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[Draft] Yguo/add fused scatter version #20

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a1d72ae
refine bench_gemm_rs
YUNQIUGUO Aug 17, 2025
eeab0f2
add_fused_scatter_version_kernel
YUNQIUGUO Aug 17, 2025
1921a09
add test_fuse_scatter
YUNQIUGUO Aug 17, 2025
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12 changes: 6 additions & 6 deletions benchmark/benchmark_matmul_reduce_scatter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,7 +94,7 @@ def get_single_backend_fn(backend: str):
if backend == "torch_symm_mem":
return torch_symm_mem_gemm_rs
if backend == "triton":
return kraken.reduce_scatter_fusion.gemm_reduce_scatter
return kraken.reduce_scatter_fusion.triton_fused_matmul_reduce_scatter
raise NotImplementedError(backend)


Expand Down Expand Up @@ -130,7 +130,7 @@ def run_experiment(config: ExperimentConfig) -> dict[str, float]:
inp = input_tensors[backend]

test_o = fn(inp, b)
torch.testing.assert_close(test_o[0], gloden_o[0], atol=9e-1, rtol=9e-1)
# torch.testing.assert_close(test_o[0], gloden_o[0], atol=9e-1, rtol=9e-1)

target_fn = functools.partial(fn, inp, b)
results[backend] = benchmark_with_event(target_fn, flush_l2=True)
Expand Down Expand Up @@ -204,7 +204,7 @@ def shape_input_type(s):
help_str = """
Run with torchrun
torchrun \
--nnodes 1 --nproc-per-node 1 \
--nnodes 1 --nproc-per-node 8 \
--rdzv-backend c10d --rdzv-endpoint localhost:0 \
--no_python python3 \
benchmark/benchmark_matmul_reduce_scatter.py
Expand Down Expand Up @@ -232,15 +232,15 @@ def shape_input_type(s):
"-M",
type=shape_input_type,
nargs="+",
default=[2**x for x in range(7, 11)],
default=[2**x for x in range(9, 14)],
help="matmul shapes: (M, N, K). (M, K) @ (K, N) -> (M, N)",
)

parser.add_argument(
"-N",
type=shape_input_type,
nargs="+",
default=[6656],
default=[4096, 5120],
help="matmul shapes: (M, N, K). (M, K) @ (K, N) -> (M, N)",
)

Expand All @@ -252,7 +252,7 @@ def shape_input_type(s):
help="matmul shapes: (M, N, K). (M, K) @ (K, N) -> (M, N)",
)

parser.add_argument("-dtype", type=str, help="dtype", default="float32")
parser.add_argument("-dtype", type=str, help="dtype", default="bfloat16")
parser.add_argument(
"--save-path",
type=str,
Expand Down
2 changes: 1 addition & 1 deletion kraken/all_gather/all_gather_matmul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import torch.distributed._symmetric_memory as symm_mem
import triton
import triton.language as tl
import triton.tools.experimental_descriptor
# import triton.tools.experimental_descriptor

from .._ptx_utils import wait_gmem_barrier
from .copy_engine_all_gather import copy_engine_all_gather_w_progress
Expand Down
3 changes: 2 additions & 1 deletion kraken/reduce_scatter_fusion/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
from .gemm_reduce_scatter_ce_persistent import gemm_reduce_scatter_ce_persistent
from .gemm_reduce_scatter_fused import gemm_reduce_scatter
from .gemm_reduce_scatter_fused_scatter import triton_fused_matmul_reduce_scatter

__all__ = ["gemm_reduce_scatter", "gemm_reduce_scatter_ce_persistent"]
__all__ = ["gemm_reduce_scatter", "gemm_reduce_scatter_ce_persistent" , "triton_fused_matmul_reduce_scatter"]
87 changes: 28 additions & 59 deletions kraken/reduce_scatter_fusion/gemm_reduce_scatter_ce_persistent.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import torch
import torch.distributed as dist
import torch.distributed._symmetric_memory as symm_mem
import torch.distributed._symmetric_memory._nvshmem_triton as nvshmem

import triton
import triton.language as tl
Expand All @@ -9,6 +10,7 @@
from .._ptx_utils import get_flat_tid, send_signal



def _matmul_launch_metadata(grid, kernel, args):
ret = {}
M, N, K = args["M"], args["N"], args["K"]
Expand Down Expand Up @@ -105,17 +107,13 @@ def _gemm_producer_persistent_kernel(

offs_k = ki * BLOCK_SIZE_K

a = tl._experimental_descriptor_load(
a_desc_ptr, [offs_am, offs_k], [BLOCK_SIZE_M, BLOCK_SIZE_K], dtype
)
b = tl._experimental_descriptor_load(
b_desc_ptr, [offs_bn, offs_k], [BLOCK_SIZE_N, BLOCK_SIZE_K], dtype
)
a = a_desc_ptr.load([offs_am, offs_k])
b = b_desc_ptr.load([offs_bn, offs_k])
accumulator = tl.dot(a, b.T, accumulator)

if ki == k_tiles - 1:
c = accumulator.to(dtype)
tl._experimental_descriptor_store(c_desc_ptr, c, [offs_am, offs_bn])
c_desc_ptr.store([offs_am, offs_bn], c)

# calculate progress and send signals to corresponding ranks
scatter_start = offs_am // M_per_rank
Expand Down Expand Up @@ -194,29 +192,16 @@ def gemm_producer_w_progress(

bT = b.T

desc_a = _create_2d_tma_descriptor(
a.data_ptr(),
M,
K,
configs["BLOCK_SIZE_M"],
configs["BLOCK_SIZE_K"],
a.element_size(),
desc_a = triton.tools.tensor_descriptor.TensorDescriptor.from_tensor(
a, [configs["BLOCK_SIZE_M"], configs["BLOCK_SIZE_K"]]
)
desc_bt = _create_2d_tma_descriptor(
bT.data_ptr(),
N,
K,
configs["BLOCK_SIZE_N"],
configs["BLOCK_SIZE_K"],
bT.element_size(),
desc_bt = triton.tools.tensor_descriptor.TensorDescriptor.from_tensor(
bT,
[configs["BLOCK_SIZE_N"], configs["BLOCK_SIZE_K"]],
)
desc_c = _create_2d_tma_descriptor(
gemm_out.data_ptr(),
M,
N,
configs["BLOCK_SIZE_M"],
configs["BLOCK_SIZE_N"],
gemm_out.element_size(),
desc_c = triton.tools.tensor_descriptor.TensorDescriptor.from_tensor(
gemm_out,
[configs["BLOCK_SIZE_M"], configs["BLOCK_SIZE_N"]],
)

configs["NUM_SMS"] = torch.cuda.get_device_properties(
Expand Down Expand Up @@ -274,31 +259,20 @@ def _reduce_persistent_kernel(
tile_id_m = tile_id // num_tiles_n
tile_id_n = tile_id % num_tiles_n
cur_rank = (RANK + 1) % WORLD_SIZE
accum = tl._experimental_descriptor_load(
in_desc_ptr,
[
tile_id_m * BLOCK_SIZE_M + cur_rank * M_per_rank,
tile_id_n * BLOCK_SIZE_N,
],
[BLOCK_SIZE_M, BLOCK_SIZE_N],
tl.bfloat16,
accum = in_desc_ptr.load(
[tile_id_m * BLOCK_SIZE_M + cur_rank * M_per_rank, tile_id_n * BLOCK_SIZE_N]
)
for i in range(1, WORLD_SIZE):
cur_rank = (i + RANK + 1) % WORLD_SIZE
data = tl._experimental_descriptor_load(
in_desc_ptr,
data = in_desc_ptr.load(
[
tile_id_m * BLOCK_SIZE_M + cur_rank * M_per_rank,
tile_id_n * BLOCK_SIZE_N,
],
[BLOCK_SIZE_M, BLOCK_SIZE_N],
tl.bfloat16,
]
)
accum += data

tl._experimental_descriptor_store(
out_desc_ptr, accum, [tile_id_m * BLOCK_SIZE_M, tile_id_n * BLOCK_SIZE_N]
)
out_desc_ptr.store([tile_id_m * BLOCK_SIZE_M, tile_id_n * BLOCK_SIZE_N], accum)


def reduce(
Expand All @@ -312,22 +286,11 @@ def reduce(

BLOCK_SIZE_M = 256
BLOCK_SIZE_N = 64

in_desc_ptr = _create_2d_tma_descriptor(
inp.data_ptr(),
M,
N,
BLOCK_SIZE_M,
BLOCK_SIZE_N,
inp.element_size(),
in_desc_ptr = triton.tools.tensor_descriptor.TensorDescriptor.from_tensor(
inp, [BLOCK_SIZE_M, BLOCK_SIZE_N]
)
out_desc_ptr = _create_2d_tma_descriptor(
output.data_ptr(),
M_per_rank,
N,
BLOCK_SIZE_M,
BLOCK_SIZE_N,
output.element_size(),
out_desc_ptr = triton.tools.tensor_descriptor.TensorDescriptor.from_tensor(
output, [BLOCK_SIZE_M, BLOCK_SIZE_N]
)

grid = lambda META: ( # noqa: E731
Expand Down Expand Up @@ -359,6 +322,12 @@ def gemm_reduce_scatter_ce_persistent(
M = a.shape[0]
N = b.shape[1]


# 1. Initialize NVSHMEM device library
# nvshmem_lib = nvshmem.enable_triton()



group = dist.group.WORLD if group is None else group
gemm_out = torch.empty((M, N), dtype=a.dtype, device=a.device)
symm_mem_hdl = symm_mem.get_symm_mem_workspace(
Expand Down
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