloop load warp to start loading C, to avoid interfering # with loading A and B. if do_epi_load_barrier_arrive: epi_load_barrier.arrive() do_epi_load_barrier_arrive = Boolean(False) # Advance to next tile tile_scheduler.advance_to_next_work() work_tile = tile_scheduler.get_current_work() # Wait A/B buffer empty ab_pipeline.producer_tail(ab_producer_state) if const_expr(self.gather_A): if ( warp_idx >= self.ab_load_warp_id + 1 and warp_idx < self.ab_load_warp_id + self.num_ab_load_warps ): # Persistent tile scheduling loop tile_scheduler = TileSchedulerCls() work_tile = tile_scheduler.initial_work_tile_info() ab_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.ab_stage ) a_prefetch_consumer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Consumer, self.a_prefetch_stage ) while work_tile.is_valid_tile: tile_coord_mnkl = work_tile.tile_idx batch_idx = tile_coord_mnkl[3] # /////////////////////////////////////////////////////////////////////////// # Local_tile partition global tensors # /////////////////////////////////////////////////////////////////////////// mAIdx_mk = varlen_manager.offset_batch_AIdx(batch_idx) if const_expr(varlen_m): # (M, K) mA_mk = mA_mkl else: assert varlen_k # (tile_M, K) mA_mk = cute.local_tile( mA_mkl, (self.cta_tile_shape_mnk[0],), (tile_coord_mnkl[0], None) ) # Partition global tensor for TiledMMA_A/B/D len_m = varlen_manager.len_m(batch_idx) len_k = varlen_manager.len_k(batch_idx) # TMA load A partition_S/D tiled_copy_A = self._make_gmem_tiled_copy_A( mA_mkl.element_type, self.a_layout, (self.num_ab_load_warps - 1) * 32 ) tidx = cute.arch.thread_idx()[0] - (self.ab_load_warp_id + 1) * 32 thr_copy_A = tiled_copy_A.get_slice(tidx) copy_A, prefetch_A = None, None if const_expr(varlen_m): a_prefetch_pipeline.consumer_wait(a_prefetch_consumer_state) copy_A = copy_utils.gather_m_get_copy_fn( thr_copy_A, mA_mk, sA, sAIdx[None, a_prefetch_consumer_state.index], limit_m=len_m - tile_coord_mnkl[0] * self.cta_tile_shape_mnk[0], limit_k=len_k, ) cute.arch.sync_warp() with cute.arch.elect_one(): a_prefetch_pipeline.consumer_release(a_prefetch_consumer_state) a_prefetch_consumer_state.advance() else: copy_A, prefetch_A = copy_utils.gather_k_get_copy_fn( thr_copy_A, mA_mk, sA, sAIdx, limit_m=len_m - tile_coord_mnkl[0] * self.cta_tile_shape_mnk[0], limit_k=len_k, ) prefetch_A = partial(prefetch_A, a_prefetch_pipeline) k_tile_cnt = cute.ceil_div(len_k, self.cta_tile_shape_mnk[2]) ab_producer_state, a_prefetch_consumer_state = self.load_A_gather_A( ab_pipeline, ab_producer_state, a_prefetch_consumer_state, copy_A, prefetch_A, k_tile_cnt, ) # Advance to next tile tile_scheduler.advance_to_next_work() work_tile = tile_scheduler.get_current_work() # # Specialized scheduler warp. Will also prefetch A indices if gatherA # if const_expr(tile_sched_params.tile_count_semaphore is not None or self.gather_A): if warp_idx == self.scheduler_warp_id: is_scheduler_warp = True if const_expr(cute.size(cluster_layout_vmnk) > 1): is_scheduler_warp = cute.arch.block_idx_in_cluster() == 0 tile_M = self.cta_tile_shape_mnk[0] tile_K = self.cta_tile_shape_mnk[2] thr_copy_AIdx, tAsAIdx, tAcAIdx = None, None, None if const_expr(self.gather_A): tiled_copy_AIdx = copy_utils.tiled_copy_1d(Int32, num_threads=32, is_async=True) thr_copy_AIdx = tiled_copy_AIdx.get_slice(cute.arch.lane_idx()) tAsAIdx = thr_copy_AIdx.partition_D(sAIdx) tAcAIdx = thr_copy_AIdx.partition_S( cute.make_identity_tensor(tile_M if varlen_m else tile_K) ) # Persistent tile scheduling loop tile_scheduler = TileSchedulerCls(is_scheduler_warp=is_scheduler_warp) work_tile = tile_scheduler.initial_work_tile_info() a_prefetch_producer_state = None if const_expr(self.gather_A): a_prefetch_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.a_prefetch_stage ) while work_tile.is_valid_tile: if const_expr(self.gather_A): tile_coord_mnkl = work_tile.tile_idx batch_idx = tile_coord_mnkl[3] mAIdx_mk = varlen_manager.offset_batch_AIdx(batch_idx) if const_expr(varlen_m): # (tile_M,) gAIdx = cute.local_tile(mAIdx_mk, (tile_M,), (tile_coord_mnkl[0],)) tAgAIdx = thr_copy_AIdx.partition_S(gAIdx) len_m = varlen_manager.len_m(batch_idx) m_limit = len_m - tile_coord_mnkl[0] * tile_M tApAIdx_m = cute.make_fragment((1, tAsAIdx.shape[1]), Boolean) for m in cutlass.range(tAsAIdx.shape[1], unroll_full=True): tApAIdx_m[0, m] = tAcAIdx[0, m] < m_limit a_prefetch_pipeline.producer_acquire(a_prefetch_producer_state) cute.copy( thr_copy_AIdx, tAgAIdx, tAsAIdx[None, None, a_prefetch_producer_state.index], pred=tApAIdx_m, ) a_prefetch_pipeline.producer_commit(a_prefetch_producer_state) a_prefetch_producer_state.advance() else: # (tile_K, RestK) gAIdx = cute.flat_divide(mAIdx_mk, (tile_K,)) tAgAIdx = thr_copy_AIdx.partition_S(gAIdx) len_k = varlen_manager.len_k(batch_idx) k_tile_cnt = cute.ceil_div(len_k, tile_K) for k_tile in cutlass.range(k_tile_cnt - 1, unroll=1): a_prefetch_pipeline.producer_acquire(a_prefetch_producer_state) cute.copy( thr_copy_AIdx, tAgAIdx[None, None, k_tile], tAsAIdx[None, None, a_prefetch_producer_state.index], ) a_prefetch_pipeline.producer_commit(a_prefetch_producer_state) a_prefetch_producer_state.advance() if 0 < k_tile_cnt: k_tile = k_tile_cnt - 1 k_limit = len_k - k_tile * tile_K tApAIdx_k = cute.make_fragment((1, tAsAIdx.shape[1]), Boolean) for m in cutlass.range(tAsAIdx.shape[1], unroll_full=True): tApAIdx_k[0, m] = tAcAIdx[0, m] < k_limit a_prefetch_pipeline.producer_acquire(a_prefetch_producer_state) cute.copy( tiled_copy_AIdx, tAgAIdx[None, None, k_tile], tAsAIdx[None, None, a_prefetch_producer_state.index], pred=tApAIdx_k, ) a_prefetch_pipeline.producer_commit(a_prefetch_producer_state) a_prefetch_producer_state.advance() # Advance to next tile tile_scheduler.fetch_next_work(is_scheduler_warp=is_scheduler_warp) tile_scheduler.advance_to_next_work(is_scheduler_warp=is_scheduler_warp) work_tile = tile_scheduler.get_current_work() # End of persistent scheduler loop if is_scheduler_warp: tile_scheduler.producer_tail() # # Specialized TMA epi load warp # if const_expr(mC_mnl is not None): if warp_idx == self.epi_load_warp_id: epi_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.epi_c_stage ) do_epi_load_barrier_wait = Boolean(True) # Persistent tile scheduling loop tile_scheduler = TileSchedulerCls() work_tile = tile_scheduler.initial_work_tile_info() while work_tile.is_valid_tile: # Get tile coord from tile scheduler tile_coord_mnkl = work_tile.tile_idx batch_idx = tile_coord_mnkl[3] copy_C_fn, _, bGS_gC = self.epilog_gmem_copy_and_partition( tma_atom_c, varlen_manager.offset_batch_epi(mC_mnl, batch_idx), self.cta_tile_shape_mnk[:2], epi_tile, sC, tile_coord_mnkl, ) copy_C = copy_utils.tma_producer_copy_fn(copy_C_fn, epi_pipeline) if do_epi_load_barrier_wait: epi_load_barrier.arrive_and_wait() do_epi_load_barrier_wait = Boolean(False) epi_tile_num = const_expr(cute.size(bGS_gC, mode=[1])) for epi_idx in cutlass.range(epi_tile_num, unroll=1): epi_pipeline.producer_acquire(epi_producer_state) copy_C(src_idx=epi_idx, producer_state=epi_producer_state) # Epi pipeline's producer commit is a NOP epi_pipeline.producer_commit(epi_producer_state) epi_producer_state.advance() # Advance to next tile tile_scheduler.advance_to_next_work() work_tile = tile_scheduler.get_current_work() # End of persistent scheduler loop epi_pipeline.producer_tail(epi_producer_state) # # Specialized MMA warp # if warp_idx == self.mma_warp_id: tmem_alloc_barrier.arrive_and_wait() # Retrieving tensor memory ptr and make accumulator tensor acc_tmem_ptr = cute.arch.retrieve_tmem_ptr( self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf ) # Partition shared/tensor memory tensor for TiledMMA_A/B/D # (MMA, MMA_M, MMA_K, STAGE) tCrA = tiled_mma.make_fragment_A(sA_mma) # (MMA, MMA_N, MMA_K, STAGE) tCrB = tiled_mma.make_fragment_B(sB) # (MMA, MMA_M, MMA_N, STAGE) tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout) if const_expr(self.blockscaled): # Make SFA tmem tensor sfa_tmem_ptr = cute.recast_ptr( acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base), dtype=self.sf_dtype, ) # (MMA, MMA_M, MMA_K) tCtSFA_layout = blockscaled_utils.make_tmem_layout_sfa( tiled_mma, self.mma_tiler, self.sf_vec_size, cute.slice_(sfa_smem_layout, (None, None, None, 0)), ) tCtSFA = cute.make_tensor(sfa_tmem_ptr, tCtSFA_layout) # Make SFB tmem tensor sfb_tmem_ptr = cute.recast_ptr( acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base) + tcgen05.find_tmem_tensor_col_offset(tCtSFA), dtype=self.sf_dtype, ) # (MMA, MMA_N, MMA_K) tCtSFB_layout = blockscaled_utils.make_tmem_layout_sfb( tiled_mma, self.mma_tiler, self.sf_vec_size, cute.slice_(sfb_smem_layout, (None, None, None, 0)), ) tCtSFB = cute.make_tensor(sfb_tmem_ptr, tCtSFB_layout) # Partition for S2T copy of SFA/SFB ( tiled_copy_s2t_sfa, tCsSFA_compact_s2t, tCtSFA_compact_s2t, ) = self.mainloop_s2t_copy_and_partition(sSFA, tCtSFA) ( tiled_copy_s2t_sfb, tCsSFB_compact_s2t, tCtSFB_compact_s2t, ) = self.mainloop_s2t_copy_and_partition(sSFB, tCtSFB) else: tCtSFA, tCtSFB = None, None tiled_copy_s2t_sfa, tCsSFA_compact_s2t, tCtSFA_compact_s2t = None, None, None tiled_copy_s2t_sfb, tCsSFB_compact_s2t, tCtSFB_compact_s2t = None, None, None # Persistent tile scheduling loop tile_scheduler = TileSchedulerCls() work_tile = tile_scheduler.initial_work_tile_info() ab_consumer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Consumer, self.ab_stage ) acc_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.num_acc_stage ) while work_tile.is_valid_tile: # Get tile coord from tile scheduler tile_coord_mnkl = work_tile.tile_idx batch_idx = tile_coord_mnkl[3] k_len = varlen_manager.len_k(batch_idx) k_tile_cnt = cute.ceil_div(k_len, self.mma_tiler[2]) # Set tensor memory buffer for current tile # (MMA, MMA_M, MMA_N) tCtAcc = tCtAcc_base[None, None, None, acc_producer_state.index] ab_consumer_state, acc_producer_state, tiled_mma = self.mma( ab_pipeline, acc_pipeline, ab_consumer_state, acc_producer_state, tiled_mma, tCrA, tCrB, tCtAcc, k_tile_cnt, is_leader_cta, cta_rank_in_cluster, tCtSFA, tCtSFB, tiled_copy_s2t_sfa, tiled_copy_s2t_sfb, tCsSFA_compact_s2t, tCsSFB_compact_s2t, tCtSFA_compact_s2t, tCtSFB_compact_s2t, ) # Advance to next tile tile_scheduler.advance_to_next_work() work_tile = tile_scheduler.get_current_work() # Wait for accumulator buffer empty acc_pipeline.producer_tail(acc_producer_state) # # Specialized epilogue warps # if warp_idx < self.mma_warp_id: # Alloc tensor memory buffer if warp_idx == self.epilog_warp_id[0]: cute.arch.alloc_tmem( self.num_tmem_alloc_cols, tmem_holding_buf, is_two_cta=use_2cta_instrs ) # Bar sync for retrieve tensor memory ptr from shared memory tmem_alloc_barrier.arrive_and_wait() is_tma_warp = Boolean(warp_idx == self.epilog_warp_id[0]) varlen_manager.init_tensormap_epi( tma_atom_d, self.epi_get_tma_atoms(epilogue_params), is_tma_warp ) tma_desc_d_ptr = varlen_manager.get_tma_desc_d_ptr() tma_desc_epi_ptrs = varlen_manager.get_tma_desc_epi_ptrs() # Retrieving tensor memory ptr and make accumulator tensor acc_tmem_ptr = cute.arch.retrieve_tmem_ptr( self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf ) # (MMA, MMA_M, MMA_N, STAGE) tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout) epilogue_barrier = pipeline.NamedBarrier( barrier_id=int(NamedBarrierGemm.Epilogue), num_threads=self.num_epi_warps * cute.arch.WARP_SIZE, ) # Partition for epilogue epi_tidx = tidx tiled_copy_t2r, tTR_tAcc_base, tTR_rAcc = self.epilog_tmem_copy_and_partition( epi_tidx, tCtAcc_base, epi_tile, use_2cta_instrs ) tTR_rD = cute.make_fragment(tTR_rAcc.shape, self.acc_dtype) tiled_copy_r2s, tRS_rD, tRS_sD = self.epilog_smem_store_and_partition( tiled_copy_t2r, self.d_layout, self.d_dtype, tTR_rD, sD, epi_tidx ) tRS_rC, tSR_rC, tSR_sC = None, None, None tiled_copy_s2r = None if const_expr(mC_mnl is not None): tiled_copy_s2r, tRS_rC, tSR_rC, tSR_sC = self.epilog_smem_load_and_partition( tiled_copy_t2r, self.c_layout, self.c_dtype, sC, tRS_rD.layout, epi_tidx ) # Persistent tile scheduling loop tile_scheduler = TileSchedulerCls() work_tile = tile_scheduler.initial_work_tile_info() acc_consumer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Consumer, self.num_acc_stage ) epi_store_pipeline = self.make_epi_store_pipeline() epi_read_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Consumer, self.epi_c_stage ) if const_expr(varlen_m): # wait tensormap initialization complete before update varlen_manager.fence_tensormap_init() while work_tile.is_valid_tile: # Get tile coord from tile scheduler tile_coord_mnkl = work_tile.tile_idx batch_idx = tile_coord_mnkl[3] epi_shapes, epi_orders = self.epi_get_tensormap_update_shapes_orders( epilogue_params, varlen_params.cu_seqlens_m, batch_idx ) varlen_manager.update_tensormap_epi( batch_idx, self.d_layout, epi_shapes, epi_orders, is_tma_warp, ) # Set tensor memory buffer for current tile # (T2R, T2R_M, T2R_N, EPI_M, EPI_M) tTR_tAcc = tTR_tAcc_base[None, None, None, None, None, acc_consumer_state.index] # Wait for accumulator buffer full acc_pipeline.consumer_wait(acc_consumer_state) varlen_manager.fence_tensormap_update_epi(is_tma_warp) copy_D = None if const_expr(has_D): copy_D, _, _ = self.epilog_gmem_copy_and_partition( tma_atom_d, varlen_manager.offset_batch_epi(mD_mnl, batch_idx), self.cta_tile_shape_mnk[:2], epi_tile, sD, tile_coord_mnkl, tma_desc_ptr=tma_desc_d_ptr, ) copy_C = None # We're using a separate warp to load C tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc)) k_len = varlen_manager.len_k(batch_idx) load_acc_subtile = partial( self.epi_load_acc_subtile, tiled_copy_t2r, tiled_copy_r2s, tTR_tAcc, tTR_rAcc, clear_acc=varlen_k and k_len == 0, ) epi_read_state, _ = self.epilogue( epilogue_params, epi_smem_tensors, tma_desc_epi_ptrs, epi_pipeline, epi_store_pipeline, epi_read_state, None, # epi_producer_state epi_tile, load_acc_subtile, tRS_rD, tRS_rC, tiled_copy_t2r, tiled_copy_r2s, tRS_sD, tiled_copy_s2r, tSR_rC, tSR_sC, copy_D, copy_C, tile_coord_mnkl, varlen_manager, epilogue_barrier, tile_scheduler, epi_tidx, is_tma_warp, ) # Async arrive accumulator buffer empty with cute.arch.elect_one(): acc_pipeline.consumer_release(acc_consumer_state) acc_consumer_state.advance() # Advance to next tile tile_scheduler.advance_to_next_work() work_tile = tile_scheduler.get_current_work() # Dealloc the tensor memory buffer if warp_idx == self.epilog_warp_id[0]: cute.arch.relinquish_tmem_alloc_permit(is_two_cta=use_2cta_instrs) epilogue_barrier.arrive_and_wait() if warp_idx == self.epilog_warp_id[0]: if const_expr(use_2cta_instrs): cute.arch.mbarrier_arrive(tmem_dealloc_mbar_ptr, cta_rank_in_cluster ^ 1) cute.arch.mbarrier_wait(tmem_dealloc_mbar_ptr, 0) cute.arch.dealloc_tmem( acc_tmem_ptr, self.num_tmem_alloc_cols, is_two_cta=use_2cta_instrs ) # Wait for D store complete if is_tma_warp: epi_store_pipeline.producer_tail() @cute.jit def load_A_gather_A( self, a_pipeline: cutlass.pipeline.PipelineAsync, a_producer_state: cutlass.pipeline.PipelineState, a_prefetch_consumer_state: Optional[cutlass.pipeline.PipelineState], copy_A: Callable, prefetch_A: Optional[Callable], k_tile_cnt: Int32, ) -> Tuple[cutlass.pipeline.PipelineState, Optional[cutlass.pipeline.PipelineState]]: # Peek (try_wait) AB buffer empty for k_block = prefetch_k_tile_cnt peek_a_empty_status = Boolean(True) if 0 < k_tile_cnt: peek_a_empty_status = a_pipeline.producer_try_acquire(a_producer_state) # ///////////////////////////////////////////////////////////////////////// # cp.async on A # ///////////////////////////////////////////////////////////////////////// is_tma_warp = False for k_tile in cutlass.range(k_tile_cnt - 1, unroll=1): smem_idx = a_producer_state.index prefetch_out = () if const_expr(prefetch_A is not None): # Prefetch early, even before smem is free prefetch_out = (prefetch_A(k_tile, smem_idx, a_prefetch_consumer_state),) a_prefetch_consumer_state.advance() a_pipeline.producer_acquire(a_producer_state, peek_a_empty_status, is_tma_warp) copy_A(k_tile, smem_idx, *prefetch_out) # This tells mbarrier to track the completion of cp.async a_pipeline.producer_cpasync_commit(a_producer_state) a_producer_state.advance() peek_a_empty_status = Boolean(True) if k_tile + 1 < k_tile_cnt: peek_a_empty_status = a_pipeline.producer_try_acquire(a_producer_state) # bound checking in the K dimension on the last k_tile if 0 < k_tile_cnt: k_tile = k_tile_cnt - 1 smem_idx = a_producer_state.index prefetch_out = () if const_expr(prefetch_A is not None): # Prefetch early, even before smem is free prefetch_out = (prefetch_A(k_tile, smem_idx, a_prefetch_consumer_state, pred=True),) a_prefetch_consumer_state.advance() a_pipeline.producer_acquire(a_producer_state, peek_a_empty_status, is_tma_warp) copy_A(k_tile, smem_idx, *prefetch_out, pred=True) a_pipeline.producer_cpasync_commit(a_producer_state) a_producer_state.advance() return a_producer_state, a_prefetch_consumer_state @cute.jit def mma( self, ab_pipeline: cutlass.pipeline.PipelineAsync, acc_pipeline: cutlass.pipeline.PipelineAsync, ab_consumer_state: cutlass.pipeline.PipelineState, acc_producer_state: cutlass.pipeline.PipelineState, tiled_mma: cute.TiledMma, tCrA: cute.Tensor, tCrB: cute.Tensor, acc: cute.Tensor, k_tile_cnt: Int32, is_leader_cta: Boolean, cta_rank_in_cluster: Int32, tCtSFA: Optional[cute.Tensor] = None, tCtSFB: Optional[cute.Tensor] = None, tiled_copy_s2t_sfa: Optional[cute.TiledCopy] = None, tiled_copy_s2t_sfb: Optional[cute.TiledCopy] = None, tCsSFA_compact_s2t: Optional[cute.Tensor] = None, tCsSFB_compact_s2t: Optional[cute.Tensor] = None, tCtSFA_compact_s2t: Optional[cute.Tensor] = None, tCtSFB_compact_s2t: Optional[cute.Tensor] = None, ) -> Tuple[cutlass.pipeline.PipelineState, cutlass.pipeline.PipelineState, cute.TiledMma]: blockscaled = const_expr(tiled_copy_s2t_sfa is not None) if const_expr(blockscaled): assert all(x is not None for x in (tCtSFA, tCtSFB)) assert all(x is not None for x in (tiled_copy_s2t_sfa, tiled_copy_s2t_sfb)) assert all(x is not None for x in (tCsSFA_compact_s2t, tCsSFB_compact_s2t)) assert all(x is not None for x in (tCtSFA_compact_s2t, tCtSFB_compact_s2t)) # If gather_A and use_2cta_instrs, the cp.async for the non-leader CTA will # arrive at an mbarrier on the non-leader CTA side, then the mma warp of the non-leader # CTA will wait for that then arrive at the mbarrier on the leader CTA. need_nonleader_cta = const_expr(self.gather_A and self.use_2cta_instrs) # Peek (try_wait) AB buffer full for k_tile = 0 peek_ab_full_status = Boolean(True) if 0 < k_tile_cnt and (is_leader_cta or need_nonleader_cta): peek_ab_full_status = ab_pipeline.consumer_try_wait(ab_consumer_state) # Wait for accumulator buffer empty if is_leader_cta: acc_pipeline.producer_acquire(acc_producer_state) # Reset the ACCUMULATE field for each tile tiled_mma.set(tcgen05.Field.ACCUMULATE, False) # Mma mainloop num_k_blocks = cute.size(tCrA, mode=[2]) for k_tile in cutlass.range(k_tile_cnt, unroll=1): if const_expr(need_nonleader_cta): if not is_leader_cta: ab_pipeline.consumer_wait(ab_consumer_state, peek_ab_full_status) with cute.arch.elect_one(): # The odd CTA signals the even CTA ab_pipeline.sync_object_full.arrive_mbarrier( ab_consumer_state.index, dst_rank=cta_rank_in_cluster & 0xFE ) if is_leader_cta: # Conditionally wait for AB buffer full ab_pipeline.consumer_wait(ab_consumer_state, peek_ab_full_status) # Copy SFA/SFB from smem to tmem if const_expr(blockscaled): s2t_stage_coord = (None, None, None, None, ab_consumer_state.index) tCsSFA_compact_s2t_staged = tCsSFA_compact_s2t[s2t_stage_coord] tCsSFB_compact_s2t_staged = tCsSFB_compact_s2t[s2t_stage_coord] cute.copy(tiled_copy_s2t_sfa, tCsSFA_compact_s2t_staged, tCtSFA_compact_s2t) cute.copy(tiled_copy_s2t_sfb, tCsSFB_compact_s2t_staged, tCtSFB_compact_s2t) for k_blk_idx in cutlass.range(num_k_blocks, unroll_full=True): k_blk_coord = (None, None, k_blk_idx, ab_consumer_state.index) if const_expr(blockscaled): # Set SFA/SFB tensor to tiled_mma sf_kblock_coord = (None, None, k_blk_idx) tiled_mma.set(tcgen05.Field.SFA, tCtSFA[sf_kblock_coord].iterator) tiled_mma.set(tcgen05.Field.SFB, tCtSFB[sf_kblock_coord].iterator) cute.gemm(tiled_mma, acc, tCrA[k_blk_coord], tCrB[k_blk_coord], acc) tiled_mma.set(tcgen05.Field.ACCUMULATE, True) # Async arrive AB buffer empty ab_pipeline.consumer_release(ab_consumer_state) ab_consumer_state.advance() # Peek (try_wait) AB buffer full for k_tile = k_tile + 1 peek_ab_full_status = Boolean(True) if k_tile + 1 < k_tile_cnt and (is_leader_cta or need_nonleader_cta): peek_ab_full_status = ab_pipeline.consumer_try_wait(ab_consumer_state) # Async arrive accumulator buffer full if is_leader_cta: acc_pipeline.producer_commit(acc_producer_state) acc_producer_state.advance() # If we don't return the tiled_mma, we get compiler error # "operand #0 does not dominate this use" return ab_consumer_state, acc_producer_state, tiled_mma @cute.jit def epi_load_acc_subtile( self, tiled_copy_t2r: cute.TiledCopy, tiled_copy_r2s: cute.TiledCopy, tTR_tAcc: cute.Tensor, tTR_rAcc: cute.Tensor, tRS_rD: cute.Tensor, epi_idx: int, clear_acc: Boolean = False, ): if not clear_acc: # Load accumulator from tensor memory buffer to register cute.copy(tiled_copy_t2r, tTR_tAcc[None, None, None, epi_idx], tTR_rAcc) tRS_rAcc = tiled_copy_r2s.retile(tTR_rAcc) tRS_rD.store(tRS_rAcc.load()) else: tRS_rD.fill(0.0) def mainloop_s2t_copy_and_partition( self, sSF: cute.Tensor, tSF: cute.Tensor, ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]: """ Make tiledCopy for smem to tmem load for scale factor tensor, then use it to partition smem memory (source) and tensor memory (destination). :param sSF: The scale factor tensor in smem :type sSF: cute.Tensor :param tSF: The scale factor tensor in tmem :type tSF: cute.Tensor :return: A tuple containing (tiled_copy_s2t, tCsSF_compact_s2t, tCtSF_compact_s2t) where: - tiled_copy_s2t: The tiled copy operation for smem to tmem load for scale factor tensor(s2t) - tCsSF_compact_s2t: The partitioned scale factor tensor in smem - tSF_compact_s2t: The partitioned scale factor tensor in tmem :rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor] """ # (MMA, MMA_MN, MMA_K, STAGE) tCsSF_compact = cute.filter_zeros(sSF) # (MMA, MMA_MN, MMA_K) tCtSF_compact = cute.filter_zeros(tSF) # Make S2T CopyAtom and tiledCopy copy_atom_s2t = cute.make_copy_atom(tcgen05.Cp4x32x128bOp(self.cta_group), self.sf_dtype) tiled_copy_s2t = tcgen05.make_s2t_copy(copy_atom_s2t, tCtSF_compact) thr_copy_s2t = tiled_copy_s2t.get_slice(0) # ((ATOM_V, REST_V), Rest_Tiler, MMA_MN, MMA_K, STAGE) tCsSF_compact_s2t_ = thr_copy_s2t.partition_S(tCsSF_compact) # ((ATOM_V, REST_V), Rest_Tiler, MMA_MN, MMA_K, STAGE) tCsSF_compact_s2t = tcgen05.get_s2t_smem_desc_tensor(tiled_copy_s2t, tCsSF_compact_s2t_) # ((ATOM_V, REST_V), Rest_Tiler, MMA_MN, MMA_K) tCtSF_compact_s2t = thr_copy_s2t.partition_D(tCtSF_compact) return tiled_copy_s2t, tCsSF_compact_s2t, tCtSF_compact_s2t def epilog_tmem_copy_and_partition( self, tidx: Int32, tAcc: cute.Tensor, epi_tile: cute.Tile, use_2cta_instrs: Union[Boolean, bool], ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]: """ Make tiledCopy for tensor memory load, then use it to partition tensor memory (source) and register array (destination). :param tidx: The thread index in epilogue warp groups :type tidx: Int32 :param tAcc: The accumulator tensor to be copied and partitioned :type tAcc: cute.Tensor :param epi_tile: The epilogue tiler :type epi_tile: cute.Tile :param use_2cta_instrs: Whether use_2cta_instrs is enabled :type use_2cta_instrs: bool :return: A tuple containing (tiled_copy_t2r, tTR_tAcc, tTR_rAcc) where: - tiled_copy_t2r: The tiled copy operation for tmem to register copy(t2r) - tTR_tAcc: The partitioned accumulator tensor - tTR_rAcc: The accumulated tensor in register used to hold t2r results :rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor] """ # Make tiledCopy for tensor memory load copy_atom_t2r = sm100_utils.get_tmem_load_op( self.cta_tile_shape_mnk, self.d_layout if self.d_layout is not None else LayoutEnum.ROW_MAJOR, self.d_dtype if self.d_dtype is not None else cutlass.BFloat16, self.acc_dtype, epi_tile, use_2cta_instrs, ) # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, STAGE) tAcc_epi = cute.flat_divide(tAcc[((None, None), 0, 0, None)], epi_tile) # (EPI_TILE_M, EPI_TILE_N) tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) # (T2R, T2R_M, T2R_N, EPI_M, EPI_M, STAGE) tTR_tAcc = thr_copy_t2r.partition_S(tAcc_epi) cAcc = cute.make_identity_tensor((self.cta_tile_shape_mnk[0], self.cta_tile_shape_mnk[1])) # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N) cAcc_epi = cute.flat_divide(cAcc, epi_tile) # (T2R, T2R_M, T2R_N, EPI_M, EPI_N) tTR_cAcc = thr_copy_t2r.partition_D(cAcc_epi) # (T2R, T2R_M, T2R_N) tTR_rAcc = cute.make_fragment(tTR_cAcc[None, None, None, 0, 0].shape, self.acc_dtype) return tiled_copy_t2r, tTR_tAcc, tTR_rAcc def epilog_smem_store_and_partition( self, tiled_copy_t2r: cute.TiledCopy, d_layout: Optional[LayoutEnum], dtype: Optional[Type[cutlass.Numeric]], tTR_rD: cute.Tensor, sD: cute.Tensor, tidx: Int32, ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]: """ Make tiledCopy for shared memory store, then use it to partition register array (source) and shared memory (destination). :param tiled_copy_t2r: The tiled copy operation for tmem to register copy(t2r) :type tiled_copy_t2r: cute.TiledCopy :param tTR_rD: The partitioned accumulator tensor :type tTR_rD: cute.Tensor :param tidx: The thread index in epilogue warp groups :type tidx: Int32 :param sD: The shared memory tensor to be copied and partitioned :type sD: cute.Tensor :type sepi: cute.Tensor :return: A tuple containing (tiled_copy_r2s, tRS_rD, tRS_sD) where: - tiled_copy_r2s: The tiled copy operation for register to smem copy(r2s) - tRS_rD: The partitioned tensor C (register source) - tRS_sD: The partitioned tensor C (smem destination) :rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor] """ copy_atom_r2s = sm100_utils.get_smem_store_op( d_layout if d_layout is not None else LayoutEnum.ROW_MAJOR, dtype if dtype is not None else cutlass.BFloat16, self.acc_dtype, tiled_copy_t2r, ) tiled_copy_r2s = cute.make_tiled_copy_D(copy_atom_r2s, tiled_copy_t2r) # (R2S, R2S_M, R2S_N, PIPE_D) thr_copy_r2s = tiled_copy_r2s.get_slice(tidx) tRS_sD = thr_copy_r2s.partition_D(sD) if sD is not None else None # (R2S, R2S_M, R2S_N) tRS_rD = tiled_copy_r2s.retile(tTR_rD) return tiled_copy_r2s, tRS_rD, tRS_sD def epilog_smem_load_and_partition( self, tiled_copy_t2r: cute.TiledCopy, c_layout: LayoutEnum, dtype: Type[cutlass.Numeric], # tTR_rC: cute.Tensor, sC: cute.Tensor, tRS_rD_layout: cutlass.Layout, tidx: Int32, ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]: copy_atom_r2s = sm100_utils.get_smem_store_op( c_layout, dtype, self.acc_dtype, tiled_copy_t2r ) store_op = copy_atom_r2s.op # m8n8 16-bit path if isinstance(store_op, StMatrix8x8x16bOp): op = LdMatrix8x8x16bOp(num_matrices=store_op.num_matrices, transpose=store_op.transpose) # m16n8 8-bit store -> m16n16 8-bit load elif isinstance(store_op, StMatrix16x8x8bOp) and store_op.num_matrices in [2, 4]: # transpose=True is enforced by the class op = LdMatrix16x16x8bOp(num_matrices=store_op.num_matrices // 2) else: op = cute.nvgpu.CopyUniversalOp() copy_atom_s2r = cute.make_copy_atom(op, dtype) tiled_copy_s2r = cute.make_tiled_copy_D(copy_atom_s2r, tiled_copy_t2r) thr_copy_s2r = tiled_copy_s2r.get_slice(tidx) # (R2S, R2S_M, R2S_N, PIPE_D) tSR_sC = thr_copy_s2r.partition_S(sC) tRS_rC = cute.make_fragment(tRS_rD_layout, dtype) # (R2S, R2S_M, R2S_N) tSR_rC = tiled_copy_s2r.retile(tRS_rC) return tiled_copy_s2r, tRS_rC, tSR_rC, tSR_sC @cute.jit def make_ab_pipeline( self, tiled_mma: cute.TiledMma, cluster_layout_vmnk: cute.Layout, ab_pipeline_mbar_ptr: cute.Pointer, is_leader_cta: Boolean, ) -> pipeline.PipelineAsync: # If gather_A and use_2cta_instrs, the cp.async for the non-leader CTA will # arrive at an mbarrier on the non-leader CTA side, then the mma warp of the non-leader # CTA will wait for that then arrive at the mbarrier on the leader CTA. # The producer count for the leader CTA is 1 (TMA) + num_cpasync_threads # + 1 (from non-leader CTA). # The producer count for the non-leader CTA is num_cpasync_threads # (TMA doesn't arrive there). if const_expr(not self.gather_A): producer_cnt = 1 else: producer_cnt = (self.num_ab_load_warps - 1) * 32 + ( 1 if const_expr(not self.use_2cta_instrs) else 2 ) ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread, producer_cnt) # Each warp will contribute to the arrive count with the number of mcast size mcast_size = self.num_mcast_ctas_a + self.num_mcast_ctas_b - 1 consumer_arrive_cnt = mcast_size ab_pipeline_consumer_group = pipeline.CooperativeGroup( pipeline.Agent.Thread, consumer_arrive_cnt ) if const_expr(not self.gather_A): pipeline_ab = pipeline.PipelineTmaUmma.create( barrier_storage=ab_pipeline_mbar_ptr, num_stages=self.ab_stage, producer_group=ab_pipeline_producer_group, consumer_group=ab_pipeline_consumer_group, tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cluster_layout_vmnk, ) else: pipeline_ab = PipelineTmaCpAsyncUmma.create( barrier_storage=ab_pipeline_mbar_ptr, num_stages=self.ab_stage, producer_group=ab_pipeline_producer_group, consumer_group=ab_pipeline_consumer_group, tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cluster_layout_vmnk, producer_drop_count=None if not self.use_2cta_instrs else (2 if not is_leader_cta else 0), ) return pipeline_ab def make_acc_pipeline( self, cluster_layout_vmnk: cute.Layout, acc_pipeline_mbar_ptr: cute.Pointer ) -> pipeline.PipelineAsync: acc_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread) num_acc_consumer_threads = self.num_epi_warps * (2 if self.use_2cta_instrs else 1) acc_pipeline_consumer_group = pipeline.CooperativeGroup( pipeline.Agent.Thread, num_acc_consumer_threads ) return pipeline.PipelineUmmaAsync.create( barrier_storage=acc_pipeline_mbar_ptr, num_stages=self.num_acc_stage, producer_group=acc_pipeline_producer_group, consumer_group=acc_pipeline_consumer_group, cta_layout_vmnk=cluster_layout_vmnk, ) def make_sched_pipeline( self, cluster_layout_mnk: cute.Layout, sched_pipeline_mbar_ptr: cute.Pointer, has_C: bool = False, ) -> pipeline.PipelineAsync: # Threads/warps participating in this pipeline sched_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread) cluster_size = cute.size(cluster_layout_mnk) # Each warp that are not the scheduler warp will contribute 1 to the arrive count warps_per_cta = self.num_ab_load_warps + len( (self.mma_warp_id, *self.epilog_warp_id, self.scheduler_warp_id) ) if has_C: warps_per_cta += 1 consumer_arrive_cnt = warps_per_cta * cluster_size - 1 sched_pipeline_consumer_group = pipeline.CooperativeGroup( pipeline.Agent.Thread, consumer_arrive_cnt ) return pipeline.PipelineAsync.create( barrier_storage=sched_pipeline_mbar_ptr, num_stages=self.sched_stage, producer_group=sched_pipeline_producer_group, consumer_group=sched_pipeline_consumer_group, # If there's cluster, the consumers must arrive at the mbar of CTA 0 in the cluster. consumer_mask=None if const_expr(cluster_size == 1) else 0, ) @cute.jit def make_a_prefetch_pipeline( self, a_prefetch_pipeline_mbar_ptr: cute.Pointer ) -> pipeline.PipelineAsync: producer_cnt = 32 a_prefetch_producer_group = pipeline.CooperativeGroup( pipeline.Agent.Thread, producer_cnt, alignment=producer_cnt ) consumer_arrive_cnt = self.num_ab_load_warps - 1 a_prefetch_consumer_group = pipeline.CooperativeGroup( pipeline.Agent.Thread, consumer_arrive_cnt ) return pipeline.PipelineCpAsync.create( barrier_storage=a_prefetch_pipeline_mbar_ptr, num_stages=self.a_prefetch_stage, producer_group=a_prefetch_producer_group, consumer_group=a_prefetch_consumer_group, ) @classmethod def _compute_stages( cls, tiled_mma: cute.TiledMma, mma_tiler_mnk: Tuple[int, int, int], cta_tile_shape_mnk: Tuple[int, int, int], epi_tile: cute.Tile, a_dtype: Type[cutlass.Numeric], b_dtype: Type[cutlass.Numeric], sf_dtype: Optional[Type[cutlass.Numeric]], sf_vec_size: Optional[int], d_dtype: Optional[Type[cutlass.Numeric]], c_dtype: Optional[Type[cutlass.Numeric]], d_layout: Optional[LayoutEnum], c_layout: Optional[LayoutEnum], epilogue_args: EpilogueArguments, prefetch_A_idx: Literal[None, "varlen_m", "varlen_k"], smem_capacity: int, occupancy: int, ) -> Tuple[int, int, int]: """Computes the number of stages for A/B/C operands based on heuristics. :param tiled_mma: The tiled MMA object defining the core computation. :type tiled_mma: cute.TiledMma :param mma_tiler_mnk: The shape (M, N, K) of the MMA tiler. :type mma_tiler_mnk: tuple[int, int, int] :param a_dtype: Data type of operand A. :type a_dtype: type[cutlass.Numeric] :param b_dtype: Data type of operand B. :type b_dtype: type[cutlass.Numeric] :param epi_tile: The epilogue tile shape. :type epi_tile: cute.Tile :param d_dtype: Data type of operand C (output). :type d_dtype: type[cutlass.Numeric] :param d_layout: Layout enum of operand D. :type d_layout: LayoutEnum :param smem_capacity: Total available shared memory capacity in bytes. :type smem_capacity: int :param occupancy: Target number of CTAs per SM (occupancy). :type occupancy: int :return: A tuple containing the computed number of stages for: (ACC stages, A/B operand stages, C stages) :rtype: tuple[int, int, int] """ blockscaled = sf_dtype is not None # Default ACC stages if const_expr(not blockscaled): num_acc_stage = 2 else: num_acc_stage = 1 if mma_tiler_mnk[1] == 256 else 2 # Default D stages epi_stage = 4 if cute.size(epi_tile[1]) <= 16 else 2 epi_c_stage = 0 if c_dtype is None else (4 if cute.size(epi_tile[1]) <= 16 else 2) # Calculate smem layout and size for one stage of A, B, and C a_smem_layout_staged_one = sm100_utils.make_smem_layout_a( tiled_mma, mma_tiler_mnk, a_dtype, 1, # a tmp 1 stage is provided ) b_smem_layout_staged_one = sm100_utils.make_smem_layout_b( tiled_mma, mma_tiler_mnk, b_dtype, 1, # a tmp 1 stage is provided ) d_smem_layout_staged_one = ( sm100_utils.make_smem_layout_epi(d_dtype, d_layout, epi_tile, 1) if d_dtype is not None else None ) c_smem_layout_staged_one = ( sm100_utils.make_smem_layout_epi(c_dtype, c_layout, epi_tile, 1) if c_dtype is not None else None ) if const_expr(blockscaled): sfa_smem_layout_staged_one = blockscaled_utils.make_smem_layout_sfa( tiled_mma, mma_tiler_mnk, sf_vec_size, 1, # a tmp 1 stage is provided ) sfb_smem_layout_staged_one = blockscaled_utils.make_smem_layout_sfb( tiled_mma, mma_tiler_mnk, sf_vec_size, 1, # a tmp 1 stage is provided ) ab_bytes_per_stage = cute.size_in_bytes( a_dtype, a_smem_layout_staged_one ) + cute.size_in_bytes(b_dtype, b_smem_layout_staged_one) if const_expr(prefetch_A_idx == "varlen_k"): # Need smem to prefetch A indices ab_bytes_per_stage += Int32.width // 8 * cta_tile_shape_mnk[2] if const_expr(blockscaled): ab_bytes_per_stage += cute.size_in_bytes( sf_dtype, sfa_smem_layout_staged_one ) + cute.size_in_bytes(sf_dtype, sfb_smem_layout_staged_one) mbar_helpers_bytes = 1024 if const_expr(prefetch_A_idx == "varlen_m"): mbar_helpers_bytes += Int32.width // 8 * cta_tile_shape_mnk[0] * 2 d_bytes_per_stage = ( cute.size_in_bytes(d_dtype, d_smem_layout_staged_one) if d_dtype is not None else 0 ) epi_bytes_per_stage = d_bytes_per_stage + cls.epi_smem_bytes_per_stage( epilogue_args, cta_tile_shape_mnk, epi_tile ) epi_bytes = epi_bytes_per_stage * epi_stage if const_expr(c_dtype is not None): c_bytes_per_stage = cute.size_in_bytes(c_dtype, c_smem_layout_staged_one) epi_bytes += c_bytes_per_stage * epi_c_stage # Calculate A/B/SFA/SFB stages: # Start with total smem per CTA (capacity / occupancy) # Subtract reserved bytes and initial C stages bytes # Divide remaining by bytes needed per A/B/SFA/SFB stage remaining_bytes = smem_capacity // occupancy - mbar_helpers_bytes - epi_bytes ab_stage = remaining_bytes // ab_bytes_per_stage # Refine epilogue stages: # Calculate remaining smem after allocating for A/B stages and reserved bytes # Add remaining unused smem to epilogue epi_stage += (remaining_bytes - ab_bytes_per_stage * ab_stage) // (epi_bytes_per_stage) return num_acc_stage, ab_stage, epi_stage, epi_c_stage @staticmethod def _compute_num_tmem_alloc_cols( tiled_mma: cute.TiledMma, mma_tiler: Tuple[int, int, int], num_acc_stage: int, ) -> int: """ Compute the number of tensor memory allocation columns. :param tiled_mma: The tiled MMA object defining the core computation. :type tiled_mma: cute.TiledMma :param mma_tiler: The shape (M, N, K) of the MMA tile. :type mma_tiler: tuple[int, int, int] :param num_acc_stage: The stage of the accumulator tensor. :type num_acc_stage: int :return: The number of tensor memory allocation columns. :rtype: int """ acc_shape = tiled_mma.partition_shape_C(mma_tiler[:2]) tCtAcc_fake = tiled_mma.make_fragment_C(cute.append(acc_shape, num_acc_stage)) num_tmem_alloc_cols = cutlass.utils.get_num_tmem_alloc_cols(tCtAcc_fake) return num_tmem_alloc_cols @staticmethod def is_valid_dtypes( a_dtype: Type[cutlass.Numeric], b_dtype: Type[cutlass.Numeric], acc_dtype: Type[cutlass.Numeric], d_dtype: Optional[Type[cutlass.Numeric]], a_major: str, b_major: str, ) -> bool: """ Check if the dtypes are valid :param ab_dtype: The data type of the A and B operands :type ab_dtype: Type[cutlass.Numeric] :param acc_dtype: The data type of the accumulator :type acc_dtype: Type[cutlass.Numeric] :param d_dtype: The data type of the output tensor :type d_dtype: Type[cutlass.Numeric] :return: True if the dtypes are valid, False otherwise :rtype: bool """ is_valid = True if b_dtype != a_dtype: is_valid = False ab_dtype = a_dtype if ab_dtype not in { cutlass.Float16, cutlass.BFloat16, cutlass.TFloat32, cutlass.Uint8, cutlass.Int8, cutlass.Float8E4M3FN, cutlass.Float8E5M2, }: is_valid = False if ( acc_dtype not in {Float32, cutlass.Float16, Int32} or acc_dtype == cutlass.Float16 and ab_dtype not in {cutlass.Float16, cutlass.Float8E4M3FN, cutlass.Float8E5M2} or acc_dtype == Int32 and ab_dtype not in {cutlass.Uint8, cutlass.Int8} ): is_valid = False if d_dtype is not None and ( acc_dtype == Float32 and d_dtype not in { Float32, cutlass.Float16, cutlass.BFloat16, cutlass.Float8E4M3FN, cutlass.Float8E5M2, Int32, cutlass.Int8, cutlass.Uint8, } or acc_dtype == cutlass.Float16 and d_dtype not in { cutlass.BFloat16, cutlass.Float16, } or acc_dtype == Int32 and d_dtype not in { cutlass.BFloat16, cutlass.Float16, Float32, Int32, cutlass.Int8, cutlass.Uint8, } ): is_valid = False if ab_dtype is cutlass.Float4E2M1FN and not (a_major == "k" and b_major == "k"): is_valid = False return is_valid @staticmethod def is_valid_dtypes_and_scale_factor_vec_size( ab_dtype: Type[cutlass.Numeric], sf_dtype: Type[cutlass.Numeric], sf_vec_size: int, d_dtype: Type[cutlass.Numeric], ) -> bool: """ Check if the dtypes and sf_vec_size are valid combinations :param ab_dtype: The data type of the A and B operands :type ab_dtype: Type[cutlass.Numeric] :param sf_dtype: The data type of the scale factor :type sf_dtype: Type[cutlass.Numeric] :param sf_vec_size: The vector size of the scale factor :type sf_vec_size: int :param d_dtype: The data type of the output tensor :type d_dtype: Type[cutlass.Numeric] :return: True if the dtypes and sf_vec_size are valid, False otherwise :rtype: bool """ is_valid = True # Check valid ab_dtype if ab_dtype not in {cutlass.Float4E2M1FN, cutlass.Float8E5M2, cutlass.Float8E4M3FN}: is_valid = False # Check valid sf_vec_size if sf_vec_size not in {16, 32}: is_valid = False # Check valid sf_dtype if sf_dtype not in {cutlass.Float8E8M0FNU, cutlass.Float8E4M3FN}: is_valid = False # Check valid sf_dtype and sf_vec_size combinations if sf_dtype == cutlass.Float8E4M3FN and sf_vec_size == 32: is_valid = False if ab_dtype in {cutlass.Float8E5M2, cutlass.Float8E4M3FN} and sf_vec_size == 16: is_valid = False # Check valid d_dtype if d_dtype not in { Float32, cutlass.Float16, cutlass.BFloat16, cutlass.Float8E5M2, cutlass.Float8E4M3FN, }: is_valid = False return is_valid @staticmethod def is_valid_mma_tiler_and_cluster_shape( mma_tiler_mn: Tuple[int, int], cluster_shape_mn: Tuple[int, int], blockscaled: bool, ) -> bool: """ Check if the mma tiler and cluster shape are valid :param mma_tiler_mn: The (M, N) shape of the MMA instruction tiler :type mma_tiler_mn: Tuple[int, int] :param cluster_shape_mn: The (ClusterM, ClusterN) shape of the CTA cluster :type cluster_shape_mn: Tuple[int, int] :return: True if the mma tiler and cluster shape are valid, False otherwise :rtype: bool """ is_valid = True # Skip invalid mma tile shape if mma_tiler_mn[0] not in [64, 128, 256]: is_valid = False if not blockscaled: if mma_tiler_mn[1] not in range(32, 257, 32): is_valid = False else: if mma_tiler_mn[1] not in [128, 256]: is_valid = False # Skip invalid cluster shape is_power_of_2 = lambda x: x > 0 and (x & (x - 1)) == 0 if ( cluster_shape_mn[0] * cluster_shape_mn[1] > 16 or cluster_shape_mn[0] <= 0 or cluster_shape_mn[1] <= 0 or not is_power_of_2(cluster_shape_mn[0]) or not is_power_of_2(cluster_shape_mn[1]) ): is_valid = False if blockscaled: # Special cluster shape check for scale factor multicasts. # Due to limited size of scale factors, we can't multicast among more than 4 CTAs. if cluster_shape_mn[0] > 4 or cluster_shape_mn[1] > 4: is_valid = False return is_valid @staticmethod def is_valid_tensor_alignment( m: int, n: int, k: int, l: int, ab_dtype: Type[cutlass.Numeric], d_dtype: Type[cutlass.Numeric], a_major: str, b_major: str, d_major: str, ) -> bool: """ Check if the tensor alignment is valid :param m: The number of rows in the A tensor :type m: int :param n: The number of columns in the B tensor :type n: int :param k: The number of columns in the A tensor :type k: int :param l: The number of columns in the C tensor :type l: int :param ab_dtype: The data type of the A and B operands :type ab_dtype: Type[cutlass.Numeric] :param d_dtype: The data type of the output tensor :type d_dtype: Type[cutlass.Numeric] :param a_major: The major axis of the A tensor :type a_major: str :param b_major: The major axis of the B tensor :type b_major: str :param d_major: The major axis of the C tensor :type d_major: str :return: True if the problem shape is valid, False otherwise :rtype: bool """ is_valid = True def check_contigous_16B_alignment(dtype, is_mode0_major, tensor_shape): major_mode_idx = 0 if is_mode0_major else 1 num_major_elements = tensor_shape[major_mode_idx] num_contiguous_elements = 16 * 8 // dtype.width return num_major_elements % num_contiguous_elements == 0 if ( not check_contigous_16B_alignment(ab_dtype, a_major == "m", (m, k, l)) or not check_contigous_16B_alignment(ab_dtype, b_major == "n", (n, k, l)) or not check_contigous_16B_alignment(d_dtype, d_major == "m", (m, n, l)) ): is_valid = False return is_valid @staticmethod def can_implement( ab_dtype: Type[cutlass.Numeric], acc_dtype: Type[cutlass.Numeric], d_dtype: Type[cutlass.Numeric], mma_tiler_mn: Tuple[int, int], cluster_shape_mn: Tuple[int, int], m: int, n: int, k: int, l: int, a_major: str, b_major: str, d_major: str, ) -> bool: """ Check if the gemm can be implemented :param ab_dtype: The data type of the A and B operands :type ab_dtype: Type[cutlass.Numeric] :param acc_dtype: The data type of the accumulator :type acc_dtype: Type[cutlass.Numeric] :param d_dtype: The data type of the output tensor :type d_dtype: Type[cutlass.Numeric] :param mma_tiler_mn: The (M, N) shape of the MMA instruction tiler :type mma_tiler_mn: Tuple[int, int] :param cluster_shape_mn: The (ClusterM, ClusterN) shape of the CTA cluster :type cluster_shape_mn: Tuple[int, int] :param m: The number of rows in the A tensor :type m: int :param n: The number of columns in the B tensor :type n: int :param k: The number of columns in the A tensor :type k: int :param l: The number of columns in the C tensor :type l: int :param a_major: The major axis of the A tensor :type a_major: str :param b_major: The major axis of the B tensor :type b_major: str :param d_major: The major axis of the C tensor :type d_major: str :return: True if the gemm can be implemented, False otherwise :rtype: bool """ can_implement = True # Skip unsupported types if not GemmSm100.is_valid_dtypes(ab_dtype, ab_dtype, acc_dtype, d_dtype, a_major, b_major): can_implement = False # Skip invalid mma tile shape and cluster shape if not GemmSm100.is_valid_mma_tiler_and_cluster_shape( mma_tiler_mn, cluster_shape_mn, blockscaled=False ): can_implement = False # Skip illegal problem shape for load/store alignment if not GemmSm100.is_valid_tensor_alignment( m, n, k, l, ab_dtype, d_dtype, a_major, b_major, d_major ): can_implement = False return can_implement def run( mnkl: Tuple[int, int, int, int], ab_dtype: Type[cutlass.Numeric], d_dtype: Type[cutlass.Numeric], c_dtype: Optional[Type[cutlass.Numeric]], acc_dtype: Type[cutlass.Numeric], a_major: str, b_major: str, d_major: str, c_major: str, mma_tiler_mn: Tuple[int, int] = (256, 256), cluster_shape_mn: Tuple[int, int] = (2, 1), tolerance: float = 1e-01, warmup_iterations: int = 0, iterations: int = 1, skip_ref_check: bool = False, dynamic_persistent: bool = False, **kwargs, ): """Execute a persistent batched dense GEMM operation on Blackwell architecture with performance benchmarking. This function prepares input tensors, configures and launches the persistent GEMM kernel, optionally performs reference validation, and benchmarks the execution performance. :param mnkl: Problem size (M, N, K, L) :type mnkl: Tuple[int, int, int, int] :param ab_dtype: Data type for input tensors A and B :type ab_dtype: Type[cutlass.Numeric] :param d_dtype: Data type for output tensor C :type d_dtype: Type[cutlass.Numeric] :param acc_dtype: Data type for accumulation during matrix multiplication :type acc_dtype: Type[cutlass.Numeric] :param a_major/b_major/d_major: Memory layout of tensor A/B/C :type a_major/b_major/d_major: str :param mma_tiler_mn: MMA tiling size. If not specified in the decorator parameters, the autotuner will use the default value of (256, 256). Otherwise, the autotuner will use the value specified in the decorator parameters. :type mma_tiler_mn: Tuple[int, int], optional :param cluster_shape_mn: Cluster shape. If not specified in the decorator parameters, the autotuner will use the default value of (2, 1). Otherwise, the autotuner will use the value specified in the decorator parameters. :type cluster_shape_mn: Tuple[int, int], optional :param tolerance: Tolerance value for reference validation comparison, defaults to 1e-01 :type tolerance: float, optional :param warmup_iterations: Number of warmup iterations before benchmarking, defaults to 0 :type warmup_iterations: int, optional :param iterations: Number of benchmark iterations to run, defaults to 1 :type iterations: int, optional :param skip_ref_check: Whether to skip reference result validation, defaults to False :type skip_ref_check: bool, optional :raises RuntimeError: If CUDA GPU is not available :raises ValueError: If the configuration is invalid or unsupported by the kernel :return: Execution time of the GEMM kernel :rtype: float """ print("Running Blackwell Persistent Dense GEMM test with:") print(f"mnkl: {mnkl}") print(f"AB dtype: {ab_dtype}, C dtype: {d_dtype}, Acc dtype: {acc_dtype}") print(f"Matrix majors - A: {a_major}, B: {b_major}, C: {d_major}") print(f"Mma Tiler (M, N): {mma_tiler_mn}, Cluster Shape (M, N): {cluster_shape_mn}") print(f"Tolerance: {tolerance}") print(f"Warmup iterations: {warmup_iterations}") print(f"Iterations: {iterations}") print(f"Skip reference checking: {skip_ref_check}") assert not dynamic_persistent, "Dynamic persistent mode is not supported yet." # Unpack parameters m, n, k, l = mnkl # Skip unsupported testcase if not GemmSm100.can_implement( ab_dtype, acc_dtype, d_dtype, mma_tiler_mn, cluster_shape_mn, m, n, k, l, a_major, b_major, d_major, ): raise TypeError( f"Unsupported testcase {ab_dtype}, {acc_dtype}, {d_dtype}, {mma_tiler_mn}, {cluster_shape_mn}, {m}, {n}, {k}, {l}, {a_major}, {b_major}, {d_major}" ) if not torch.cuda.is_available(): raise RuntimeError("GPU is required to run this example!") torch.manual_seed(1111) # Create and permute tensor A/B/C def create_and_permute_tensor(l, mode0, mode1, is_mode0_major, dtype, is_dynamic_layout=True): # is_mode0_major: (l, mode1, mode0) -> (mode0, mode1, l) # else: (l, mode0, mode1) -> (mode0, mode1, l) shape = (l, mode1, mode0) if is_mode0_major else (l, mode0, mode1) permute_order = (2, 1, 0) if is_mode0_major else (1, 2, 0) is_unsigned = dtype in {cutlass.Uint8} # Temporarily use uint8 as torch does not support fp8 type torch_dtype = cutlass_torch.dtype(dtype) gen_dtype = ( torch_dtype if dtype not in {cutlass.Float8E5M2, cutlass.Float8E4M3FN} else torch.bfloat16 ) # Create dtype torch tensor (cpu) torch_tensor_cpu = cutlass_torch.create_and_permute_torch_tensor( shape, gen_dtype, permute_order=permute_order, # init_type=cutlass.torch.TensorInitType.RANDOM, # init_config=cutlass.torch.RandomInitConfig( # min_val=0 if is_unsigned else -2, max_val=4 if is_unsigned else 2 # ), init_type=cutlass.torch.TensorInitType.GAUSSIAN, init_config=cutlass.torch.GaussianInitConfig(std=k ** (-0.5), scale=1), ).to(torch_dtype) # Create dtype torch tensor (gpu) torch_tensor = torch_tensor_cpu.cuda() # Create f32 torch tensor (cpu) f32_torch_tensor = torch_tensor_cpu.to(dtype=torch.float32) # Create dtype cute tensor (gpu) torch_tensor_view = ( torch_tensor if dtype not in {cutlass.Float8E5M2, cutlass.Float8E4M3FN} else torch_tensor.view(torch.uint8) ) cute_tensor = from_dlpack(torch_tensor_view, assumed_align=16) cute_tensor.element_type = dtype if is_dynamic_layout: cute_tensor = cute_tensor.mark_layout_dynamic(leading_dim=(0 if is_mode0_major else 1)) cute_tensor = cutlass_torch.convert_cute_tensor( f32_torch_tensor, cute_tensor, dtype, is_dynamic_layout=is_dynamic_layout, ) return f32_torch_tensor, cute_tensor, torch_tensor, torch_tensor_cpu a_ref, mA, a_torch, a_torch_cpu = create_and_permute_tensor( l, m, k, a_major == "m", ab_dtype, is_dynamic_layout=True ) b_ref, mB, b_torch, b_torch_cpu = create_and_permute_tensor( l, n, k, b_major == "n", ab_dtype, is_dynamic_layout=True ) _, mD, d_torch, d_torch_cpu = create_and_permute_tensor( l, m, n, d_major == "m", d_dtype, is_dynamic_layout=True ) if c_dtype is not None: c, mC, c_torch, d_torch_cpu = create_and_permute_tensor(l, m, n, c_major == "m", c_dtype) else: c, mC, c_torch = None, None, None # Configure gemm kernel cluster_shape_mnk = (*cluster_shape_mn, 1) gemm = GemmSm100(acc_dtype, ab_dtype, mma_tiler_mn, cluster_shape_mnk) # Compute max active clusters on current device hardware_info = cutlass.utils.HardwareInfo() max_active_clusters = hardware_info.get_max_active_clusters( cluster_shape_mn[0] * cluster_shape_mn[1] ) if dynamic_persistent: tile_count_semaphore = torch.zeros(1, dtype=torch.int32, device="cuda") else: tile_count_semaphore = None scheduler_args = TileSchedulerOptions( Int32(max_active_clusters), tile_count_semaphore=make_ptr( Int32, tile_count_semaphore.data_ptr(), cute.AddressSpace.gmem, assumed_align=4 ) if tile_count_semaphore is not None else None, ) epi_args = gemm.EpilogueArguments() varlen_args = VarlenArguments() # Get current CUDA stream from PyTorch torch_stream = torch.cuda.current_stream() # Get the raw stream pointer as a CUstream current_stream = cuda.CUstream(torch_stream.cuda_stream) # Compile gemm kernel compiled_gemm = cute.compile( gemm, mA, mB, mD, mC, epi_args, scheduler_args, varlen_args, current_stream, ) if not skip_ref_check: compiled_gemm(mA, mB, mD, mC, epi_args, scheduler_args, varlen_args, current_stream) if ab_dtype in { cutlass.Int8, cutlass.Uint8, cutlass.Float8E4M3FN, cutlass.Float8E5M2, }: ref = torch.einsum("mkl,nkl->mnl", a_ref.cpu(), b_ref.cpu()) else: ref = torch.einsum("mkl,nkl->mnl", a_ref, b_ref) if c is not None: ref = ref + c ref = ref.cpu() # Copy gpu result back gpu_d = d_torch.cpu() # Convert ref to c_type if d_dtype == Float32: ref_d = ref elif d_dtype in {cutlass.Float8E5M2, cutlass.Float8E4M3FN}: # m major: (l, n, m) -> (m, n, l) # n major: (l, m, n) -> (m, n, l) permute_order = (1, 2, 0) if d_major == "n" else (2, 1, 0) shape = (l, m, n) if d_major == "n" else (l, n, m) f8_torch_tensor = cutlass_torch.create_and_permute_torch_tensor( shape, torch.uint8, permute_order=permute_order, init_type=cutlass_torch.TensorInitType.SKIP, ).cuda() # Create dtype cute tensor (gpu) ref_d_tensor = from_dlpack(f8_torch_tensor, assumed_align=16).mark_layout_dynamic( leading_dim=(1 if d_major == "n" else 0) ) ref_d_tensor.element_type = d_dtype ref_d_tensor = cutlass_torch.convert_cute_tensor( ref, ref_d_tensor, d_dtype, is_dynamic_layout=True, ) ref_d = f8_torch_tensor.cpu() else: ref_d = ref.to(cutlass_torch.dtype(d_dtype)) # Reference checking ref_d and gpu_d torch.testing.assert_close(gpu_d, ref_d, atol=tolerance, rtol=1e-05) from triton.testing import do_bench current_stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) flops = 2 * m * n * k * l repeats = iterations warmup = warmup_iterations import time time.sleep(0.5) if ab_dtype.width == 8: assert l == 1 scale_ab = torch.ones((1,), dtype=torch.float32, device="cuda") fn_cublas = lambda: torch._scaled_mm( a_torch[:, :, 0], b_torch[:, :, 0].mT, scale_a=scale_ab, scale_b=scale_ab, out_dtype=torch.bfloat16, # use_fast_accum=fp8_fast_accum, ) else: if c_torch is None: fn_cublas = lambda: torch.matmul(a_torch.permute(2, 0, 1), b_torch.permute(2, 0, 1).mT) else: c_torch_convert = c_torch.to(a_torch.dtype) # In case C is in FP32 fn_cublas = lambda: torch.baddbmm( c_torch_convert.permute(2, 0, 1), a_torch.permute(2, 0, 1), b_torch.permute(2, 0, 1).mT, ) timing_cublas = do_bench(fn_cublas, warmup=warmup, rep=repeats) tflops_cublas = flops / (timing_cublas * 1e9) # Convert to TFlops print(f"CuBLAS Average time: {timing_cublas:.3f} ms, TFLOPS: {tflops_cublas:.1f}") time.sleep(0.5) fn = lambda: compiled_gemm( mA, mB, mD, mC, epi_args, scheduler_args, varlen_args, current_stream ) timing = do_bench(fn, warmup=warmup, rep=repeats) tflops = flops / (timing * 1e9) # Convert to TFlops print(f"Cute-DSL Average time: {timing:.3f} ms, TFLOPS: {tflops:.1f}") # time.sleep(0.5) # timing_cublas = do_bench(fn_cublas, warmup=warmup, rep=repeats) # tflops_cublas = flops / (timing_cublas * 1e9) # Convert to TFlops # print(f"CuBLAS Average time: {timing_cublas:.3f} ms, TFLOPS: {tflops_cublas:.1f}") if __name__ == "__main__": def parse_comma_separated_ints(s: str) -> Tuple[int, ...]: try: return tuple(int(x.strip()) for x in s.split(",")) except ValueError: raise argparse.ArgumentTypeError("Invalid format. Expected comma-separated integers.") parser = argparse.ArgumentParser(description="Example of Dense Persistent GEMM on Blackwell.") parser.add_argument( "--mnkl", type=parse_comma_separated_ints, default=(256, 256, 512, 1), help="mnkl dimensions (comma-separated)", ) parser.add_argument( "--mma_tiler_mn", type=parse_comma_separated_ints, default=(128, 128), help="Mma tile shape (comma-separated)", ) parser.add_argument( "--cluster_shape_mn", type=parse_comma_separated_ints, default=(1, 1), help="Cluster shape (comma-separated)", ) parser.add_argument("--ab_dtype", type=cutlass.dtype, default=cutlass.BFloat16) parser.add_argument("--d_dtype", type=cutlass.dtype, default=cutlass.BFloat16) parser.add_argument("--c_dtype", type=cutlass.dtype, default=None) parser.add_argument("--acc_dtype", type=cutlass.dtype, default=Float32) parser.add_argument("--a_major", choices=["k", "m"], type=str, default="k") parser.add_argument("--b_major", choices=["k", "n"], type=str, default="k") parser.add_argument("--d_major", choices=["n", "m"], type=str, default="n") parser.add_argument("--c_major", choices=["n", "m"], type=str, default="n") parser.add_argument("--tolerance", type=float, default=3e-02, help="Tolerance for validation") parser.add_argument("--warmup_iterations", type=int, default=5, help="Warmup iterations") parser.add_argument( "--iterations", type=int, default=30, help="Number of iterations to run the kernel", ) parser.add_argument("--skip_ref_check", action="store_true", help="Skip reference checking") parser.add_argument( "--dynamic_persistent", action="store_true", help="Dynamic persistent kernel" ) args = parser.parse_args() if len(args.mnkl) != 4: parser.error("--mnkl must contain exactly 4 values") if len(args.mma_tiler_mn) != 2: parser.error("--mma_tiler_mn must contain exactly 2 values") if len(args.cluster_shape_mn) != 2: parser.error("--cluster_shape_mn must contain exactly 2 values") run( args.mnkl, args.ab_dtype, args.d_dtype, args.c_dtype, args.acc_dtype, args.a_major, args.b_major, args.d_major, args.c_major, args.mma_tiler_mn, args.cluster_shape_mn, args.tolerance, args.warmup_iterations, args.iterations, args.skip_ref_check, args.dynamic_persistent, ) print("PASS")