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Add CuTeDSL MXFP8 M-groups scale rearrange kernel #4592

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201 changes: 201 additions & 0 deletions scripts/validate_m_groups_cutedsl.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,201 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD 3-Clause license found in the
# LICENSE file in the root directory of this source tree.

import argparse

import torch

from benchmarks.utils import benchmark_cuda_function_in_microseconds
from torchao.prototype.moe_training.kernels.mxfp8.cutedsl_rearrange_2d_m_groups import (
mx_block_rearrange_2d_m_groups_cutedsl,
)
from torchao.prototype.moe_training.kernels.mxfp8.quant import (
_mxfp8_cutedsl_kernels_available,
mx_block_rearrange_2d_M_groups_cuda,
triton_mx_block_rearrange_2d_M_groups,
)
from torchao.prototype.moe_training.utils import generate_jagged_offs


def benchmark_cuda_graph_function_in_microseconds(f, *args, iters=1000, **kwargs):
for _ in range(10):
f(*args, **kwargs)
torch.cuda.synchronize()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
f(*args, **kwargs)
for _ in range(10):
graph.replay()
torch.cuda.synchronize()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
for _ in range(iters):
graph.replay()
end.record()
torch.cuda.synchronize()
return start.elapsed_time(end) * 1000.0 / iters


def benchmark_function(f, *args, use_cuda_graph=False, graph_iters=1000, **kwargs):
if use_cuda_graph:
return benchmark_cuda_graph_function_in_microseconds(
f,
*args,
iters=graph_iters,
**kwargs,
)
return benchmark_cuda_function_in_microseconds(f, *args, **kwargs)


def main():
parser = argparse.ArgumentParser()
parser.add_argument("--rows", type=int, default=131072)
parser.add_argument("--cols", type=int, default=224)
parser.add_argument("--groups", type=int, default=8)
parser.add_argument("--multiple-of", type=int, default=128)
parser.add_argument(
"--chunk-width", type=int, default=0, choices=(0, 16, 32, 64, 128)
)
parser.add_argument(
"--bench-only",
choices=("cuda", "triton", "cutedsl"),
default=None,
)
parser.add_argument("--deterministic", action="store_true")
parser.add_argument("--cuda-graph-bench", action="store_true")
parser.add_argument("--graph-iters", type=int, default=1000)
args = parser.parse_args()

if not torch.cuda.is_available():
raise SystemExit("CUDA is not available")
if torch.cuda.get_device_capability()[0] != 10:
raise SystemExit(f"Requires SM 10.x; got {torch.cuda.get_device_capability()}")
if not _mxfp8_cutedsl_kernels_available:
raise SystemExit("CuteDSL mxfp8 kernels are unavailable")

if args.deterministic:
scales = (
torch.arange(args.rows * args.cols, device="cuda", dtype=torch.int32)
.remainder(251)
.to(torch.uint8)
.reshape(args.rows, args.cols)
.view(torch.float8_e8m0fnu)
)
else:
scales = torch.randint(
0,
255,
(args.rows, args.cols),
device="cuda",
dtype=torch.uint8,
).view(torch.float8_e8m0fnu)
scales_ref = scales.contiguous()
offsets = generate_jagged_offs(
args.groups,
args.rows,
multiple_of=args.multiple_of,
device="cuda",
dtype=torch.int32,
)

chunk_width = None if args.chunk_width == 0 else args.chunk_width

print(
f"shape={tuple(scales.shape)} strides={scales.stride()} groups={args.groups} chunk_width={chunk_width or 'auto'}"
)
if args.bench_only is None:
out_cuda = mx_block_rearrange_2d_M_groups_cuda(scales_ref, offsets)
out_triton = triton_mx_block_rearrange_2d_M_groups(scales_ref, offsets)
out_cutedsl = mx_block_rearrange_2d_m_groups_cutedsl(
scales,
offsets,
chunk_width=chunk_width,
)
torch.cuda.synchronize()

print(f"triton_equal={torch.equal(out_cuda, out_triton)}")
print(f"cutedsl_equal={torch.equal(out_cuda, out_cutedsl)}")
if not torch.equal(out_cuda, out_cutedsl):
diff = out_cuda.view(torch.uint8) != out_cutedsl.view(torch.uint8)
print(f"cutedsl_diff_count={diff.sum().item()}")
coords = diff.nonzero()[:16]
idx = coords[:, 0] * out_cuda.shape[1] + coords[:, 1]
cuda_flat = out_cuda.view(torch.uint8).flatten()
cutedsl_flat = out_cutedsl.view(torch.uint8).flatten()
print(f"first_diff_coords_v2={coords.cpu().tolist()}")
print(f"first_diff_flat_indices_v2={idx.cpu().tolist()}")
print(f"cuda_values={cuda_flat[idx].cpu().tolist()}")
print(f"cutedsl_values={cutedsl_flat[idx].cpu().tolist()}")
print(f"offsets={offsets.cpu().tolist()}")
if args.deterministic:
first_flat = int(idx[0].item())
sf_tile = first_flat // 512
tile_byte = first_flat % 512
sf_tiles_per_row = out_cuda.shape[1] // 4
row_chunk = sf_tile // sf_tiles_per_row
sf_col_tile = sf_tile % sf_tiles_per_row
local_col = sf_col_tile * 4
local_row = None
local_byte = None
for r in range(128):
off = (r % 32) * 16 + (r // 32) * 4
if off <= tile_byte < off + 4:
local_row = r
local_byte = tile_byte - off
break
offsets_cpu = offsets.cpu().tolist()
prev = 0
chunk_base = 0
input_row = None
for g, end in enumerate(offsets_cpu):
chunks = (end - prev + 127) // 128
if row_chunk < chunk_base + chunks:
input_row = prev + (row_chunk - chunk_base) * 128 + local_row
break
chunk_base += chunks
prev = end
print(
f"decoded_first_diff=row_chunk:{row_chunk} sf_col_tile:{sf_col_tile} local_row:{local_row} local_col:{local_col} local_byte:{local_byte} input_row:{input_row}"
)
if input_row is not None:
src = scales.view(torch.uint8)[
input_row, local_col : local_col + 16
]
print(f"decoded_source16={src.cpu().tolist()}")

if args.bench_only in (None, "cuda"):
cuda_us = benchmark_function(
mx_block_rearrange_2d_M_groups_cuda,
scales_ref,
offsets,
use_cuda_graph=args.cuda_graph_bench,
graph_iters=args.graph_iters,
)
print(f"cuda_us={cuda_us:.2f}")
if args.bench_only in (None, "triton"):
triton_us = benchmark_function(
triton_mx_block_rearrange_2d_M_groups,
scales_ref,
offsets,
use_cuda_graph=args.cuda_graph_bench,
graph_iters=args.graph_iters,
)
print(f"triton_us={triton_us:.2f}")
if args.bench_only in (None, "cutedsl"):
cutedsl_us = benchmark_function(
mx_block_rearrange_2d_m_groups_cutedsl,
scales,
offsets,
chunk_width,
use_cuda_graph=args.cuda_graph_bench,
graph_iters=args.graph_iters,
)
print(f"cutedsl_us={cutedsl_us:.2f}")


if __name__ == "__main__":
main()
63 changes: 62 additions & 1 deletion test/prototype/moe_training/test_kernels.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,9 @@ def _is_sm_10x() -> bool:
triton_mx_block_rearrange_2d_M_groups,
triton_mx_block_rearrange_per_group_3d,
)
from torchao.prototype.moe_training.kernels.mxfp8.cutedsl_rearrange_2d_m_groups import (
mx_block_rearrange_2d_m_groups_cutedsl,
)
from torchao.prototype.moe_training.kernels.mxfp8.quant import (
_mxfp8_cuda_kernels_available,
_mxfp8_cutedsl_kernels_available,
Expand All @@ -64,7 +67,7 @@ def _is_sm_10x() -> bool:
)
from torchao.prototype.mx_formats.kernels import triton_mx_block_rearrange
from torchao.prototype.mx_formats.mx_tensor import ScaleCalculationMode, to_mx
from torchao.prototype.mx_formats.utils import from_blocked
from torchao.prototype.mx_formats.utils import from_blocked, to_blocked
from torchao.testing.utils import skip_if_rocm


Expand Down Expand Up @@ -265,6 +268,64 @@ def test_triton_mx_block_rearrange_2d_M_groups(
)


@pytest.mark.skipif(
not _is_sm_10x(),
reason="MXFP8 requires CUDA SM 10.x",
)
@pytest.mark.skipif(
not _mxfp8_cutedsl_kernels_available,
reason="MXFP8 cutedsl kernels not available",
)
@skip_if_rocm("ROCm enablement in progress")
@pytest.mark.parametrize(
"scale_rows,scale_cols,n_groups",
[
(256, 37, 8),
(256, 45, 8),
(512, 64, 8),
(1024, 224, 8),
(1024, 512, 8),
(1024, 1025, 8),
],
)
def test_cutedsl_mx_block_rearrange_2d_M_groups(
scale_rows: int,
scale_cols: int,
n_groups: int,
):
device = "cuda"
block_size = 32
e8m0_scales = torch.randint(
0,
255,
(scale_rows, scale_cols),
device=device,
dtype=torch.uint8,
).view(torch.float8_e8m0fnu)
input_group_offsets = generate_jagged_offs(
n_groups, scale_rows, multiple_of=block_size, device=device
)

padded_cols = ((scale_cols + 3) // 4) * 4
ref_out_scales = e8m0_scales.new_zeros((scale_rows + n_groups * 128, padded_cols))
input_group_start = 0
output_group_start = 0
for input_group_end in input_group_offsets.tolist():
group_scales = e8m0_scales[input_group_start:input_group_end]
group_rows_padded = ((group_scales.shape[0] + 127) // 128) * 128
ref_out_scales[output_group_start : output_group_start + group_rows_padded] = (
to_blocked(group_scales).view(-1, padded_cols)
)
input_group_start = input_group_end
output_group_start += group_rows_padded

cutedsl_out_scales = mx_block_rearrange_2d_m_groups_cutedsl(
e8m0_scales,
input_group_offsets,
)
assert torch.equal(ref_out_scales, cutedsl_out_scales), "blocked scales not equal"


@pytest.mark.skipif(
not _is_sm_10x(),
reason="MXFP8 requires CUDA SM 10.x",
Expand Down
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