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cutlass/examples/python/CuTeDSL/blackwell/dense_gemm_alpha_beta_persistent.py
Junkai-Wu b1d6e2c9b3 v4.3 update. (#2709) 
* v4.3 update.

* Update the cute_dsl_api changelog's doc link

* Update version to 4.3.0

* Update the example link

* Update doc to encourage user to install DSL from requirements.txt

---------

Co-authored-by: Larry Wu <larwu@nvidia.com>
2025-10-21 14:26:30 -04:00

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# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
from typing import Optional, Tuple, Type, Union
import torch
import cuda.bindings.driver as cuda
import cutlass
import cutlass.cute as cute
import cutlass.cute.testing as testing
import cutlass.torch as cutlass_torch
import cutlass.utils as utils
import cutlass.pipeline as pipeline
import cutlass.utils.blackwell_helpers as sm100_utils
from cutlass.cute.nvgpu import cpasync, tcgen05
"""
A high-performance persistent batched dense GEMM (D = alpha * A * B + beta * C) 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")
- Matrix D is MxNxL, L is batch dimension, D can be row-major("N") or column-major("M")
- alpha and beta are float scalars
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.
- Load C matrix from global memory (GMEM) to shared memory (SMEM) using TMA operations and then copied to registers (RMEM).
- Compute D = alpha * accumulator + beta * C.
- Type convert D matrix to output type.
- Store D matrix from registers (RMEM) to shared memory (SMEM) to global memory (GMEM) with TMA operations,
- Optionally accept an elementwise lambda function epilogue_op to apply to the output tensor:
e.g., relu can set epilogue_op = lambda x: cute.where(x > 0, x, cute.full_like(x, 0))
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_alpha_beta_persistent.py \
--ab_dtype Float16 --c_dtype Float16 --d_dtype Float16 --acc_dtype Float32 --epi_dtype Float32 \
--mma_tiler_mn 256,128 --cluster_shape_mn 2,1 \
--mnkl 8192,8192,8192,1 \
--use_2cta_instrs --alpha 2.0 --beta 1.0
To collect performance with NCU profiler:
.. code-block:: bash
ncu python examples/blackwell/dense_gemm_alpha_beta_persistent.py \
--ab_dtype Float16 --c_dtype Float16 --d_dtype Float16 --acc_dtype Float32 --epi_dtype Float32 \
--mma_tiler_mn 256,128 --cluster_shape_mn 2,1 \
--mnkl 8192,8192,8192,1 \
--use_2cta_instrs --alpha 2.0 --beta 1.0 \
--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 SM100PersistentDenseGemmAlphaBetaKernel class documentation
* A/B tensor must have the same data type
* C/D tensor must have the same major order
* 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/D 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 SM100PersistentDenseGemmAlphaBetaKernel:
"""This class implements batched matrix multiplication (D = alpha * A * B + beta * C) 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 epi_dtype: Data type for epilogue operation
:type epi_dtype: type[cutlass.Numeric]
:param use_2cta_instrs: Whether to use CTA group 2 for advanced thread cooperation
:type use_2cta_instrs: bool
: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/D 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 = SM100PersistentDenseGemmAlphaBetaKernel(
... acc_dtype=cutlass.Float32,
... epi_dtype=cutlass.Float32,
... use_2cta_instrs=True,
... mma_tiler_mn=(128, 128),
... cluster_shape_mn=(2, 2),
... )
>>> gemm(a_tensor, b_tensor, c_tensor, d_tensor, alpha, beta, max_active_clusters, stream)
"""
def __init__(
self,
acc_dtype: Type[cutlass.Numeric],
epi_dtype: Type[cutlass.Numeric],
use_2cta_instrs: bool,
mma_tiler_mn: Tuple[int, int],
cluster_shape_mn: Tuple[int, int],
):
"""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_mn: The (ClusterM, ClusterN) shape of the CTA cluster.
:param acc_dtype: Data type of the accumulator.
:type acc_dtype: type[cutlass.Numeric]
:param epi_dtype: Data type of the epilogue.
:type epi_dtype: type[cutlass.Numeric]
:param mma_tiler_mn: Tuple (M, N) shape of the MMA instruction.
:type mma_tiler_mn: Tuple[int, int]
:param use_2cta_instrs: Boolean, True to use cta_group=2 MMA variant.
:type use_2cta_instrs: bool
:param cluster_shape_mn: Tuple (ClusterM, ClusterN) shape of the cluster.
:type cluster_shape_mn: Tuple[int, int]
"""
self.acc_dtype: Type[cutlass.Numeric] = acc_dtype
self.epi_dtype: Type[cutlass.Numeric] = epi_dtype
self.use_2cta_instrs = use_2cta_instrs
self.cluster_shape_mn = cluster_shape_mn
# K dimension is deferred in _setup_attributes
self.mma_tiler_mn = mma_tiler_mn
self.mma_tiler = (*mma_tiler_mn, 1)
self.cta_group = (
tcgen05.CtaGroup.TWO if use_2cta_instrs else tcgen05.CtaGroup.ONE
)
self.occupancy = 1
# Set specialized warp ids
self.epilog_warp_ids = (
0,
1,
2,
3,
)
self.mma_warp_id = 4
self.tma_warp_id = 5
self.epilog_load_warp_id = 6
self.threads_per_cta = 32 * len(
(
self.mma_warp_id,
self.tma_warp_id,
*self.epilog_warp_ids,
self.epilog_load_warp_id,
)
)
# Set barrier id for cta sync, epilogue sync and tmem ptr sync
self.cta_sync_bar_id = 1
self.epilog_sync_bar_id = 2
self.tmem_alloc_sync_bar_id = 3
self.num_smem_capacity = utils.get_smem_capacity_in_bytes("sm_100")
def _setup_attributes(self):
"""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/D stage counts in shared memory
- Computing A/B/C/D shared memory layout
- Computing tensor memory allocation columns
"""
# Configure tiled mma
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],
)
# Compute mma/cluster/tile shapes
mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2])
mma_inst_tile_k = 4
self.mma_tiler = (
self.mma_tiler[0],
self.mma_tiler[1],
mma_inst_shape_k * mma_inst_tile_k,
)
self.cta_tile_shape_mnk = (
self.mma_tiler[0] // cute.size(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_mn, 1)),
(tiled_mma.thr_id.shape,),
)
# Compute number of multicast CTAs for A/B
self.num_mcast_ctas_a = cute.size(self.cluster_layout_vmnk.shape[2])
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
# Compute epilogue subtile
self.epi_tile = sm100_utils.compute_epilogue_tile_shape(
self.cta_tile_shape_mnk,
self.use_2cta_instrs,
layout_d=self.cd_layout,
elem_ty_d=self.d_dtype,
layout_c=self.cd_layout,
elem_ty_c=self.c_dtype,
)
# Setup A/B/C stage count in shared memory and ACC stage count in tensor memory
(
self.num_acc_stage,
self.num_ab_stage,
self.num_c_stage,
self.num_d_stage,
) = self._compute_stages(
tiled_mma,
self.mma_tiler,
self.a_dtype,
self.b_dtype,
self.epi_tile,
self.c_dtype,
self.d_dtype,
self.cd_layout,
self.num_smem_capacity,
self.occupancy,
)
# Compute A/B/C shared memory layout
self.a_smem_layout_staged = sm100_utils.make_smem_layout_a(
tiled_mma,
self.mma_tiler,
self.a_dtype,
self.num_ab_stage,
)
self.b_smem_layout_staged = sm100_utils.make_smem_layout_b(
tiled_mma,
self.mma_tiler,
self.b_dtype,
self.num_ab_stage,
)
self.c_smem_layout_staged = sm100_utils.make_smem_layout_epi(
self.c_dtype,
self.cd_layout,
self.epi_tile,
self.num_c_stage,
)
self.d_smem_layout_staged = sm100_utils.make_smem_layout_epi(
self.d_dtype,
self.cd_layout,
self.epi_tile,
self.num_d_stage,
)
# Compute the number of tensor memory allocation columns
self.num_tmem_alloc_cols = self._compute_num_tmem_alloc_cols(
tiled_mma, self.mma_tiler, self.num_acc_stage
)
@cute.jit
def __call__(
self,
a: cute.Tensor,
b: cute.Tensor,
c: cute.Tensor,
d: cute.Tensor,
alpha: cutlass.Float32,
beta: cutlass.Float32,
max_active_clusters: cutlass.Constexpr,
stream: cuda.CUstream,
epilogue_op: cutlass.Constexpr = lambda x: x,
):
"""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 a: Input tensor A
:type a: cute.Tensor
:param b: Input tensor B
:type b: cute.Tensor
:param c: Input tensor C
:type c: cute.Tensor
:param d: Output tensor D
:type d: 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
:param alpha: Scalar multiplier for the matrix product of A and B
:type alpha: cutlass.Float32
:param beta: Scalar multiplier for the matrix C
:type beta: cutlass.Float32
:param epilogue_op: Optional elementwise lambda function to apply to the output tensor
:type epilogue_op: cutlass.Constexpr
: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.
"""
# Setup static attributes before smem/grid/tma computation
self.a_dtype: Type[cutlass.Numeric] = a.element_type
self.b_dtype: Type[cutlass.Numeric] = b.element_type
self.c_dtype: Type[cutlass.Numeric] = c.element_type
self.d_dtype: Type[cutlass.Numeric] = d.element_type
self.a_major_mode = utils.LayoutEnum.from_tensor(a).mma_major_mode()
self.b_major_mode = utils.LayoutEnum.from_tensor(b).mma_major_mode()
self.cd_layout = utils.LayoutEnum.from_tensor(c)
if cutlass.const_expr(self.cd_layout != utils.LayoutEnum.from_tensor(d)):
raise ValueError("C and D must have the same layout.")
# Check if input data types are compatible with MMA instruction
if cutlass.const_expr(self.a_dtype != self.b_dtype):
raise TypeError(f"Type must match: {self.a_dtype} != {self.b_dtype}")
# Setup attributes that dependent on gemm inputs
self._setup_attributes()
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],
)
atom_thr_size = cute.size(tiled_mma.thr_id.shape)
# Setup TMA load for A
a_op = sm100_utils.cluster_shape_to_tma_atom_A(
self.cluster_shape_mn, tiled_mma.thr_id
)
a_smem_layout = cute.slice_(self.a_smem_layout_staged, (None, None, None, 0))
tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A(
a_op,
a,
a_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
internal_type=(
cutlass.TFloat32 if a.element_type is cutlass.Float32 else None
),
)
# Setup TMA load for B
b_op = sm100_utils.cluster_shape_to_tma_atom_B(
self.cluster_shape_mn, tiled_mma.thr_id
)
b_smem_layout = cute.slice_(self.b_smem_layout_staged, (None, None, None, 0))
tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B(
b_op,
b,
b_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
internal_type=(
cutlass.TFloat32 if b.element_type is cutlass.Float32 else None
),
)
a_copy_size = cute.size_in_bytes(self.a_dtype, a_smem_layout)
b_copy_size = cute.size_in_bytes(self.b_dtype, b_smem_layout)
self.num_tma_load_bytes = (a_copy_size + b_copy_size) * atom_thr_size
# Setup TMA for C/D
tma_atom_d = None
tma_tensor_d = None
tma_atom_c = None
tma_tensor_c = None
self.tma_c_load_bytes = 0
d_smem_layout = cute.slice_(self.d_smem_layout_staged, (None, None, 0))
tma_atom_d, tma_tensor_d = cpasync.make_tiled_tma_atom(
cpasync.CopyBulkTensorTileS2GOp(),
d,
d_smem_layout,
self.epi_tile,
)
c_smem_layout = cute.slice_(self.c_smem_layout_staged, (None, None, 0))
self.tma_c_load_bytes = cute.size_in_bytes(self.c_dtype, c_smem_layout)
tma_atom_c, tma_tensor_c = cpasync.make_tiled_tma_atom(
cpasync.CopyBulkTensorTileG2SOp(),
c,
c_smem_layout,
self.epi_tile,
)
# Compute grid size
self.tile_sched_params, grid = self._compute_grid(
d, self.cta_tile_shape_mnk, self.cluster_shape_mn, max_active_clusters
)
self.buffer_align_bytes = 1024
c_smem_size = cute.cosize(self.c_smem_layout_staged.outer)
d_smem_size = cute.cosize(self.d_smem_layout_staged.outer)
# Define shared storage for kernel
@cute.struct
class SharedStorage:
ab_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
ab_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
acc_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
acc_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
c_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_c_stage]
c_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_c_stage]
tmem_dealloc_mbar_ptr: cutlass.Int64
tmem_holding_buf: cutlass.Int32
# (EPI_TILE_M, EPI_TILE_N, STAGE)
sD: cute.struct.Align[
cute.struct.MemRange[
self.d_dtype,
d_smem_size,
],
self.buffer_align_bytes,
]
# (EPI_TILE_M, EPI_TILE_N, STAGE)
sC: cute.struct.Align[
cute.struct.MemRange[
self.c_dtype,
c_smem_size,
],
self.buffer_align_bytes,
]
# (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,
]
self.shared_storage = SharedStorage
# Launch the kernel synchronously
self.kernel(
tiled_mma,
tma_atom_a,
tma_tensor_a,
tma_atom_b,
tma_tensor_b,
tma_atom_c,
tma_tensor_c,
tma_atom_d,
tma_tensor_d,
self.cluster_layout_vmnk,
self.a_smem_layout_staged,
self.b_smem_layout_staged,
self.c_smem_layout_staged,
self.d_smem_layout_staged,
self.epi_tile,
self.tile_sched_params,
epilogue_op,
alpha,
beta,
).launch(
grid=grid,
block=[self.threads_per_cta, 1, 1],
cluster=(*self.cluster_shape_mn, 1),
stream=stream,
)
return
# GPU device kernel
@cute.kernel
def kernel(
self,
tiled_mma: cute.TiledMma,
tma_atom_a: cute.CopyAtom,
mA_mkl: cute.Tensor,
tma_atom_b: cute.CopyAtom,
mB_nkl: cute.Tensor,
tma_atom_c: Optional[cute.CopyAtom],
mC_mnl: cute.Tensor,
tma_atom_d: Optional[cute.CopyAtom],
mD_mnl: cute.Tensor,
cluster_layout_vmnk: cute.Layout,
a_smem_layout_staged: cute.ComposedLayout,
b_smem_layout_staged: cute.ComposedLayout,
c_smem_layout_staged: Union[cute.Layout, cute.ComposedLayout, None],
d_smem_layout_staged: Union[cute.Layout, cute.ComposedLayout, None],
epi_tile: cute.Tile,
tile_sched_params: utils.PersistentTileSchedulerParams,
epilogue_op: cutlass.Constexpr,
alpha: cutlass.Float32,
beta: cutlass.Float32,
):
"""
GPU device kernel performing the Persistent batched GEMM computation.
"""
warp_idx = cute.arch.warp_idx()
warp_idx = cute.arch.make_warp_uniform(warp_idx)
#
# Prefetch tma desc
#
if warp_idx == self.tma_warp_id:
cpasync.prefetch_descriptor(tma_atom_a)
cpasync.prefetch_descriptor(tma_atom_b)
cpasync.prefetch_descriptor(tma_atom_c)
cpasync.prefetch_descriptor(tma_atom_d)
use_2cta_instrs = cute.size(tiled_mma.thr_id.shape) == 2
#
# Setup cta/thread coordinates
#
# Coords inside cluster
bidx, bidy, bidz = 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()
)
block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(
cta_rank_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 = utils.SmemAllocator()
storage = smem.allocate(self.shared_storage)
# Initialize mainloop ab_pipeline (barrier) and states
ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
num_tma_producer = self.num_mcast_ctas_a + self.num_mcast_ctas_b - 1
ab_pipeline_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread, num_tma_producer
)
ab_pipeline = pipeline.PipelineTmaUmma.create(
barrier_storage=storage.ab_full_mbar_ptr.data_ptr(),
num_stages=self.num_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,
)
# Initialize acc_pipeline (barrier) and states
acc_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
num_acc_consumer_threads = len(self.epilog_warp_ids) * (
2 if use_2cta_instrs else 1
)
acc_pipeline_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread, num_acc_consumer_threads
)
acc_pipeline = pipeline.PipelineUmmaAsync.create(
barrier_storage=storage.acc_full_mbar_ptr.data_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,
)
# Load C pipeline
c_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
c_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread,
len(self.epilog_warp_ids),
)
c_pipeline = pipeline.PipelineTmaAsync.create(
barrier_storage=storage.c_full_mbar_ptr.data_ptr(),
num_stages=self.num_c_stage,
producer_group=c_producer_group,
consumer_group=c_consumer_group,
tx_count=self.tma_c_load_bytes,
)
tmem_alloc_barrier = pipeline.NamedBarrier(
barrier_id=self.tmem_alloc_sync_bar_id,
num_threads=32 * len((self.mma_warp_id, *self.epilog_warp_ids)),
)
# Tensor memory dealloc barrier init
tmem = utils.TmemAllocator(
storage.tmem_holding_buf,
barrier_for_retrieve=tmem_alloc_barrier,
allocator_warp_id=self.epilog_warp_ids[0],
is_two_cta=use_2cta_instrs,
two_cta_tmem_dealloc_mbar_ptr=storage.tmem_dealloc_mbar_ptr,
)
# Cluster arrive after barrier init
if cute.size(self.cluster_shape_mn) > 1:
cute.arch.cluster_arrive_relaxed()
#
# Setup smem tensor A/B/C/D
#
# (EPI_TILE_M, EPI_TILE_N, STAGE)
sC = storage.sC.get_tensor(
c_smem_layout_staged.outer, swizzle=c_smem_layout_staged.inner
)
sD = storage.sD.get_tensor(
d_smem_layout_staged.outer, swizzle=d_smem_layout_staged.inner
)
# (MMA, MMA_M, MMA_K, STAGE)
sA = storage.sA.get_tensor(
a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner
)
# (MMA, MMA_N, MMA_K, STAGE)
sB = storage.sB.get_tensor(
b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner
)
#
# Compute multicast mask for A/B buffer full
#
a_full_mcast_mask = None
b_full_mcast_mask = None
if cutlass.const_expr(self.is_a_mcast or self.is_b_mcast or use_2cta_instrs):
a_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
)
b_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=1
)
#
# Local_tile partition global tensors
#
# (bM, bK, loopM, loopK, loopL)
gA_mkl = cute.local_tile(
mA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
)
# (bN, bK, loopN, loopK, loopL)
gB_nkl = cute.local_tile(
mB_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
)
# (bM, bN, loopM, loopN, loopL)
gC_mnl = cute.local_tile(
mC_mnl, cute.slice_(self.mma_tiler, (None, None, 0)), (None, None, None)
)
# (bM, bN, loopM, loopN, loopL)
gD_mnl = cute.local_tile(
mD_mnl, cute.slice_(self.mma_tiler, (None, None, 0)), (None, None, None)
)
k_tile_cnt = cute.size(gA_mkl, mode=[3])
#
# Partition global tensor for TiledMMA_A/B/C/D
#
thr_mma = tiled_mma.get_slice(mma_tile_coord_v)
# (MMA, MMA_M, MMA_K, loopM, loopK, loopL)
tCgA = thr_mma.partition_A(gA_mkl)
# (MMA, MMA_N, MMA_K, loopN, loopK, loopL)
tCgB = thr_mma.partition_B(gB_nkl)
# (MMA, MMA_M, MMA_N, loopM, loopN, loopL)
tCgC = thr_mma.partition_C(gC_mnl)
# (MMA, MMA_M, MMA_N, loopM, loopN, loopL)
tCgD = thr_mma.partition_C(gD_mnl)
#
# Partition global/shared tensor for TMA load A/B
#
# TMA load A partition_S/D
a_cta_layout = cute.make_layout(
cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape
)
# ((atom_v, rest_v), STAGE)
# ((atom_v, rest_v), tiles_m, tiles_k, tiles_l)
tAsA, tAgA = cpasync.tma_partition(
tma_atom_a,
block_in_cluster_coord_vmnk[2],
a_cta_layout,
cute.group_modes(sA, 0, 3),
cute.group_modes(tCgA, 0, 3),
)
# TMA load B partition_S/D
b_cta_layout = cute.make_layout(
cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape
)
# ((atom_v, rest_v), STAGE)
# ((atom_v, rest_v), tiles_n, tiles_k, tiles_l)
tBsB, tBgB = cpasync.tma_partition(
tma_atom_b,
block_in_cluster_coord_vmnk[1],
b_cta_layout,
cute.group_modes(sB, 0, 3),
cute.group_modes(tCgB, 0, 3),
)
#
# Partition shared/tensor memory tensor for TiledMMA_A/B/C/D
#
# (MMA, MMA_M, MMA_K, STAGE)
tCrA = tiled_mma.make_fragment_A(sA)
# (MMA, MMA_N, MMA_K, STAGE)
tCrB = tiled_mma.make_fragment_B(sB)
# (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)
)
# Named barriers
#
cta_sync_barrier = pipeline.NamedBarrier(
self.cta_sync_bar_id, self.threads_per_cta
)
epilog_sync_barrier = pipeline.NamedBarrier(
self.epilog_sync_bar_id, 32 * len(self.epilog_warp_ids)
)
#
# Cluster wait before tensor memory alloc
#
if cute.size(self.cluster_shape_mn) > 1:
cute.arch.cluster_wait()
else:
cta_sync_barrier.arrive_and_wait()
#
# Specialized TMA load warp
#
if warp_idx == self.tma_warp_id:
#
# Persistent tile scheduling loop
#
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
ab_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_ab_stage
)
while work_tile.is_valid_tile:
# Get tile coord from tile scheduler
cur_tile_coord = work_tile.tile_idx
mma_tile_coord_mnl = (
cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
cur_tile_coord[1],
cur_tile_coord[2],
)
#
# Slice to per mma tile index
#
# ((atom_v, rest_v), loopK)
tAgA_slice = tAgA[
(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])
]
# ((atom_v, rest_v), loopK)
tBgB_slice = tBgB[
(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])
]
# Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt
ab_producer_state.reset_count()
peek_ab_empty_status = cutlass.Boolean(1)
if ab_producer_state.count < k_tile_cnt:
peek_ab_empty_status = ab_pipeline.producer_try_acquire(
ab_producer_state
)
#
# Tma load loop
#
for k_tile in cutlass.range(0, k_tile_cnt, 1, unroll=1):
# Conditionally wait for AB buffer empty
ab_pipeline.producer_acquire(
ab_producer_state, peek_ab_empty_status
)
# TMA load A/B
cute.copy(
tma_atom_a,
tAgA_slice[(None, ab_producer_state.count)],
tAsA[(None, ab_producer_state.index)],
tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
mcast_mask=a_full_mcast_mask,
)
cute.copy(
tma_atom_b,
tBgB_slice[(None, ab_producer_state.count)],
tBsB[(None, ab_producer_state.index)],
tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
mcast_mask=b_full_mcast_mask,
)
# Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt + k_tile + 1
ab_producer_state.advance()
peek_ab_empty_status = cutlass.Boolean(1)
if ab_producer_state.count < k_tile_cnt:
peek_ab_empty_status = ab_pipeline.producer_try_acquire(
ab_producer_state
)
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
#
# Wait A/B buffer empty
#
ab_pipeline.producer_tail(ab_producer_state)
#
# Specialized MMA warp
#
if warp_idx == self.mma_warp_id:
#
# Bar sync for retrieve tensor memory ptr from shared mem
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(tmem_ptr, tCtAcc_fake.layout)
#
# Persistent tile scheduling loop
#
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
ab_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_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
cur_tile_coord = work_tile.tile_idx
mma_tile_coord_mnl = (
cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
cur_tile_coord[1],
cur_tile_coord[2],
)
# Set tensor memory buffer for current tile
# (MMA, MMA_M, MMA_N)
tCtAcc = tCtAcc_base[(None, None, None, acc_producer_state.index)]
# Peek (try_wait) AB buffer full for k_tile = 0
ab_consumer_state.reset_count()
peek_ab_full_status = cutlass.Boolean(1)
if ab_consumer_state.count < k_tile_cnt and is_leader_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
#
for k_tile in range(k_tile_cnt):
if is_leader_cta:
# Conditionally wait for AB buffer full
ab_pipeline.consumer_wait(
ab_consumer_state, peek_ab_full_status
)
# tCtAcc += tCrA * tCrB
num_k_blocks = cute.size(tCrA, mode=[2])
for k_block_idx in cutlass.range(
num_k_blocks, unroll_full=True
):
k_block_coord = (
None,
None,
k_block_idx,
ab_consumer_state.index,
)
cute.gemm(
tiled_mma,
tCtAcc,
tCrA[k_block_coord],
tCrB[k_block_coord],
tCtAcc,
)
# Enable accumulate on tCtAcc after first k_block
tiled_mma.set(tcgen05.Field.ACCUMULATE, True)
# Async arrive AB buffer empty
ab_pipeline.consumer_release(ab_consumer_state)
# Peek (try_wait) AB buffer full for k_tile = k_tile + 1
ab_consumer_state.advance()
peek_ab_full_status = cutlass.Boolean(1)
if ab_consumer_state.count < k_tile_cnt:
if is_leader_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()
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.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
#
tmem.allocate(self.num_tmem_alloc_cols)
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(tmem_ptr, tCtAcc_fake.layout)
#
# Partition for epilogue
#
epi_tidx = tidx
(
tiled_copy_t2r,
tTR_tAcc_base,
tTR_rAcc,
) = self.epilog_tmem_copy_and_partition(
epi_tidx, tCtAcc_base, tCgD, epi_tile, use_2cta_instrs
)
tTR_rC = None
tiled_copy_s2r = None
simt_atom_c = None
tSR_rC = None
tSR_sC = None
tTR_gC_partitioned = None
tTR_rD = None
tiled_copy_r2s = None
simt_atom_d = None
tRS_rD = None
tRS_sD = None
bSG_sD = None
bSG_gD_partitioned = None
tTR_gD_partitioned = None
tTR_rC = cute.make_rmem_tensor(tTR_rAcc.shape, self.c_dtype)
(tiled_copy_s2r, tSR_rC, tSR_sC) = self.epilog_smem_copy_and_partition_load(
tiled_copy_t2r, tTR_rC, epi_tidx, sC
)
tTR_rD = cute.make_rmem_tensor(tTR_rAcc.shape, self.d_dtype)
(
tiled_copy_r2s,
tRS_rD,
tRS_sD,
) = self.epilog_smem_copy_and_partition_store(
tiled_copy_t2r, tTR_rD, epi_tidx, sD
)
(
tma_atom_d,
bSG_sD,
bSG_gD_partitioned,
) = self.epilog_gmem_copy_and_partition(
epi_tidx, tma_atom_d, tCgD, epi_tile, sD, self.d_dtype
)
#
# Persistent tile scheduling loop
#
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
# Store D pipeline
d_pipeline = None
c_pipeline_consumer_state = None
d_producer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread,
32 * len(self.epilog_warp_ids),
)
d_pipeline = pipeline.PipelineTmaStore.create(
num_stages=self.num_d_stage,
producer_group=d_producer_group,
)
c_pipeline_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_c_stage
)
while work_tile.is_valid_tile:
# Get tile coord from tile scheduler
cur_tile_coord = work_tile.tile_idx
mma_tile_coord_mnl = (
cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
cur_tile_coord[1],
cur_tile_coord[2],
)
#
# Slice to per mma tile index
#
tTR_gC = None
bSG_gD = None
tTR_gD = None
# ((ATOM_V, REST_V), EPI_M, EPI_N)
bSG_gD = bSG_gD_partitioned[
(
None,
None,
None,
*mma_tile_coord_mnl,
)
]
# 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)
tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
bSG_gD = cute.group_modes(bSG_gD, 1, cute.rank(bSG_gD))
#
# Store accumulator to global memory in subtiles
#
subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
num_prev_subtiles = tile_sched.num_tiles_executed * subtile_cnt
for subtile_idx in cutlass.range(subtile_cnt):
#
# Load accumulator from tensor memory buffer to register
#
tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)
# Wait for C load to complete
c_pipeline.consumer_wait(c_pipeline_consumer_state)
# Load C from shared memory to register
cute.copy(
tiled_copy_s2r,
tSR_sC[(None, None, None, c_pipeline_consumer_state.index)],
tSR_rC,
)
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
c_pipeline.consumer_release(c_pipeline_consumer_state)
# Advance pipeline states
c_pipeline_consumer_state.advance()
#
# Perform epilogue op on accumulator and convert to D type
#
acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load()
c_vec_load = tiled_copy_r2s.retile(tSR_rC).load()
d_vec = epilogue_op(
(
alpha.to(self.epi_dtype) * acc_vec.to(self.epi_dtype)
+ beta.to(self.epi_dtype) * c_vec_load.to(self.epi_dtype)
)
).to(self.d_dtype)
tRS_rD.store(d_vec)
#
# Store C to shared memory
#
d_buffer = (num_prev_subtiles + subtile_idx) % self.num_d_stage
cute.copy(
tiled_copy_r2s, tRS_rD, tRS_sD[(None, None, None, d_buffer)]
)
# Fence and barrier to make sure shared memory store is visible to TMA store
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
epilog_sync_barrier.arrive_and_wait()
#
# TMA store D to global memory
#
if warp_idx == self.epilog_warp_ids[0]:
cute.copy(
tma_atom_d,
bSG_sD[(None, d_buffer)],
bSG_gD[(None, subtile_idx)],
)
# Fence and barrier to make sure shared memory store is visible to TMA store
d_pipeline.producer_commit()
d_pipeline.producer_acquire()
epilog_sync_barrier.arrive_and_wait()
#
# 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_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
#
# Dealloc the tensor memory buffer
#
tmem.relinquish_alloc_permit()
epilog_sync_barrier.arrive_and_wait()
tmem.free(tmem_ptr)
#
# Wait for D store complete
#
d_pipeline.producer_tail()
#
# Specialized epilog load warp
#
if warp_idx == self.epilog_load_warp_id:
(
tma_atom_c,
bGS_sC,
bGS_gC_partitioned,
) = self.epilog_gmem_copy_and_partition(
tidx, tma_atom_c, tCgC, epi_tile, sC, self.c_dtype
)
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
c_pipeline_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_c_stage
)
while work_tile.is_valid_tile:
# Get tile coord from tile scheduler
cur_tile_coord = work_tile.tile_idx
mma_tile_coord_mnl = (
cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
cur_tile_coord[1],
cur_tile_coord[2],
)
bGS_gC = bGS_gC_partitioned[
(
None,
None,
None,
*mma_tile_coord_mnl,
)
]
bGS_gC = cute.group_modes(bGS_gC, 1, cute.rank(bGS_gC))
subtile_cnt = cute.size(bGS_gC.shape, mode=[1])
for subtile_idx in cutlass.range(subtile_cnt):
# Load C from global memory to shared memory using TMA load
c_pipeline.producer_acquire(c_pipeline_producer_state)
cute.copy(
tma_atom_c,
bGS_gC[(None, subtile_idx)],
bGS_sC[(None, c_pipeline_producer_state.index)],
tma_bar_ptr=c_pipeline.producer_get_barrier(
c_pipeline_producer_state
),
)
c_pipeline_producer_state.advance()
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
#
# Wait C buffer empty
#
c_pipeline.producer_tail(c_pipeline_producer_state)
def epilog_tmem_copy_and_partition(
self,
tidx: cutlass.Int32,
tAcc: cute.Tensor,
gC_mnl: cute.Tensor,
epi_tile: cute.Tile,
use_2cta_instrs: Union[cutlass.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: cutlass.Int32
:param tAcc: The accumulator tensor to be copied and partitioned
:type tAcc: cute.Tensor
:param gC_mnl: The global tensor C
:type gC_mnl: 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.cd_layout,
self.c_dtype,
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)
# (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N)
gC_mnl_epi = cute.flat_divide(gC_mnl[((None, None), 0, 0, 0, 0, 0)], epi_tile)
# (T2R, T2R_M, T2R_N, EPI_M, EPI_N)
tTR_gC = thr_copy_t2r.partition_D(gC_mnl_epi)
# (T2R, T2R_M, T2R_N)
tTR_rAcc = cute.make_rmem_tensor(
tTR_gC[(None, None, None, 0, 0)].shape, self.acc_dtype
)
return tiled_copy_t2r, tTR_tAcc, tTR_rAcc
def epilog_smem_copy_and_partition_load(
self,
tiled_copy_t2r: cute.TiledCopy,
tTR_rC: cute.Tensor,
tidx: cutlass.Int32,
sC: cute.Tensor,
) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
"""
Make tiledCopy for shared memory load, then use it to partition register array (destination) and shared memory (source).
:param tiled_copy_t2r: The tiled copy operation for tmem to register copy(t2r)
:type tiled_copy_t2r: cute.TiledCopy
:param tTR_rC: The partitioned accumulator tensor
:type tTR_rC: cute.Tensor
:param tidx: The thread index in epilogue warp groups
:type tidx: cutlass.Int32
:param sC: The shared memory tensor to be copied and partitioned
:type sC: cute.Tensor
:return: A tuple containing (tiled_copy_s2r, tSR_rC, tSR_sC) where:
- tiled_copy_s2r: The tiled copy operation for smem to register copy(s2r)
- tSR_rC: The partitioned tensor C (register destination)
- tSR_sC: The partitioned tensor C (smem source)
:rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]
"""
copy_atom_s2r = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), self.c_dtype)
tiled_copy_s2r = cute.make_tiled_copy_D(copy_atom_s2r, tiled_copy_t2r)
# (S2R, S2R_M, S2R_N, PIPE_C)
thr_copy_s2r = tiled_copy_s2r.get_slice(tidx)
tSR_sC = thr_copy_s2r.partition_D(sC)
# (S2R, S2R_M, S2R_N)
tSR_rC = tiled_copy_s2r.retile(tTR_rC)
return tiled_copy_s2r, tSR_rC, tSR_sC
def epilog_smem_copy_and_partition_store(
self,
tiled_copy_t2r: cute.TiledCopy,
tTR_rC: cute.Tensor,
tidx: cutlass.Int32,
sC: cute.Tensor,
) -> 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_rC: The partitioned accumulator tensor
:type tTR_rC: cute.Tensor
:param tidx: The thread index in epilogue warp groups
:type tidx: cutlass.Int32
:param sC: The shared memory tensor to be copied and partitioned
:type sC: cute.Tensor
:return: A tuple containing (tiled_copy_r2s, tRS_rC, tRS_sC) where:
- tiled_copy_r2s: The tiled copy operation for register to smem copy(r2s)
- tRS_rC: The partitioned tensor C (register source)
- tRS_sC: The partitioned tensor C (smem destination)
:rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]
"""
copy_atom_r2s = sm100_utils.get_smem_store_op(
self.cd_layout, self.d_dtype, 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_sC = thr_copy_r2s.partition_D(sC)
# (R2S, R2S_M, R2S_N)
tRS_rC = tiled_copy_r2s.retile(tTR_rC)
return tiled_copy_r2s, tRS_rC, tRS_sC
def epilog_gmem_copy_and_partition(
self,
tidx: cutlass.Int32,
atom: Union[cute.CopyAtom, cute.TiledCopy],
gC_mnl: cute.Tensor,
epi_tile: cute.Tile,
sC: cute.Tensor,
dtype: Type[cutlass.Numeric],
) -> Tuple[cute.CopyAtom, cute.Tensor, cute.Tensor]:
"""Make tiledCopy for global memory store, then use it to:
- partition register array (source) and global memory (destination) for none TMA store version;
- partition shared memory (source) and global memory (destination) for TMA store version.
:param tidx: The thread index in epilogue warp groups
:type tidx: cutlass.Int32
:param atom: The copy_atom_c to be used for TMA store version, or tiled_copy_t2r for none TMA store version
:type atom: cute.CopyAtom or cute.TiledCopy
:param gC_mnl: The global tensor C
:type gC_mnl: cute.Tensor
:param epi_tile: The epilogue tiler
:type epi_tile: cute.Tile
:param sC: The shared memory tensor to be copied and partitioned
:type sC: cute.Tensor
:return: A tuple containing either:
- For TMA store: (tma_atom_c, bSG_sC, bSG_gC) where:
- tma_atom_c: The TMA copy atom
- bSG_sC: The partitioned shared memory tensor C
- bSG_gC: The partitioned global tensor C
- For non-TMA store: (simt_atom, tTR_rC, tTR_gC) where:
- simt_atom: The SIMT copy atom
- tTR_rC: The register tensor C
- tTR_gC: The partitioned global tensor C
:rtype: Tuple[cute.CopyAtom, cute.Tensor, cute.Tensor]
"""
# (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, tiles_m, tiles_n, tiles_l)
gC_epi = cute.flat_divide(
gC_mnl[((None, None), 0, 0, None, None, None)], epi_tile
)
tma_atom_c = atom
sC_for_tma_partition = cute.group_modes(sC, 0, 2)
gC_for_tma_partition = cute.group_modes(gC_epi, 0, 2)
# ((ATOM_V, REST_V), EPI_M, EPI_N)
# ((ATOM_V, REST_V), EPI_M, EPI_N, tiles_m, tiles_n, tiles_l)
bSG_sC, bSG_gC = cpasync.tma_partition(
tma_atom_c,
0,
cute.make_layout(1),
sC_for_tma_partition,
gC_for_tma_partition,
)
return tma_atom_c, bSG_sC, bSG_gC
@staticmethod
def _compute_stages(
tiled_mma: cute.TiledMma,
mma_tiler_mnk: Tuple[int, int, int],
a_dtype: Type[cutlass.Numeric],
b_dtype: Type[cutlass.Numeric],
epi_tile: cute.Tile,
c_dtype: Type[cutlass.Numeric],
d_dtype: Type[cutlass.Numeric],
cd_layout: utils.LayoutEnum,
num_smem_capacity: int,
occupancy: int,
) -> Tuple[int, int, int]:
"""Computes the number of stages for A/B/C/D 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 c_dtype: Data type of operand C (input).
:type c_dtype: type[cutlass.Numeric]
:param d_dtype: Data type of operand D (output).
:type d_dtype: type[cutlass.Numeric]
:param cd_layout: Layout of operand C/D in global memory.
:type cd_layout: utils.LayoutEnum
:param num_smem_capacity: Total available shared memory capacity in bytes.
:type num_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]
"""
# Default ACC stages
num_acc_stage = 2
# Default C stages
num_c_stage = 2
# Default D stages
num_d_stage = 2
# Calculate smem layout and size for one stage of A, B, C, and D
a_smem_layout_stage_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
)
c_smem_layout_staged_one = sm100_utils.make_smem_layout_epi(
c_dtype,
cd_layout,
epi_tile,
1,
)
d_smem_layout_staged_one = sm100_utils.make_smem_layout_epi(
d_dtype,
cd_layout,
epi_tile,
1,
)
ab_bytes_per_stage = cute.size_in_bytes(
a_dtype, a_smem_layout_stage_one
) + cute.size_in_bytes(b_dtype, b_smem_layout_staged_one)
mbar_helpers_bytes = 1024
c_bytes_per_stage = cute.size_in_bytes(c_dtype, c_smem_layout_staged_one)
c_bytes = c_bytes_per_stage * num_c_stage
d_bytes_per_stage = cute.size_in_bytes(d_dtype, d_smem_layout_staged_one)
d_bytes = d_bytes_per_stage * num_d_stage
# Calculate A/B stages:
# Start with total smem per CTA (capacity / occupancy)
# Subtract reserved bytes and initial C/D stages bytes
# Divide remaining by bytes needed per A/B stage
num_ab_stage = (
num_smem_capacity // occupancy - (mbar_helpers_bytes + c_bytes + d_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
num_d_stage += (
num_smem_capacity
- occupancy * ab_bytes_per_stage * num_ab_stage
- occupancy * (mbar_helpers_bytes + c_bytes + d_bytes)
) // (occupancy * d_bytes_per_stage)
return num_acc_stage, num_ab_stage, num_c_stage, num_d_stage
@staticmethod
def _compute_grid(
d: cute.Tensor,
cta_tile_shape_mnk: Tuple[int, int, int],
cluster_shape_mn: Tuple[int, int],
max_active_clusters: cutlass.Constexpr,
) -> Tuple[utils.PersistentTileSchedulerParams, Tuple[int, int, int]]:
"""Use persistent tile scheduler to compute the grid size for the output tensor C.
:param d: The output tensor D
:type d: cute.Tensor
:param cta_tile_shape_mnk: The shape (M, N, K) of the CTA tile.
:type cta_tile_shape_mnk: tuple[int, int, int]
:param cluster_shape_mn: Shape of each cluster in M, N dimensions.
:type cluster_shape_mn: tuple[int, int]
:param max_active_clusters: Maximum number of active clusters.
:type max_active_clusters: cutlass.Constexpr
:return: A tuple containing:
- tile_sched_params: Parameters for the persistent tile scheduler.
- grid: Grid shape for kernel launch.
:rtype: Tuple[utils.PersistentTileSchedulerParams, tuple[int, int, int]]
"""
d_shape = cute.slice_(cta_tile_shape_mnk, (None, None, 0))
gd = cute.zipped_divide(d, tiler=d_shape)
num_ctas_mnl = gd[(0, (None, None, None))].shape
cluster_shape_mnl = (*cluster_shape_mn, 1)
tile_sched_params = utils.PersistentTileSchedulerParams(
num_ctas_mnl, cluster_shape_mnl
)
grid = utils.StaticPersistentTileScheduler.get_grid_shape(
tile_sched_params, max_active_clusters
)
return tile_sched_params, grid
@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 = utils.get_num_tmem_alloc_cols(tCtAcc_fake)
return num_tmem_alloc_cols
def is_valid_dtypes(
self,
ab_dtype: Type[cutlass.Numeric],
c_dtype: Type[cutlass.Numeric],
) -> 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 c_dtype: The data type of the output tensor
:type c_dtype: Type[cutlass.Numeric]
:return: True if the dtypes are valid, False otherwise
:rtype: bool
"""
valid_ab_dtypes = {
cutlass.Float16,
cutlass.BFloat16,
cutlass.TFloat32,
cutlass.Uint8,
cutlass.Int8,
cutlass.Float8E4M3FN,
cutlass.Float8E5M2,
}
if ab_dtype not in valid_ab_dtypes:
return False
if self.acc_dtype not in {cutlass.Float32, cutlass.Float16, cutlass.Int32}:
return False
# Define compatibility mapping between accumulator type and AB type
acc_ab_compatibility = {
cutlass.Float32: {
cutlass.Float16,
cutlass.BFloat16,
cutlass.TFloat32,
cutlass.Float8E4M3FN,
cutlass.Float8E5M2,
}, # Float32 accumulator supports floating point AB types only
cutlass.Float16: {
cutlass.Float16,
cutlass.Float8E4M3FN,
cutlass.Float8E5M2,
},
cutlass.Int32: {cutlass.Uint8, cutlass.Int8},
}
# Check compatibility between accumulator type and AB type
if ab_dtype not in acc_ab_compatibility[self.acc_dtype]:
return False
# Define compatibility mapping between accumulator type and C type
acc_c_compatibility = {
cutlass.Float32: {
cutlass.Float32,
cutlass.Float16,
cutlass.BFloat16,
cutlass.Float8E4M3FN,
cutlass.Float8E5M2,
cutlass.Int32,
cutlass.Int8,
cutlass.Uint8,
},
cutlass.Float16: {
cutlass.BFloat16,
cutlass.Float16,
},
cutlass.Int32: {
cutlass.BFloat16,
cutlass.Float16,
cutlass.Float32,
cutlass.Int32,
cutlass.Int8,
cutlass.Uint8,
},
}
# Check compatibility between accumulator type and C type
if c_dtype not in acc_c_compatibility[self.acc_dtype]:
return False
return True
def is_valid_mma_tiler_and_cluster_shape(self) -> bool:
"""Check if the mma tiler and cluster shape are valid.
:return: True if the mma tiler and cluster shape are valid, False otherwise
:rtype: bool
"""
is_valid = True
# Skip invalid mma tile shape
if not (
(not self.use_2cta_instrs and self.mma_tiler_mn[0] in [64, 128])
or (self.use_2cta_instrs and self.mma_tiler_mn[0] in [128, 256])
):
is_valid = False
if self.mma_tiler_mn[1] not in range(32, 257, 32):
is_valid = False
# Skip illegal cluster shape
if self.cluster_shape_mn[0] % (2 if self.use_2cta_instrs else 1) != 0:
is_valid = False
# Skip invalid cluster shape
is_power_of_2 = lambda x: x > 0 and (x & (x - 1)) == 0
if (
self.cluster_shape_mn[0] * self.cluster_shape_mn[1] > 16
or self.cluster_shape_mn[0] <= 0
or self.cluster_shape_mn[1] <= 0
or not is_power_of_2(self.cluster_shape_mn[0])
or not is_power_of_2(self.cluster_shape_mn[1])
):
is_valid = False
return is_valid
def is_valid_tensor_alignment(
self,
m: int,
n: int,
k: int,
l: int,
ab_dtype: Type[cutlass.Numeric],
c_dtype: Type[cutlass.Numeric],
d_dtype: Type[cutlass.Numeric],
a_major: str,
b_major: str,
cd_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 c_dtype: The data type of the C tensor
:type c_dtype: Type[cutlass.Numeric]
:param d_dtype: The data type of the D 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 cd_major: The major axis of the C/D tensor
:type cd_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(c_dtype, cd_major == "m", (m, n, l))
or not check_contigous_16B_alignment(d_dtype, cd_major == "m", (m, n, l))
):
is_valid = False
return is_valid
def can_implement(
self, a: cute.Tensor, b: cute.Tensor, c: cute.Tensor, d: cute.Tensor
) -> bool:
"""Check if the given tensors can be implemented by this kernel.
:param a: Input tensor A
:type a: cute.Tensor
:param b: Input tensor B
:type b: cute.Tensor
:param c: Output tensor C
:type c: cute.Tensor
:return: True if the gemm supports the given config, False otherwise
:rtype: bool
"""
m, n, k, l = a.shape[0], b.shape[0], a.shape[1], a.shape[2]
# infer a_major, b_major, cd_major
is_m_major_a = utils.LayoutEnum.from_tensor(a).is_m_major_a()
is_n_major_b = utils.LayoutEnum.from_tensor(b).is_n_major_b()
is_m_major_c = utils.LayoutEnum.from_tensor(c).is_m_major_c()
a_major = "m" if is_m_major_a else "k"
b_major = "n" if is_n_major_b else "k"
cd_major = "m" if is_m_major_c else "n"
can_implement = True
# Skip unsupported types
if not self.is_valid_dtypes(a.element_type, c.element_type):
can_implement = False
# Skip invalid mma tile shape and cluster shape
if not self.is_valid_mma_tiler_and_cluster_shape():
can_implement = False
# Skip illegal problem shape for load/store alignment
if not self.is_valid_tensor_alignment(
m,
n,
k,
l,
a.element_type,
c.element_type,
d.element_type,
a_major,
b_major,
cd_major,
):
can_implement = False
return can_implement
def create_tensors(l, m, n, k, a_major, b_major, cd_major, ab_dtype, c_dtype, d_dtype):
torch.manual_seed(1111)
a_torch_cpu = cutlass_torch.matrix(l, m, k, a_major == "m", ab_dtype)
b_torch_cpu = cutlass_torch.matrix(l, n, k, b_major == "n", ab_dtype)
c_torch_cpu = cutlass_torch.matrix(l, m, n, cd_major == "m", c_dtype)
d_torch_cpu = cutlass_torch.matrix(l, m, n, cd_major == "m", d_dtype)
a_tensor, _ = cutlass_torch.cute_tensor_like(
a_torch_cpu, ab_dtype, is_dynamic_layout=True, assumed_align=16
)
b_tensor, _ = cutlass_torch.cute_tensor_like(
b_torch_cpu, ab_dtype, is_dynamic_layout=True, assumed_align=16
)
c_tensor, _ = cutlass_torch.cute_tensor_like(
c_torch_cpu, c_dtype, is_dynamic_layout=True, assumed_align=16
)
d_tensor, d_torch_gpu = cutlass_torch.cute_tensor_like(
d_torch_cpu, d_dtype, is_dynamic_layout=True, assumed_align=16
)
return (
a_tensor,
b_tensor,
c_tensor,
d_tensor,
a_torch_cpu,
b_torch_cpu,
c_torch_cpu,
d_torch_gpu,
)
def run(
a: cute.Tensor,
b: cute.Tensor,
c: cute.Tensor,
d: cute.Tensor,
stream: cuda.CUstream,
alpha: float,
beta: float,
acc_dtype: Type[cutlass.Numeric] = cutlass.Float32,
epi_dtype: Type[cutlass.Numeric] = cutlass.Float32,
mma_tiler_mn: Tuple[int, int] = (256, 256),
cluster_shape_mn: Tuple[int, int] = (2, 1),
use_2cta_instrs: bool = True,
warmup_iterations: int = 0,
iterations: int = 1,
):
"""Run the gemm kernel utility function. It will return the compiled gemm kernel function and its execution time.
:param a: Input tensor A
:type a: cute.Tensor
:param b: Input tensor B
:type b: cute.Tensor
:param c: Output tensor C
:type c: cute.Tensor
:param stream: CUDA stream
:type stream: cuda.CUstream
:param acc_dtype: Accumulator data type, defaults to cutlass.Float32
:type acc_dtype: cutlass.DataType, optional
: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 use_2cta_instrs: Whether to use 2CTA instructions. If not specified in the decorator parameters, the autotuner
will use the default value of True. Otherwise, the autotuner will use the value specified in the decorator parameters.
:type use_2cta_instrs: bool, optional
:return: Compiled GEMM kernel function and its execution time
:rtype: Callable
"""
# Build GEMM object
gemm = SM100PersistentDenseGemmAlphaBetaKernel(
acc_dtype,
epi_dtype,
use_2cta_instrs,
mma_tiler_mn,
cluster_shape_mn,
)
# Check if configuration can be implemented
can_implement = gemm.can_implement(a, b, c, d)
if not can_implement:
raise ValueError(
f"The current config which is invalid/unsupported: use_2cta_instrs = {use_2cta_instrs}, "
f"mma_tiler_mn = {mma_tiler_mn}, cluster_shape_mn = {cluster_shape_mn}"
)
max_active_clusters = utils.HardwareInfo().get_max_active_clusters(
cluster_shape_mn[0] * cluster_shape_mn[1]
)
compiled_gemm = cute.compile(
gemm, a, b, c, d, alpha, beta, max_active_clusters, stream
)
exec_time = testing.benchmark(
compiled_gemm,
kernel_arguments=testing.JitArguments(a, b, c, d, alpha, beta, stream),
stream=stream,
warmup_iterations=warmup_iterations,
iterations=iterations,
)
return compiled_gemm, exec_time
def compare(
a_torch_cpu,
b_torch_cpu,
c_torch_cpu,
d_torch_gpu,
d_dtype,
epi_dtype,
alpha,
beta,
tolerance,
):
# Copy gpu result back
kernel_result = d_torch_gpu.cpu()
# Compute reference result
ref = torch.einsum(
"mkl,nkl->mnl",
a_torch_cpu.to(dtype=torch.float32),
b_torch_cpu.to(dtype=torch.float32),
)
torch_epi_dtype = cutlass_torch.dtype(epi_dtype)
torch_alpha = torch.tensor(alpha, dtype=torch_epi_dtype)
torch_beta = torch.tensor(beta, dtype=torch_epi_dtype)
ref_d_epi_dtype = torch_alpha * ref.to(
dtype=torch_epi_dtype
) + torch_beta * c_torch_cpu.to(dtype=torch_epi_dtype)
# Convert ref to d_dtype
_, ref_torch_gpu = cutlass_torch.cute_tensor_like(
ref_d_epi_dtype, d_dtype, is_dynamic_layout=True, assumed_align=16
)
ref_result = ref_torch_gpu.cpu()
# Assert close results
torch.testing.assert_close(kernel_result, ref_result, atol=tolerance, rtol=1e-05)
def run_dense_gemm(
mnkl: Tuple[int, int, int, int],
ab_dtype: Type[cutlass.Numeric],
c_dtype: Type[cutlass.Numeric],
d_dtype: Type[cutlass.Numeric],
acc_dtype: Type[cutlass.Numeric],
epi_dtype: Type[cutlass.Numeric],
a_major: str,
b_major: str,
cd_major: str,
alpha: float,
beta: float,
mma_tiler_mn: Tuple[int, int],
cluster_shape_mn: Tuple[int, int],
use_2cta_instrs: bool,
tolerance: float,
warmup_iterations: int = 0,
iterations: int = 1,
skip_ref_check: bool = False,
):
"""
Prepare A/B/C/D tensors, launch GPU kernel, and reference checking.
"""
print("Running Blackwell Persistent Dense GEMM test with:")
print(f"mnkl: {mnkl}")
print(
f"AB dtype: {ab_dtype}, C dtype: {c_dtype}, D dtype: {d_dtype}, Acc dtype: {acc_dtype}, Epi dtype: {epi_dtype}"
)
print(f"Matrix majors - A: {a_major}, B: {b_major}, C: {cd_major}, D: {cd_major}")
print(f"Mma Tiler (M, N): {mma_tiler_mn}, Cluster Shape (M, N): {cluster_shape_mn}")
print(f"2CTA MMA instructions: {'True' if use_2cta_instrs else 'False'}")
print(f"Tolerance: {tolerance}")
print(f"Warmup iterations: {warmup_iterations}")
print(f"Iterations: {iterations}")
print(f"Skip reference checking: {skip_ref_check}")
# Unpack parameters
m, n, k, l = mnkl
if not torch.cuda.is_available():
raise RuntimeError("GPU is required to run this example!")
(
a_tensor,
b_tensor,
c_tensor,
d_tensor,
a_torch_cpu,
b_torch_cpu,
c_torch_cpu,
d_torch_gpu,
) = create_tensors(
l, m, n, k, a_major, b_major, cd_major, ab_dtype, c_dtype, d_dtype
)
# 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)
compiled_gemm, exec_time = run(
a_tensor,
b_tensor,
c_tensor,
d_tensor,
current_stream,
alpha,
beta,
acc_dtype,
epi_dtype,
mma_tiler_mn,
cluster_shape_mn,
use_2cta_instrs,
warmup_iterations,
iterations,
)
print(f"Execution time: {exec_time} us")
# Compute reference result
if not skip_ref_check:
compare(
a_torch_cpu,
b_torch_cpu,
c_torch_cpu,
d_torch_gpu,
d_dtype,
epi_dtype,
alpha,
beta,
tolerance,
)
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.TFloat32)
parser.add_argument("--c_dtype", type=cutlass.dtype, default=cutlass.Float32)
parser.add_argument("--d_dtype", type=cutlass.dtype, default=cutlass.Float32)
parser.add_argument("--acc_dtype", type=cutlass.dtype, default=cutlass.Float32)
parser.add_argument("--epi_dtype", type=cutlass.dtype, default=cutlass.Float32)
parser.add_argument("--alpha", type=float, default=1.0, help="alpha scale factor")
parser.add_argument("--beta", type=float, default=0.0, help="beta scale factor")
parser.add_argument(
"--use_2cta_instrs",
action="store_true",
help="Enable 2CTA MMA instructions feature",
)
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("--cd_major", choices=["n", "m"], type=str, default="n")
parser.add_argument(
"--tolerance", type=float, default=1e-01, help="Tolerance for validation"
)
parser.add_argument(
"--warmup_iterations", type=int, default=0, help="Warmup iterations"
)
parser.add_argument(
"--iterations",
type=int,
default=1,
help="Number of iterations to run the kernel",
)
parser.add_argument(
"--skip_ref_check", action="store_true", help="Skip reference checking"
)
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_dense_gemm(
args.mnkl,
args.ab_dtype,
args.c_dtype,
args.d_dtype,
args.acc_dtype,
args.epi_dtype,
args.a_major,
args.b_major,
args.cd_major,
args.alpha,
args.beta,
args.mma_tiler_mn,
args.cluster_shape_mn,
args.use_2cta_instrs,
args.tolerance,
args.warmup_iterations,
args.iterations,
args.skip_ref_check,
)
print("PASS")
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