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[Kernel] Update cutlass_scaled_mm to support 2d group (blockwise) scaling (#11868)

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Lucas Wilkinson 2025-01-30 21:33:00 -05:00 committed by GitHub
parent 4078052f09
commit 9798b2fb00
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GPG Key ID: B5690EEEBB952194
25 changed files with 1924 additions and 346 deletions
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9
CMakeLists.txt
View File

@ -245,7 +245,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
FetchContent_Declare( FetchContent_Declare(
cutlass cutlass
GIT_REPOSITORY https://github.com/nvidia/cutlass.git GIT_REPOSITORY https://github.com/nvidia/cutlass.git
GIT_TAG v3.6.0 GIT_TAG v3.7.0
GIT_PROGRESS TRUE GIT_PROGRESS TRUE
# Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history. # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
@ -299,7 +299,12 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
# CUDA 12.0 or later (and only work on Hopper, 9.0a for now). # CUDA 12.0 or later (and only work on Hopper, 9.0a for now).
cuda_archs_loose_intersection(SCALED_MM_3X_ARCHS "9.0a" "${CUDA_ARCHS}") cuda_archs_loose_intersection(SCALED_MM_3X_ARCHS "9.0a" "${CUDA_ARCHS}")
if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS) if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS)
set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu") set(SRCS
"csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu"
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_int8.cu"
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_azp_sm90_int8.cu"
"csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm90_fp8.cu")
set_gencode_flags_for_srcs( set_gencode_flags_for_srcs(
SRCS "${SRCS}" SRCS "${SRCS}"
CUDA_ARCHS "${SCALED_MM_3X_ARCHS}") CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")

282
benchmarks/cutlass_benchmarks/w8a8_benchmarks.py
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@ -3,7 +3,7 @@ import copy
import itertools import itertools
import pickle as pkl import pickle as pkl
import time import time
from typing import Callable, Iterable, List, Tuple from typing import Callable, Iterable, List, Optional, Tuple
import torch import torch
import torch.utils.benchmark as TBenchmark import torch.utils.benchmark as TBenchmark
@ -12,6 +12,8 @@ from utils import make_rand_tensors
from weight_shapes import WEIGHT_SHAPES from weight_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
w8a8_block_fp8_matmul)
from vllm.utils import FlexibleArgumentParser from vllm.utils import FlexibleArgumentParser
DEFAULT_MODELS = list(WEIGHT_SHAPES.keys()) DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@ -38,8 +40,15 @@ def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
).blocked_autorange(min_run_time=min_run_time) ).blocked_autorange(min_run_time=min_run_time)
def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str, def bench_int8(
sub_label: str) -> Iterable[TMeasurement]: dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
"""Benchmark INT8-based kernels."""
assert dtype == torch.int8 assert dtype == torch.int8
a, b = make_rand_tensors(torch.int8, m, n, k) a, b = make_rand_tensors(torch.int8, m, n, k)
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32) scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
@ -48,155 +57,132 @@ def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
azp = torch.zeros((m, ), device="cuda", dtype=torch.int32) azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32) azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32)
bench_fns = {
"pytorch_bf16_bf16_bf16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
),
"pytorch_fp16_fp16_fp16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
"cutlass_i8_i8_bf16_scaled_mm":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
"cutlass_i8_i8_bf16_scaled_mm_bias":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
bias),
"cutlass_i8_i8_bf16_scaled_mm_azp":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj),
"cutlass_i8_i8_bf16_scaled_mm_azp_bias":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj, None, bias),
"cutlass_i8_i8_bf16_scaled_mm_azp_pt":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj, azp),
"cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj, azp, bias),
}
timers = [] timers = []
# pytorch impl - bfloat16 for name, fn in bench_fns.items():
timers.append( # If bench_kernels is None, run all. Otherwise, run only exact matches.
bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales", if bench_kernels is None or name in bench_kernels:
torch.mm, a.to(dtype=torch.bfloat16), print(f"Running {name}")
b.to(dtype=torch.bfloat16))) timers.append(bench_fn(label, sub_label, name, fn))
# pytorch impl - float16
timers.append(
bench_fn(label, sub_label,
"pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
# cutlass impl
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
torch.bfloat16))
# cutlass with bias
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
bias))
# cutlass with azp per-tensor
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp",
ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
torch.bfloat16, azp_adj))
# cutlass with azp per-tensor + bias
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_bias",
ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
torch.bfloat16, azp_adj, None, bias))
# cutlass with azp per-token
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_pt",
ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
torch.bfloat16, azp_adj, azp))
# cutlass with azp per-token + bias
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias",
ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
torch.bfloat16, azp_adj, azp, bias))
return timers return timers
def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str, def bench_fp8(
sub_label: str) -> Iterable[TMeasurement]: dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
"""Benchmark FP8-based kernels."""
assert dtype == torch.float8_e4m3fn assert dtype == torch.float8_e4m3fn
a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k) a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
a_cont = a.contiguous()
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32) scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32) scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
block_scale_a = torch.rand((m, k // 128),
device="cuda",
dtype=torch.float32)
block_scale_b = torch.rand((k // 128, n // 128),
device="cuda",
dtype=torch.float32)
block_scale_a_M_major = block_scale_a.t().contiguous().t()
block_scale_b_K_major = block_scale_b.t().contiguous().t()
bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16) bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
timers = [] print(m, k, n)
# pytorch impl w. bf16 bench_fns = {
timers.append( "pytorch_bf16_bf16_bf16_matmul-no-scales":
bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales", lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
torch.mm, a.to(dtype=torch.bfloat16, device="cuda"), ),
b.to(dtype=torch.bfloat16, device="cuda"))) "pytorch_fp16_fp16_fp16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
# pytorch impl: bf16 output, without fp8 fast accum "pytorch_fp8_fp8_fp16_scaled_mm":
timers.append( lambda: torch._scaled_mm(
bench_fn(label, a, b, scale_a, scale_b, out_dtype=torch.float16),
sub_label, "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum":
"pytorch_fp8_fp8_bf16_scaled_mm", lambda: torch._scaled_mm(a,
torch._scaled_mm,
a,
b, b,
scale_a=scale_a, scale_a,
scale_b=scale_b, scale_b,
out_dtype=torch.bfloat16))
# pytorch impl: bf16 output, with fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.bfloat16,
use_fast_accum=True))
# pytorch impl: fp16 output, without fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_fp16_scaled_mm",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.float16))
# pytorch impl: fp16 output, with fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.float16, out_dtype=torch.float16,
use_fast_accum=True)) use_fast_accum=True),
"pytorch_fp8_fp8_bf16_scaled_mm":
lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.bfloat16),
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum":
lambda: torch._scaled_mm(a,
b,
scale_a,
scale_b,
out_dtype=torch.bfloat16,
use_fast_accum=True),
"cutlass_fp8_fp8_bf16_scaled_mm":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
"cutlass_fp8_fp8_fp16_scaled_mm":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16),
"cutlass_fp8_fp8_bf16_scaled_mm_bias":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
bias),
"cutlass_fp8_fp8_fp16_scaled_mm_bias":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16,
bias.to(dtype=torch.float16)),
"triton_fp8_fp8_fp16_scaled_mm_blockwise":
lambda: w8a8_block_fp8_matmul(a_cont, b.t(), block_scale_a,
block_scale_b.t(), (128, 128)),
"cutlass_fp8_fp8_fp16_scaled_mm_blockwise":
lambda: ops.cutlass_scaled_mm(a, b, block_scale_a_M_major,
block_scale_b_K_major, torch.float16),
}
# cutlass impl: bf16 output timers = []
timers.append( for name, fn in bench_fns.items():
bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm", # If bench_kernels is None, run all. Otherwise, run only exact matches.
ops.cutlass_scaled_mm, a, b, scale_a, scale_b, if bench_kernels is None or name in bench_kernels:
torch.bfloat16)) print(f"Running {name}")
# cutlass impl: fp16 output timers.append(bench_fn(label, sub_label, name, fn))
timers.append(
bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_mm",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.float16))
# cutlass impl: bf16 output, with bias
timers.append(
bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm_bias",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
bias))
# cutlass impl: fp16 output, with bias
timers.append(
bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_mm_bias",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.float16,
bias.to(dtype=torch.float16)))
return timers return timers
def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str, def bench(dtype: torch.dtype,
sub_label: str) -> Iterable[TMeasurement]: m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
if dtype == torch.int8: if dtype == torch.int8:
return bench_int8(dtype, m, k, n, label, sub_label) return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
if dtype == torch.float8_e4m3fn: if dtype == torch.float8_e4m3fn:
return bench_fp8(dtype, m, k, n, label, sub_label) return bench_fp8(dtype, m, k, n, label, sub_label, bench_kernels)
raise ValueError("unsupported type") raise ValueError("unsupported type")
@ -207,18 +193,22 @@ def print_timers(timers: Iterable[TMeasurement]):
def run(dtype: torch.dtype, def run(dtype: torch.dtype,
MKNs: Iterable[Tuple[int, int, int]]) -> Iterable[TMeasurement]: MKNs: Iterable[Tuple[int, int, int]],
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
results = [] results = []
for m, k, n in MKNs: for m, k, n in MKNs:
timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm", timers = bench(dtype,
f"MKN=({m}x{k}x{n})") m,
k,
n,
f"scaled-{dtype}-gemm",
f"MKN=({m}x{k}x{n})",
bench_kernels=bench_kernels)
print_timers(timers) print_timers(timers)
results.extend(timers) results.extend(timers)
return results return results
# output makers
def make_output(data: Iterable[TMeasurement], def make_output(data: Iterable[TMeasurement],
MKNs: Iterable[Tuple[int, int, int]], MKNs: Iterable[Tuple[int, int, int]],
base_description: str, base_description: str,
@ -232,15 +222,11 @@ def make_output(data: Iterable[TMeasurement],
pkl.dump(data, f) pkl.dump(data, f)
# argparse runners
def run_square_bench(args): def run_square_bench(args):
dim_sizes = list( dim_sizes = list(
range(args.dim_start, args.dim_end + 1, args.dim_increment)) range(args.dim_start, args.dim_end + 1, args.dim_increment))
MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes)) MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
data = run(args.dtype, MKNs) data = run(args.dtype, MKNs, bench_kernels=args.kernels)
make_output(data, MKNs, f"square_bench-{args.dtype}") make_output(data, MKNs, f"square_bench-{args.dtype}")
@ -251,8 +237,7 @@ def run_range_bench(args):
Ks = [args.k_constant] * n if args.k_constant is not None else dim_sizes Ks = [args.k_constant] * n if args.k_constant is not None else dim_sizes
Ns = [args.n_constant] * n if args.n_constant is not None else dim_sizes Ns = [args.n_constant] * n if args.n_constant is not None else dim_sizes
MKNs = list(zip(Ms, Ks, Ns)) MKNs = list(zip(Ms, Ks, Ns))
data = run(args.dtype, MKNs) data = run(args.dtype, MKNs, bench_kernels=args.kernels)
make_output(data, MKNs, f"range_bench-{args.dtype}") make_output(data, MKNs, f"range_bench-{args.dtype}")
@ -278,7 +263,7 @@ def run_model_bench(args):
for k, n in KNs: for k, n in KNs:
MKNs.append((m, k, n)) MKNs.append((m, k, n))
data = run(args.dtype, MKNs) data = run(args.dtype, MKNs, bench_kernels=args.kernels)
model_bench_data.append(data) model_bench_data.append(data)
# Print all results # Print all results
@ -328,6 +313,15 @@ Benchmark Cutlass GEMM.
type=to_torch_dtype, type=to_torch_dtype,
required=True, required=True,
help="Available options are ['int8', 'fp8']") help="Available options are ['int8', 'fp8']")
parser.add_argument(
"--kernels",
nargs="+",
type=str,
default=None,
help=
"Exact names of the kernels to benchmark. If not set, runs all kernels."
)
subparsers = parser.add_subparsers(dest="cmd") subparsers = parser.add_subparsers(dest="cmd")
square_parser = subparsers.add_parser("square_bench") square_parser = subparsers.add_parser("square_bench")

9
csrc/core/math.hpp
View File

@ -1,7 +1,14 @@
#pragma once
#include <climits> #include <climits>
#include <iostream> #include <iostream>
inline uint32_t next_pow_2(uint32_t const num) { inline constexpr uint32_t next_pow_2(uint32_t const num) {
if (num <= 1) return num; if (num <= 1) return num;
return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1)); return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1));
} }
template <typename T>
inline constexpr std::enable_if_t<std::is_integral_v<T>, T> ceil_div(T a, T b) {
return (a + b - 1) / b;
}

17
csrc/cutlass_extensions/common.hpp
View File

@ -32,3 +32,20 @@ inline int get_cuda_max_shared_memory_per_block_opt_in(int const device) {
} }
int32_t get_sm_version_num(); int32_t get_sm_version_num();
/**
* A wrapper for a kernel that is used to guard against compilation on
* architectures that will never use the kernel. The purpose of this is to
* reduce the size of the compiled binary.
* __CUDA_ARCH__ is not defined in host code, so this lets us smuggle the ifdef
* into code that will be executed on the device where it is defined.
*/
template <typename Kernel>
struct enable_sm90_or_later : Kernel {
template <typename... Args>
CUTLASS_DEVICE void operator()(Args&&... args) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 900
Kernel::operator()(std::forward<Args>(args)...);
#endif
}
};

123
csrc/cutlass_extensions/gemm/collective/collective_builder.hpp Normal file
View File

@ -0,0 +1,123 @@
// Modified from: cutlass/gemm/collective/builders/sm90_gmma_builder.inl
// clang-format off
#pragma once
#include "cutlass/gemm/collective/builders/sm90_gmma_builder.inl"
#include "cutlass_extensions/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8_blockwise_scaling.hpp"
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace cutlass::gemm::collective {
/////////////////////////////////////////////////////////////////////////////////////////////////
// GMMA_TMA_WS_SS (BlockScaled Builders)
template <
class ElementA,
class GmemLayoutATag,
int AlignmentA,
class ElementB,
class GmemLayoutBTag,
int AlignmentB,
class ElementAccumulator,
class TileShape_MNK,
class ClusterShape_MNK,
class StageCountType,
int ScaleGranularityM
>
struct CollectiveBuilder<
arch::Sm90,
arch::OpClassTensorOp,
ElementA,
GmemLayoutATag,
AlignmentA,
ElementB,
GmemLayoutBTag,
AlignmentB,
ElementAccumulator,
TileShape_MNK,
ClusterShape_MNK,
StageCountType,
KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<ScaleGranularityM>,
cute::enable_if_t<
not detail::is_use_rmem_A<ElementA, GmemLayoutATag, ElementB, GmemLayoutBTag>()>
> {
using KernelScheduleType = KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<ScaleGranularityM>;
static_assert(is_static<TileShape_MNK>::value);
static_assert(is_static<ClusterShape_MNK>::value);
#ifndef CUTLASS_SM90_COLLECTIVE_BUILDER_SUPPORTED
static_assert(cutlass::detail::dependent_false<ElementA>, "Unsupported Toolkit for SM90 Collective Builder\n");
#endif
static_assert(detail::is_aligned<ElementA, AlignmentA, ElementB, AlignmentB, detail::tma_alignment_bytes>(),
"Should meet TMA alignment requirement\n");
static constexpr bool IsArrayOfPointersGemm = (cute::is_any_of_v<KernelScheduleType,
KernelPtrArrayTmaWarpSpecializedCooperative,
KernelPtrArrayTmaWarpSpecializedPingpong>);
static constexpr bool IsFP8Input = detail::is_input_fp8<ElementA, ElementB>();
static_assert((!IsFP8Input || !IsArrayOfPointersGemm),
"KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum is only compatible with FP8 Blocked Scaled version right now.");
// For fp32 types, map to tf32 MMA value type
using ElementAMma = cute::conditional_t<cute::is_same_v<ElementA, float>, tfloat32_t, ElementA>;
using ElementBMma = cute::conditional_t<cute::is_same_v<ElementB, float>, tfloat32_t, ElementB>;
static constexpr cute::GMMA::Major GmmaMajorA = detail::gmma_ss_tag_to_major_A<ElementAMma, GmemLayoutATag>();
static constexpr cute::GMMA::Major GmmaMajorB = detail::gmma_ss_tag_to_major_B<ElementBMma, GmemLayoutBTag>();
static constexpr bool IsCooperative = cute::is_any_of_v<KernelScheduleType,
KernelTmaWarpSpecializedCooperative,
KernelPtrArrayTmaWarpSpecializedCooperative,
KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<ScaleGranularityM>>;
using AtomLayoutMNK = cute::conditional_t<IsCooperative,
Layout<Shape<_2,_1,_1>>, Layout<Shape<_1,_1,_1>>>;
using TiledMma = decltype(cute::make_tiled_mma(cute::GMMA::ss_op_selector<
ElementAMma, ElementBMma, ElementAccumulator, TileShape_MNK, GmmaMajorA, GmmaMajorB>(), AtomLayoutMNK{}));
using GmemTiledCopyA = decltype(detail::sm90_cluster_shape_to_tma_atom(shape<1>(ClusterShape_MNK{})));
using GmemTiledCopyB = decltype(detail::sm90_cluster_shape_to_tma_atom(shape<0>(ClusterShape_MNK{})));
using SmemLayoutAtomA = decltype(detail::ss_smem_selector<
GmmaMajorA, ElementAMma, decltype(cute::get<0>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
using SmemLayoutAtomB = decltype(detail::ss_smem_selector<
GmmaMajorB, ElementBMma, decltype(cute::get<1>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
static constexpr size_t TensorMapStorage = IsArrayOfPointersGemm ? sizeof(cute::TmaDescriptor) * 2 /* for A and B */ : 0;
static constexpr int KernelSmemCarveout = static_cast<int>(TensorMapStorage);
static constexpr int PipelineStages = detail::compute_stage_count_or_override<detail::sm90_smem_capacity_bytes - KernelSmemCarveout,
ElementAMma, ElementBMma, TileShape_MNK>(StageCountType{});
using DispatchPolicy = MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8<PipelineStages, ClusterShape_MNK, KernelScheduleType, ScaleGranularityM>;
using SmemCopyAtomA = void;
using SmemCopyAtomB = void;
using CollectiveOp = CollectiveMma<
DispatchPolicy,
TileShape_MNK,
ElementA,
TagToStrideA_t<GmemLayoutATag>,
ElementB,
TagToStrideB_t<GmemLayoutBTag>,
TiledMma,
GmemTiledCopyA,
SmemLayoutAtomA,
SmemCopyAtomA,
cute::identity,
GmemTiledCopyB,
SmemLayoutAtomB,
SmemCopyAtomB,
cute::identity
>;
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass::gemm::collective
/////////////////////////////////////////////////////////////////////////////////////////////////

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// clang-format off
// adapted from: https://github.com/soundOfDestiny/cutlass/blob/a4208aa6958864923505cade9c63eb2a6daf16e5/include/cutlass/gemm/collective/fp8_accumulation.hpp
/***************************************************************************************************
* Copyright (c) 2023 - 2024 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.
*
**************************************************************************************************/
#pragma once
#include "cute/algorithm/clear.hpp"
#include "cute/tensor.hpp"
//////////////////////////////////////////////////////////////////////////////
///////////////////////////////////FP8 Accumulation///////////////////////////
//////////////////////////////////////////////////////////////////////////////
/// This class provides API to promote (add) or scale (multiply_add) the results
/// from the tensor core accumulators to the main accumulators when the number
/// of MMAs reaches the max number of MMA interval specified by user, after that
/// the tensor core accumulators are zeroed.
//////////////////////////////////////////////////////////////////////////////
namespace cutlass::gemm::collective {
template <
class EngineAccum,
class LayoutAccum>
struct GmmaFP8AccumulationWithScale {
using TensorAccum = cute::Tensor<EngineAccum, LayoutAccum>;
using ElementAccumulator = typename EngineAccum::value_type;
static_assert(is_static<LayoutAccum>::value, "Accumulator Layout should be static");
static_assert(is_rmem<TensorAccum>::value , "Accumulator tensor must be rmem resident.");
private:
TensorAccum& accum_;
TensorAccum accum_temp_;
uint32_t accum_promotion_interval_; // defines the max num of executed MMAs after which accum should be promoted.
uint32_t mma_count_per_mainloop_iteration_; // num of MMAs per k_tile of mainloop
uint32_t mma_count_; // current executed MMAs
uint32_t reset_accum_flag_; // accum needs to be zeroed or not.
// promote or `add` the partial accumulators to main accumulator (FADD).
CUTLASS_DEVICE
void promote_core() {
warpgroup_wait<0>();
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < size(accum_); ++i) {
accum_(i) += accum_temp_(i);
}
}
// `multiply` scale the partial accumulators and `add` to main accumulator (FFMA).
template <
class EngineScale,
class LayoutScale>
CUTLASS_DEVICE
void scale_core(const cute::Tensor<EngineScale, LayoutScale> &scale) {
using TensorScale = cute::Tensor<EngineScale, LayoutScale>;
static_assert(is_static<LayoutScale>::value, "Scale Layout should be static");
static_assert(is_rmem<TensorScale>::value , "Scale tensor must be rmem resident.");
static_assert(LayoutAccum{}.shape() == LayoutScale{}.shape(), "Accumulator and scale must have same shape.");
warpgroup_wait<0>();
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < size(accum_); ++i) {
accum_(i) += accum_temp_(i) * scale(i);
}
}
public:
CUTLASS_DEVICE
GmmaFP8AccumulationWithScale(
TensorAccum &accum,
uint32_t accum_promotion_interval,
uint32_t mma_count_per_mainloop_iteration)
: accum_(accum),
accum_promotion_interval_(accum_promotion_interval),
mma_count_per_mainloop_iteration_(mma_count_per_mainloop_iteration),
mma_count_(0),
reset_accum_flag_(0)
{
accum_temp_ = cute::make_fragment_like(accum);
}
//
// Methods (Common)
//
CUTLASS_DEVICE
TensorAccum& operator()() {
return accum_temp_;
}
/// prepare the MMA accumulators when initialization or zeroing is required.
CUTLASS_DEVICE
bool prepare_if_needed() {
return reset_accum_flag_;
}
//
// Methods (for FADD version)
//
/// promote (add) the results from the MMA accumulators to main accumulator if needed.
CUTLASS_DEVICE
void promote_if_needed() {
mma_count_ += mma_count_per_mainloop_iteration_;
reset_accum_flag_ = __shfl_sync(0xffffffff, mma_count_ == accum_promotion_interval_, 0);
if (reset_accum_flag_) {
promote_core();
mma_count_ = 0;
}
}
/// promote (add) the residue results from the MMA accumulators to main accumulator if needed.
CUTLASS_DEVICE
void promote_residue_if_needed() {
if (__shfl_sync(0xffffffff, mma_count_ > 0, 0)) {
promote_core();
}
}
//
// Methods (for FFMA version)
//
/// scale (multiply_add) the results from the MMA accumulators to main accumulator if needed.
template <
class EngineScale,
class LayoutScale>
CUTLASS_DEVICE
void scale_if_needed(const cute::Tensor<EngineScale, LayoutScale> &scale) {
mma_count_ += mma_count_per_mainloop_iteration_;
reset_accum_flag_ = __shfl_sync(0xffffffff, mma_count_ == accum_promotion_interval_, 0);
if (reset_accum_flag_) {
scale_core(scale);
mma_count_ = 0;
}
}
/// scale (multiply_add) the residue results from the MMA accumulators to main accumulator if needed.
template <
class EngineScale,
class LayoutScale>
CUTLASS_DEVICE
void scale_residue_if_needed(const cute::Tensor<EngineScale, LayoutScale> &scale) {
if (__shfl_sync(0xffffffff, mma_count_ > 0, 0)) {
scale_core(scale);
}
}
};
} // namespace cutlass::gemm::collective

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// clang-format off
// Adapted (Heavily) from: https://github.com/soundOfDestiny/cutlass/blob/9d997ce0dea4c5fa1a617db6b7ff29aa9235822c/include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8_blockwise_scaling.hpp
/***************************************************************************************************
* Copyright (c) 2023 - 2024 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.
*
**************************************************************************************************/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/trace.h"
#include "cutlass/numeric_types.h"
#include "cute/arch/cluster_sm90.hpp"
#include "cute/arch/copy_sm80.hpp"
#include "cute/arch/copy_sm90.hpp"
#include "cute/algorithm/functional.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cute/algorithm/gemm.hpp"
#include "cute/tensor_predicate.hpp"
#include "cute/numeric/arithmetic_tuple.hpp"
#include "cutlass_extensions/gemm/dispatch_policy.hpp"
#include "cutlass_extensions/gemm/collective/fp8_accumulation.hpp"
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace cutlass::gemm::collective {
using namespace cute;
/////////////////////////////////////////////////////////////////////////////////////////////////
// WarpSpecialized Mainloop
template <
int Stages,
class ClusterShape,
class KernelSchedule,
int ScaleGranularityM_,
class TileShape_,
class ElementA_,
class StrideA_,
class ElementB_,
class StrideB_,
class TiledMma_,
class GmemTiledCopyA_,
class SmemLayoutAtomA_,
class SmemCopyAtomA_,
class TransformA_,
class GmemTiledCopyB_,
class SmemLayoutAtomB_,
class SmemCopyAtomB_,
class TransformB_>
struct CollectiveMma<
MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8<Stages, ClusterShape, KernelSchedule, ScaleGranularityM_>,
TileShape_,
ElementA_,
StrideA_,
ElementB_,
StrideB_,
TiledMma_,
GmemTiledCopyA_,
SmemLayoutAtomA_,
SmemCopyAtomA_,
TransformA_,
GmemTiledCopyB_,
SmemLayoutAtomB_,
SmemCopyAtomB_,
TransformB_>
{
//
// Type Aliases
//
using DispatchPolicy = MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8<Stages, ClusterShape, KernelSchedule, ScaleGranularityM_>;
using TileShape = TileShape_;
using ElementA = ElementA_;
using StrideA = StrideA_;
using ElementB = ElementB_;
using StrideB = StrideB_;
using TiledMma = TiledMma_;
using ElementAccumulator = typename TiledMma::ValTypeC;
using ElementBlockScale = ElementAccumulator;
using GmemTiledCopyA = GmemTiledCopyA_;
using GmemTiledCopyB = GmemTiledCopyB_;
using SmemLayoutAtomA = SmemLayoutAtomA_;
using SmemLayoutAtomB = SmemLayoutAtomB_;
using SmemCopyAtomA = SmemCopyAtomA_;
using SmemCopyAtomB = SmemCopyAtomB_;
using TransformA = TransformA_;
using TransformB = TransformB_;
using ArchTag = typename DispatchPolicy::ArchTag;
using CtaShape_MNK = decltype(shape_div(TileShape{}, ClusterShape{}));
using MainloopPipeline = cutlass::PipelineTmaAsync<DispatchPolicy::Stages>;
using PipelineState = cutlass::PipelineState<DispatchPolicy::Stages>;
using PipelineParams = typename MainloopPipeline::Params;
// Two threads per CTA are producers (1 for operand tile and 32 for scales)
static constexpr int NumProducerThreadEvents = 33;
static constexpr int ScaleGranularityM = ScaleGranularityM_ == 0 ? size<0>(TileShape{}) : ScaleGranularityM_;
static constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
static_assert(cute::rank(SmemLayoutAtomA{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
static_assert((size<0>(TileShape{}) % size<0>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
static_assert(cute::rank(SmemLayoutAtomB{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
static_assert((size<1>(TileShape{}) % size<0>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
static_assert((size<0>(TileShape{}) % ScaleGranularityM) == 0, "FP8 scaling granularity must evenly divide tile shape along M.");
// Tile along modes in a way that maximizes the TMA box size.
using SmemLayoutA = decltype(tile_to_shape(
SmemLayoutAtomA{},
make_shape(shape<0>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{}),
cute::conditional_t< ::cutlass::gemm::detail::is_major<0,StrideA>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
using SmemLayoutB = decltype(tile_to_shape(
SmemLayoutAtomB{},
make_shape(shape<1>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{}),
cute::conditional_t< ::cutlass::gemm::detail::is_major<0,StrideB>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
// Block scaling gmem-to-smem copy atom
using SmemBlockScalingCopyAtomA = Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<ElementBlockScale>, ElementBlockScale>;
using SmemBlockScalingCopyAtomB = Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<ElementBlockScale>, ElementBlockScale>;
// Block scaling smem layout
using SmemLayoutScaleA = Layout<Shape<Int<ScaleMsPerTile>, Int<DispatchPolicy::Stages>>>;
using SmemLayoutScaleB = Layout<Shape<Int<DispatchPolicy::Stages>>, Stride<_1>>; // `ScaleNsPerTile` is always 1.
static_assert(DispatchPolicy::Stages >= 2, "Specialization requires Stages set to value 1 or more.");
static_assert(cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value &&
cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeB>::value,
"MMA atom must source both A and B operand from smem_desc for this mainloop.");
static_assert(cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>,
"GmemTiledCopy - invalid SM90 TMA copy atom specified.");
static_assert(cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>,
"GmemTiledCopy - invalid SM90 TMA copy atom specified.");
static_assert(cute::is_same_v<ElementAccumulator, ElementBlockScale>,
"ElementAccumulator and ElementBlockScale should be same datatype");
struct SharedStorage
{
struct TensorStorage : cute::aligned_struct<128> {
cute::array_aligned<typename TiledMma::ValTypeA, cute::cosize_v<SmemLayoutA>> smem_A; // mxk
cute::array_aligned<typename TiledMma::ValTypeB, cute::cosize_v<SmemLayoutB>> smem_B; // nxk
cute::array_aligned<ElementBlockScale, cute::cosize_v<SmemLayoutScaleA>> smem_scale_A; // ScaleMsPerTile x k
cute::array_aligned<ElementBlockScale, cute::cosize_v<SmemLayoutScaleB>> smem_scale_B; // 1xk
} tensors;
using PipelineStorage = typename MainloopPipeline::SharedStorage;
PipelineStorage pipeline;
};
using TensorStorage = typename SharedStorage::TensorStorage;
using PipelineStorage = typename SharedStorage::PipelineStorage;
// Host side kernel arguments
struct Arguments {
ElementA const* ptr_A;
StrideA dA;
ElementB const* ptr_B;
StrideB dB;
ElementBlockScale const* ptr_scale_A;
ElementBlockScale const* ptr_scale_B;
};
// Device side kernel params
struct Params {
// Assumption: StrideA is congruent with Problem_MK
using TMA_A = decltype(make_tma_copy_A_sm90(
GmemTiledCopyA{},
make_tensor(static_cast<ElementA const*>(nullptr), repeat_like(StrideA{}, int32_t(0)), StrideA{}),
SmemLayoutA{}(_,_,0),
TileShape{},
ClusterShape{}));
// Assumption: StrideB is congruent with Problem_NK
using TMA_B = decltype(make_tma_copy_B_sm90(
GmemTiledCopyB{},
make_tensor(static_cast<ElementB const*>(nullptr), repeat_like(StrideB{}, int32_t(0)), StrideB{}),
SmemLayoutB{}(_,_,0),
TileShape{},
ClusterShape{}));
TMA_A tma_load_a;
TMA_B tma_load_b;
uint32_t tma_transaction_bytes = TmaTransactionBytes;
uint32_t tma_transaction_bytes_mk = TmaTransactionBytesMK;
uint32_t tma_transaction_bytes_nk = TmaTransactionBytesNK;
// Block scaling factors for A and B
ElementBlockScale const* ptr_scale_A;
ElementBlockScale const* ptr_scale_B;
};
//
// Methods
//
template <class ProblemShape>
static constexpr Params
to_underlying_arguments(ProblemShape const& problem_shape, Arguments const& args, void* workspace) {
(void) workspace;
// Optionally append 1s until problem shape is rank-4 (MNKL), in case it is only rank-3 (MNK)
auto problem_shape_MNKL = append<4>(problem_shape, 1);
auto [M,N,K,L] = problem_shape_MNKL;
auto ptr_A = reinterpret_cast<ElementA const*>(args.ptr_A);
auto ptr_B = reinterpret_cast<ElementB const*>(args.ptr_B);
Tensor tensor_a = make_tensor(ptr_A, make_layout(make_shape(M,K,L), args.dA));
Tensor tensor_b = make_tensor(ptr_B, make_layout(make_shape(N,K,L), args.dB));
typename Params::TMA_A tma_load_a = make_tma_copy_A_sm90(
GmemTiledCopyA{},
tensor_a,
SmemLayoutA{}(_,_,cute::Int<0>{}),
TileShape{},
ClusterShape{});
typename Params::TMA_B tma_load_b = make_tma_copy_B_sm90(
GmemTiledCopyB{},
tensor_b,
SmemLayoutB{}(_,_,cute::Int<0>{}),
TileShape{},
ClusterShape{});
uint32_t transaction_bytes_mk = TmaTransactionBytesMK;
uint32_t transaction_bytes_nk = TmaTransactionBytesNK;
uint32_t transaction_bytes = transaction_bytes_mk + transaction_bytes_nk;
return {
tma_load_a,
tma_load_b,
transaction_bytes,
transaction_bytes_mk,
transaction_bytes_nk,
args.ptr_scale_A,
args.ptr_scale_B
};
}
template<class ProblemShape>
static bool
can_implement(
ProblemShape const& problem_shape,
[[maybe_unused]] Arguments const& args) {
constexpr int tma_alignment_bits = 128;
auto problem_shape_MNKL = append<4>(problem_shape, 1);
auto [M,N,K,L] = problem_shape_MNKL;
bool implementable = true;
constexpr int min_tma_aligned_elements_A = tma_alignment_bits / cutlass::sizeof_bits<ElementA>::value;
implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), StrideA{});
constexpr int min_tma_aligned_elements_B = tma_alignment_bits / cutlass::sizeof_bits<ElementB>::value;
implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_B>(cute::make_shape(N,K,L), StrideB{});
if (!implementable) {
CUTLASS_TRACE_HOST(" CAN IMPLEMENT: Problem Size doesn't meet the minimum alignment requirements for TMA.\n");
}
return implementable;
}
static constexpr int K_PIPE_MAX = DispatchPolicy::Stages;
static constexpr int K_PIPE_MMAS = 1;
static constexpr uint32_t TmaTransactionBytesMK =
cutlass::bits_to_bytes(size<0>(SmemLayoutA{}) * size<1>(SmemLayoutA{}) * static_cast<uint32_t>(sizeof_bits<ElementA>::value));
static constexpr uint32_t TmaTransactionBytesNK =
cutlass::bits_to_bytes(size<0>(SmemLayoutB{}) * size<1>(SmemLayoutB{}) * static_cast<uint32_t>(sizeof_bits<ElementB>::value));
static constexpr uint32_t TmaTransactionBytes = TmaTransactionBytesMK + TmaTransactionBytesNK;
/// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance
CUTLASS_DEVICE
static void prefetch_tma_descriptors(Params const& mainloop_params)
{
cute::prefetch_tma_descriptor(mainloop_params.tma_load_a.get_tma_descriptor());
cute::prefetch_tma_descriptor(mainloop_params.tma_load_b.get_tma_descriptor());
}
/// Set up the data needed by this collective for load and mma.
/// Returns a tuple of tensors. The collective and the kernel layer have the contract
/// Returned tuple must contain at least two elements, with the first two elements being:
/// gA_mkl - The tma tensor, A after a local tile so it has shape (BLK_M,BLK_K,m,k,l)
/// gB_nkl - The tma tensor, B after a local tile so it has shape (BLK_N,BLK_K,n,k,l)
template <class ProblemShape_MNKL>
CUTLASS_DEVICE auto
load_init(ProblemShape_MNKL const& problem_shape_MNKL, Params const& mainloop_params) const {
using X = Underscore;
// Separate out problem shape for convenience
auto [M,N,K,L] = problem_shape_MNKL;
// TMA requires special handling of strides to deal with coord codomain mapping
// Represent the full tensors -- get these from TMA
Tensor mA_mkl = mainloop_params.tma_load_a.get_tma_tensor(make_shape(M,K,L)); // (m,k,l)
Tensor mB_nkl = mainloop_params.tma_load_b.get_tma_tensor(make_shape(N,K,L)); // (n,k,l)
// Make tiled views, defer the slice
Tensor gA_mkl = local_tile(mA_mkl, TileShape{}, make_coord(_,_,_), Step<_1, X,_1>{}); // (BLK_M,BLK_K,m,k,l)
Tensor gB_nkl = local_tile(mB_nkl, TileShape{}, make_coord(_,_,_), Step< X,_1,_1>{}); // (BLK_N,BLK_K,n,k,l)
constexpr auto scales_m = Int<ScaleMsPerTile>{};
auto tM = get<2>(gA_mkl.shape());
auto tN = get<2>(gB_nkl.shape());
auto tK = get<3>(gA_mkl.shape());
// Make the tiled views of scale tensors
auto scaleA_shape = make_shape(M / ScaleGranularityM, tK, L); // (scale_m,k,l)
auto scaleA_layout = make_ordered_layout(scaleA_shape, Step<_0, _1, _2>{});
auto scaleB_shape = make_shape(tN, tK, L); // (n,k,l)
auto scaleB_layout = make_ordered_layout(scaleB_shape, Step<_1, _0, _2>{});
// Note that mScaleA_mkl and mScaleB_nkl are already blocked tiled in the `m` host and
// gScaleA_mkl and gScaleB_nkl in `g` global memory are same as mScaleA_mkl and mScaleB_nkl.
Tensor mScaleA_mkl = make_tensor(make_gmem_ptr(mainloop_params.ptr_scale_A), scaleA_layout); // (scale_m,k,l)
Tensor mScaleB_nkl = make_tensor(make_gmem_ptr(mainloop_params.ptr_scale_B), scaleB_layout); // (n,k,l)
return cute::make_tuple(gA_mkl, gB_nkl, mScaleA_mkl, mScaleB_nkl);
}
/// Perform a collective-scoped matrix multiply-accumulate
/// Producer Perspective
template <
class TensorA, class TensorB,
class TensorScaleA, class TensorScaleB,
class KTileIterator, class BlockCoord
>
CUTLASS_DEVICE void
load(
Params const& mainloop_params,
MainloopPipeline pipeline,
PipelineState smem_pipe_write,
cute::tuple<TensorA, TensorB, TensorScaleA, TensorScaleB> const& load_inputs,
BlockCoord const& blk_coord,
KTileIterator k_tile_iter, int k_tile_count,
int thread_idx,
uint32_t block_rank_in_cluster,
TensorStorage& shared_tensors) {
int lane_predicate = cute::elect_one_sync();
// Blockscaling: Tma loads for load_input and CpAsync for load_scale
Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{}); // (BLK_M,BLK_K,PIPE)
Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), SmemLayoutB{}); // (BLK_N,BLK_K,PIPE)
Tensor sScaleA = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_A.data()), SmemLayoutScaleA{}); // (ScaleMsPerTile,k)
Tensor sScaleB = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_B.data()), SmemLayoutScaleB{}); // (k)
//
// Prepare the TMA loads for A and B
//
constexpr uint32_t cluster_shape_x = get<0>(ClusterShape());
uint2 cluster_local_block_id = {block_rank_in_cluster % cluster_shape_x, block_rank_in_cluster / cluster_shape_x};
Tensor gA_mkl = get<0>(load_inputs);
Tensor gB_nkl = get<1>(load_inputs);
auto block_tma_a = mainloop_params.tma_load_a.get_slice(cluster_local_block_id.y);
auto block_tma_b = mainloop_params.tma_load_b.get_slice(cluster_local_block_id.x);
// Partition the inputs based on the current block coordinates.
auto [m_coord, n_coord, k_coord, l_coord] = blk_coord;
Tensor gA = gA_mkl(_,_,m_coord,_,l_coord); // (BLK_M,BLK_K,k)
Tensor gB = gB_nkl(_,_,n_coord,_,l_coord); // (BLK_N,BLK_K,k)
// Block scaling: load_scale has scaling tensors in global memory which are not tiled
Tensor mScaleA_mkl = get<2>(load_inputs);
Tensor mScaleB_nkl = get<3>(load_inputs);
auto scales_m = get<0>(mScaleA_mkl.shape());
Tensor cScaleA_mkl = make_identity_tensor(mScaleA_mkl.shape());
Tensor gScaleA = local_tile(
mScaleA_mkl, make_tile(Int<ScaleMsPerTile>{}),
make_coord(m_coord,_,l_coord)); // (ScaleMsPerTile,k,1)
Tensor cScaleA = local_tile(
cScaleA_mkl, make_tile(Int<ScaleMsPerTile>{}),
make_coord(m_coord,_,l_coord));
Tensor gScaleB = mScaleB_nkl(n_coord,_,l_coord); // (1,k,1)
// TODO: test `scale_copy_a` with `ScaleMsPerTile` < 128
TiledCopy scale_copy_a = make_tiled_copy(SmemBlockScalingCopyAtomA{},
Layout<Shape<_32, _1>>{}, Layout<Shape<_4, _1>>{}); // (1,1,1)
TiledCopy scale_copy_b = make_tiled_copy(SmemBlockScalingCopyAtomB{},
Layout<Shape<_1>>{}, Layout<Shape<_1>>{}); // (1,1,1)
ThrCopy thr_scale_copy_a = scale_copy_a.get_slice(threadIdx.x);
ThrCopy thr_scale_copy_b = scale_copy_b.get_slice(threadIdx.x);
Tensor tAgA_ScaleA = thr_scale_copy_a.partition_S(gScaleA);
Tensor tAcA_ScaleA = thr_scale_copy_a.partition_S(cScaleA);
Tensor tAsA_ScaleA = thr_scale_copy_a.partition_D(sScaleA);
Tensor tBgB_ScaleB = thr_scale_copy_b.partition_S(gScaleB);
Tensor tBsB_ScaleB = thr_scale_copy_b.partition_D(sScaleB);
// Applies the mapping from block_tma_a
Tensor tAgA = block_tma_a.partition_S(gA); // (TMA,TMA_M,TMA_K,k)
Tensor tAsA = block_tma_a.partition_D(sA); // (TMA,TMA_M,TMA_K,PIPE)
Tensor tBgB = block_tma_b.partition_S(gB); // (TMA,TMA_N,TMA_K,k)
Tensor tBsB = block_tma_b.partition_D(sB); // (TMA,TMA_N,TMA_K,PIPE)
uint16_t mcast_mask_a = 0;
uint16_t mcast_mask_b = 0;
// Issue TmaLoads for GEMM operands A/B and CpAsync for scale tensors
// Maps the tile -> block, value
if constexpr (cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>) {
auto block_layout = Layout<typename DispatchPolicy::ClusterShape>{}; // (m,n) -> block_id
for (int n = 0; n < size<1>(block_layout); ++n) {
mcast_mask_a |= (uint16_t(1) << block_layout(cluster_local_block_id.x,n,Int<0>{}));
}
}
if constexpr (cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>) {
auto block_layout = Layout<typename DispatchPolicy::ClusterShape>{}; // (m,n) -> block_id
for (int m = 0; m < size<0>(block_layout); ++m) {
mcast_mask_b |= (uint16_t(1) << block_layout(m,cluster_local_block_id.y,Int<0>{}));
}
}
// Allocate predicate tensors for a_scales (since we can't guarantee that
// all scales are valid, since we could have a partial tiles along M)
Tensor tApA_ScaleA = make_tensor<bool>(shape(tAsA_ScaleA(_,_,0)));
#pragma unroll
for (int i = 0; i < size(tApA_ScaleA); ++i) {
tApA_ScaleA(i) = get<0>(tAcA_ScaleA(i)) < scales_m;
}
// Mainloop
CUTLASS_PRAGMA_NO_UNROLL
for ( ; k_tile_count > 0; --k_tile_count) {
// LOCK smem_pipe_write for _writing_
pipeline.producer_acquire(smem_pipe_write);
//
// Copy gmem to smem for *k_tile_iter
//
int write_stage = smem_pipe_write.index();
using BarrierType = typename MainloopPipeline::ProducerBarrierType;
BarrierType* tma_barrier = pipeline.producer_get_barrier(smem_pipe_write);
// Copy operands A and B from global memory to shared memory
if (lane_predicate) copy(mainloop_params.tma_load_a.with(*tma_barrier, mcast_mask_a), tAgA(_,_,_,*k_tile_iter), tAsA(_,_,_,write_stage));
if (lane_predicate) copy(mainloop_params.tma_load_b.with(*tma_barrier, mcast_mask_b), tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage));
// Copy scale tensors from global memory to shared memory
copy_if(scale_copy_a, tApA_ScaleA, tAgA_ScaleA(_,_,*k_tile_iter), tAsA_ScaleA(_,_,write_stage));
copy(scale_copy_b, tBgB_ScaleB(_,*k_tile_iter), tBsB_ScaleB(_,write_stage));
pipeline.producer_commit(smem_pipe_write, cutlass::arch::cpasync_barrier_arrive_noinc);
++k_tile_iter;
// Advance smem_pipe_write
++smem_pipe_write;
}
}
/// Perform a Producer Epilogue to prevent early exit of blocks in a Cluster
CUTLASS_DEVICE void
load_tail(
MainloopPipeline pipeline,
PipelineState smem_pipe_write) {
int lane_predicate = cute::elect_one_sync();
// Issue the epilogue waits
if (lane_predicate) {
/* This helps avoid early exit of blocks in Cluster
* Waits for all stages to either be released (all
* Consumer UNLOCKs), or if the stage was never used
* then would just be acquired since the phase was
* still inverted from make_producer_start_state
*/
pipeline.producer_tail(smem_pipe_write);
}
}
/// Perform a collective-scoped matrix multiply-accumulate
/// Consumer Perspective
template <
class FrgTensorC
>
CUTLASS_DEVICE void
mma(MainloopPipeline pipeline,
PipelineState smem_pipe_read,
FrgTensorC& accum,
int k_tile_count,
int thread_idx,
TensorStorage& shared_tensors,
Params const& mainloop_params) {
static_assert(is_rmem<FrgTensorC>::value, "C tensor must be rmem resident.");
static_assert(cute::rank(SmemLayoutA{}) == 3, "Smem layout must be rank 3.");
static_assert(cute::rank(SmemLayoutB{}) == 3, "Smem layout must be rank 3.");
static_assert(cute::is_void_v<SmemCopyAtomA>,
"SM90 GMMA mainloops cannot have a non-void copy atom for smem sourced instructions.");
static_assert(cute::is_void_v<SmemCopyAtomB>,
"SM90 GMMA mainloops cannot have a non-void copy atom for smem sourced instructions.");
Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{}); // (BLK_M,BLK_K,PIPE)
Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), SmemLayoutB{}); // (BLK_N,BLK_K,PIPE)
// Block scaling
Tensor sScaleAViewAsC = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_A.data()),
Layout<
Shape<Shape<Int<ScaleGranularityM>, Int<ScaleMsPerTile>>, cute::tuple_element_t<1, TileShape>, Int<DispatchPolicy::Stages>>,
Stride<Stride<_0, _1>, _0, Int<ScaleMsPerTile>>
>{}); // ((ScaleGranularityM,ScaleMsPerTile),n,k)
Tensor sScaleB = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_B.data()), SmemLayoutScaleB{}); // (k)
//
// Define C accumulators and A/B partitioning
//
// Layout of warp group to thread mapping
static_assert(stride<0>(typename TiledMma::ALayout{}) == 0 and
stride<0>(typename TiledMma::BLayout{}) == 0 and
size<0>(typename TiledMma::ALayout{}) == NumThreadsPerWarpGroup and
size<0>(typename TiledMma::BLayout{}) == NumThreadsPerWarpGroup,
"Stride of the first mode must be 0 and the size of the mode must be NumThreadsPerWarpGroup");
constexpr int MmaWarpGroups = size(TiledMma{}) / NumThreadsPerWarpGroup;
Layout warp_group_thread_layout = make_layout(Int<MmaWarpGroups>{},
Int<NumThreadsPerWarpGroup>{});
int warp_group_idx = __shfl_sync(0xFFFFFFFF, thread_idx / NumThreadsPerWarpGroup, 0);
TiledMma tiled_mma;
auto thread_mma = tiled_mma.get_slice(warp_group_thread_layout(warp_group_idx));
Tensor tCsScaleAViewAsC = tiled_mma.get_slice(thread_idx).partition_C(sScaleAViewAsC); // (MMA,MMA_M,MMA_N,PIPE), `thread_mma` above is correct when partitioning A and B, but it is not correct when partitioning C.
Tensor tCsA = thread_mma.partition_A(sA); // (MMA,MMA_M,MMA_K,PIPE)
Tensor tCsB = thread_mma.partition_B(sB); // (MMA,MMA_N,MMA_K,PIPE)
// Allocate "fragments/descriptors"
Tensor tCrA = thread_mma.make_fragment_A(tCsA); // (MMA,MMA_M,MMA_K,PIPE)
Tensor tCrB = thread_mma.make_fragment_B(tCsB); // (MMA,MMA_N,MMA_K,PIPE)
CUTE_STATIC_ASSERT_V(size<1>(tCsA) == size<1>(accum)); // M
CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<2>(accum)); // N
CUTE_STATIC_ASSERT_V(size<2>(tCsA) == size<2>(tCsB)); // K
CUTE_STATIC_ASSERT_V(size<3>(tCsA) == size<3>(tCsB)); // PIPE
CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<2>(sA)); // PIPE
CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<2>(sB)); // PIPE
//
// PIPELINED MAIN LOOP
//
static_assert((0 <= K_PIPE_MMAS) && (K_PIPE_MMAS < K_PIPE_MAX),
"ERROR : Incorrect number of MMAs in flight");
// We release buffers to producer warps(dma load) with some mmas in flight
PipelineState smem_pipe_release = smem_pipe_read;
// Per block scale values for operand A and B
using RegLayoutScaleAViewAsC = decltype(make_layout_like(tCsScaleAViewAsC(_, _, _, 0).layout())); // `make_layout_like` makes a compact layout.
using RegLayoutScaleAEssential = decltype(filter_zeros(RegLayoutScaleAViewAsC{}.stride(), RegLayoutScaleAViewAsC{}.shape())); // an interface to traverse the underlying storage for the compact layout mentioned above
Tensor tCrScaleAViewAsC = make_tensor<ElementBlockScale>(RegLayoutScaleAViewAsC{}); // (MMA,MMA_M,MMA_N)
ElementBlockScale scale_b;
// Prologue GMMAs
int prologue_mma_count = min(K_PIPE_MMAS, k_tile_count);
tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
GmmaFP8AccumulationWithScale accumulation(accum, size<2>(TileShape{}) / size<2>(typename TiledMma::AtomShape_MNK{}), size<2>(tCrA));
warpgroup_fence_operand(accumulation());
CUTLASS_PRAGMA_UNROLL
for (int k_tile_prologue = prologue_mma_count; k_tile_prologue > 0; --k_tile_prologue)
{
// WAIT on smem_pipe_read until its data are available (phase bit flips from rdPhaseBit value)
auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
pipeline.consumer_wait(smem_pipe_read, barrier_token);
if (accumulation.prepare_if_needed()) {
tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
}
int read_stage = smem_pipe_read.index();
// Load per block scale values from shared memory to registers.
scale_b = sScaleB[read_stage];
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
tCrScaleAViewAsC.data()[i] = tCsScaleAViewAsC(_, _, _, read_stage)(idx2crd(i, RegLayoutScaleAEssential{}));
}
if constexpr (ScaleMsPerTile == 1) {
static_assert(size(RegLayoutScaleAEssential{}) == 1);
tCrScaleAViewAsC.data()[0] = __shfl_sync(0xffffffff, tCrScaleAViewAsC.data()[0] * scale_b, 0); // `tCrScaleAViewAsC.data()[0]` are all same in a warp group when `ScaleMsPerTile == 1`.
} else {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
tCrScaleAViewAsC.data()[i] = tCrScaleAViewAsC.data()[i] * scale_b;
}
}
warpgroup_arrive();
// Unroll the K mode manually to set scale D to 1
CUTLASS_PRAGMA_UNROLL
for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
// (V,M,K) x (V,N,K) => (V,M,N)
cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage), tCrB(_,_,k_block,read_stage), accumulation());
tiled_mma.accumulate_ = GMMA::ScaleOut::One;
}
warpgroup_commit_batch();
// Block scale the accumulators with reg tensor `tCrScaleAViewAsC`
accumulation.scale_if_needed(tCrScaleAViewAsC);
++smem_pipe_read;
}
warpgroup_fence_operand(accumulation());
// Mainloop GMMAs
k_tile_count -= prologue_mma_count;
CUTLASS_PRAGMA_NO_UNROLL
for ( ; k_tile_count > 0; --k_tile_count)
{
// WAIT on smem_pipe_read until its data are available (phase bit flips from rdPhaseBit value)
auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
pipeline.consumer_wait(smem_pipe_read, barrier_token);
//
// Compute on k_tile
//
int read_stage = smem_pipe_read.index();
// Load per block scale values from shared memory to registers (at most twice per block along M and exactly once per block along N)
scale_b = sScaleB[read_stage];
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
tCrScaleAViewAsC.data()[i] = tCsScaleAViewAsC(_, _, _, read_stage)(idx2crd(i, RegLayoutScaleAEssential{}));
}
if constexpr (ScaleMsPerTile == 1) {
static_assert(size(RegLayoutScaleAEssential{}) == 1);
tCrScaleAViewAsC.data()[0] = __shfl_sync(0xffffffff, tCrScaleAViewAsC.data()[0] * scale_b, 0); // `tCrScaleAViewAsC.data()[0]` are all same in a warp group when `ScaleMsPerTile == 1`.
} else {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
tCrScaleAViewAsC.data()[i] = tCrScaleAViewAsC.data()[i] * scale_b;
}
}
if (accumulation.prepare_if_needed()) {
tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
}
warpgroup_fence_operand(accumulation());
warpgroup_arrive();
// Unroll the K mode manually to set scale D to 1
CUTLASS_PRAGMA_UNROLL
for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
// (V,M,K) x (V,N,K) => (V,M,N)
cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage), tCrB(_,_,k_block,read_stage), accumulation());
tiled_mma.accumulate_ = GMMA::ScaleOut::One;
}
warpgroup_commit_batch();
/// Wait on the GMMA barrier for K_PIPE_MMAS (or fewer) outstanding to ensure smem_pipe_write is consumed
warpgroup_wait<K_PIPE_MMAS>();
warpgroup_fence_operand(accumulation());
// Block scale the accumulators with reg tensor `tCrScaleAViewAsC`
accumulation.scale_if_needed(tCrScaleAViewAsC);
pipeline.consumer_release(smem_pipe_release); // UNLOCK smem_pipe_release, done _computing_ on it
// Advance smem_pipe_read and smem_pipe_release
++smem_pipe_read;
++smem_pipe_release;
}
accumulation.scale_residue_if_needed(tCrScaleAViewAsC);
warpgroup_fence_operand(accumulation());
}
/// Perform a Consumer Epilogue to release all buffers
CUTLASS_DEVICE void
mma_tail(MainloopPipeline pipeline, PipelineState smem_pipe_release, int k_tile_count) {
// Prologue GMMAs
int prologue_mma_count = min(K_PIPE_MMAS, k_tile_count);
k_tile_count -= prologue_mma_count;
smem_pipe_release.advance(k_tile_count);
// Wait on all GMMAs to complete
warpgroup_wait<0>();
for (int count = 0; count < prologue_mma_count; ++count) {
pipeline.consumer_release(smem_pipe_release); // UNLOCK smem_pipe_release, done _computing_ on it
++smem_pipe_release;
}
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass::gemm::collective
/////////////////////////////////////////////////////////////////////////////////////////////////

39
csrc/cutlass_extensions/gemm/dispatch_policy.hpp Normal file
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@ -0,0 +1,39 @@
#pragma once
#include "cutlass/gemm/dispatch_policy.hpp"
namespace cutlass::gemm {
//////////////////////////////////////////////////////////////////////////////
// FP8 related policies (including Blocked Scaled Accumulation)
// `ScaleGranularityM` specifies scaling granularity along M, while zero-value
// `ScaleGranularityM` indicates that scaling granularity is
// `size<0>(TileShape_MNK{})` along M.
template <int ScaleGranularityM = 0>
struct KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum
: KernelTmaWarpSpecializedCooperative {};
// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp
// specialized dynamic schedule For FP8 kernels with Block Scaling
template <int Stages_, class ClusterShape_ = Shape<_1, _1, _1>,
class KernelSchedule = KernelTmaWarpSpecialized,
int ScaleGranularityM =
0 // `ScaleGranularityM` specifies scaling granularity along M,
// while zero-value `ScaleGranularityM` indicates that scaling
// granularity is `size<0>(TileShape_MNK{})` along M.
>
struct MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8
: MainloopSm90TmaGmmaWarpSpecialized<Stages_, ClusterShape_,
KernelSchedule> {
static_assert(
cute::is_same_v<
KernelSchedule,
KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<
ScaleGranularityM>>,
"KernelSchedule must be one of the warp specialized policies");
};
//////////////////////////////////////////////////////////////////////////////
} // namespace cutlass::gemm

2
csrc/cutlass_extensions/vllm_collective_builder.cuh
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@ -1,6 +1,6 @@
#pragma once #pragma once
#include "cutlass/gemm/collective/collective_builder.hpp" #include "cutlass_extensions/gemm/collective/collective_builder.hpp"
namespace cutlass::gemm::collective { namespace cutlass::gemm::collective {
using namespace cute; using namespace cute;

93
csrc/quantization/cutlass_w8a8/c3x/cutlass_gemm_caller.cuh Normal file
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@ -0,0 +1,93 @@
#pragma once
// clang-format will break include orders
// clang-format off
#include <torch/all.h>
#include <ATen/cuda/CUDAContext.h>
#include "cutlass/cutlass.h"
#include "cute/tensor.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cutlass/numeric_types.h"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "core/math.hpp"
#include "cutlass_extensions/common.hpp"
// clang-format on
namespace vllm::c3x {
static inline cute::Shape<int, int, int, int> get_problem_shape(
torch::Tensor const& a, torch::Tensor const& b) {
int32_t m = a.size(0), n = b.size(1), k = a.size(1);
return {m, n, k, 1};
}
template <typename GemmKernel>
void cutlass_gemm_caller(torch::Device device,
cute::Shape<int, int, int, int> prob_shape,
typename GemmKernel::MainloopArguments mainloop_args,
typename GemmKernel::EpilogueArguments epilogue_args) {
typename GemmKernel::Arguments args{cutlass::gemm::GemmUniversalMode::kGemm,
prob_shape, mainloop_args, epilogue_args};
// Launch the CUTLASS GEMM kernel.
using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
GemmOp gemm_op;
CUTLASS_CHECK(gemm_op.can_implement(args));
size_t workspace_size = gemm_op.get_workspace_size(args);
auto const workspace_options =
torch::TensorOptions().dtype(torch::kUInt8).device(device);
auto workspace = torch::empty(workspace_size, workspace_options);
auto stream = at::cuda::getCurrentCUDAStream(device.index());
cutlass::Status status = gemm_op.run(args, workspace.data_ptr(), stream);
CUTLASS_CHECK(status);
}
template <typename Gemm, typename... EpilogueArgs>
void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_params) {
using ElementAB = typename Gemm::ElementAB;
using ElementD = typename Gemm::ElementD;
using GemmKernel = typename Gemm::GemmKernel;
int64_t lda = a.stride(0);
int64_t ldb = b.stride(1);
int64_t ldc = out.stride(0);
using StrideA = cute::Stride<int64_t, cute::Int<1>, int64_t>;
using StrideB = cute::Stride<int64_t, cute::Int<1>, int64_t>;
using StrideC = typename Gemm::StrideC;
StrideA a_stride{lda, cute::Int<1>{}, 0};
StrideB b_stride{ldb, cute::Int<1>{}, 0};
StrideC c_stride{ldc, cute::Int<1>{}, cute::Int<0>{}};
typename GemmKernel::ProblemShape prob_shape = get_problem_shape(a, b);
auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
typename GemmKernel::MainloopArguments mainloop_args{a_ptr, a_stride, b_ptr,
b_stride};
auto c_ptr = static_cast<ElementD*>(out.data_ptr());
typename GemmKernel::EpilogueArguments epilogue_args{
Gemm::Epilogue::prepare_args(
std::forward<EpilogueArgs>(epilogue_params)...),
c_ptr, c_stride, c_ptr, c_stride};
cutlass_gemm_caller<GemmKernel>(a.device(), prob_shape, mainloop_args,
epilogue_args);
}
} // namespace vllm::c3x

74
csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cuh → csrc/quantization/cutlass_w8a8/c3x/scaled_mm.cuh
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@ -2,9 +2,6 @@
// clang-format will break include orders // clang-format will break include orders
// clang-format off // clang-format off
#include <torch/all.h>
#include <ATen/cuda/CUDAContext.h>
#include "cutlass/cutlass.h" #include "cutlass/cutlass.h"
@ -32,21 +29,6 @@ using namespace cute;
namespace vllm { namespace vllm {
// A wrapper for the GEMM kernel that is used to guard against compilation on
// architectures that will never use the kernel. The purpose of this is to
// reduce the size of the compiled binary.
// __CUDA_ARCH__ is not defined in host code, so this lets us smuggle the ifdef
// into code that will be executed on the device where it is defined.
template <typename Kernel>
struct enable_sm90_or_later : Kernel {
template <typename... Args>
CUTLASS_DEVICE void operator()(Args&&... args) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 900
Kernel::operator()(std::forward<Args>(args)...);
#endif
}
};
template <typename ElementAB_, typename ElementD_, template <typename ElementAB_, typename ElementD_,
template <typename, typename, typename> typename Epilogue_, template <typename, typename, typename> typename Epilogue_,
typename TileShape, typename ClusterShape, typename KernelSchedule, typename TileShape, typename ClusterShape, typename KernelSchedule,
@ -101,60 +83,4 @@ struct cutlass_3x_gemm {
struct GemmKernel : public KernelType {}; struct GemmKernel : public KernelType {};
}; };
template <typename Gemm, typename... EpilogueArgs>
void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_params) {
using ElementAB = typename Gemm::ElementAB;
using ElementD = typename Gemm::ElementD;
int32_t m = a.size(0);
int32_t n = b.size(1);
int32_t k = a.size(1);
int64_t lda = a.stride(0);
int64_t ldb = b.stride(1);
int64_t ldc = out.stride(0);
using StrideA = Stride<int64_t, Int<1>, int64_t>;
using StrideB = Stride<int64_t, Int<1>, int64_t>;
using StrideC = typename Gemm::StrideC;
StrideA a_stride{lda, Int<1>{}, 0};
StrideB b_stride{ldb, Int<1>{}, 0};
StrideC c_stride{ldc, Int<1>{}, Int<0>{}};
using GemmKernel = typename Gemm::GemmKernel;
typename GemmKernel::ProblemShape prob_shape{m, n, k, 1};
auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
typename GemmKernel::MainloopArguments mainloop_args{a_ptr, a_stride, b_ptr,
b_stride};
auto c_ptr = static_cast<ElementD*>(out.data_ptr());
typename GemmKernel::EpilogueArguments epilogue_args{
Gemm::Epilogue::prepare_args(
std::forward<EpilogueArgs>(epilogue_params)...),
c_ptr, c_stride, c_ptr, c_stride};
typename GemmKernel::Arguments args{cutlass::gemm::GemmUniversalMode::kGemm,
prob_shape, mainloop_args, epilogue_args};
// Launch the CUTLASS GEMM kernel.
using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
GemmOp gemm_op;
CUTLASS_CHECK(gemm_op.can_implement(args));
size_t workspace_size = gemm_op.get_workspace_size(args);
auto const workspace_options =
torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
auto workspace = torch::empty(workspace_size, workspace_options);
auto stream = at::cuda::getCurrentCUDAStream(a.get_device());
cutlass::Status status = gemm_op.run(args, workspace.data_ptr(), stream);
CUTLASS_CHECK(status);
}
} // namespace vllm } // namespace vllm

24
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@ -0,0 +1,24 @@
#include "scaled_mm_kernels.hpp"
#include "scaled_mm_sm90_int8_dispatch.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
namespace vllm {
void cutlass_scaled_mm_azp_sm90_int8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
torch::Tensor const& azp_adj,
std::optional<torch::Tensor> const& azp,
std::optional<torch::Tensor> const& bias) {
if (azp) {
return cutlass_scaled_mm_sm90_int8_epilogue<
c3x::ScaledEpilogueBiasAzpToken>(out, a, b, a_scales, b_scales, azp_adj,
*azp, bias);
} else {
return cutlass_scaled_mm_sm90_int8_epilogue<c3x::ScaledEpilogueBiasAzp>(
out, a, b, a_scales, b_scales, azp_adj, bias);
}
}
} // namespace vllm

24
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm90_fp8.cu Normal file
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@ -0,0 +1,24 @@
#include "scaled_mm_kernels.hpp"
#include "scaled_mm_blockwise_sm90_fp8_dispatch.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
namespace vllm {
void cutlass_scaled_mm_blockwise_sm90_fp8(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales) {
if (out.dtype() == torch::kBFloat16) {
cutlass_gemm_blockwise_sm90_fp8_dispatch<cutlass::bfloat16_t>(
out, a, b, a_scales, b_scales);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
cutlass_gemm_blockwise_sm90_fp8_dispatch<cutlass::half_t>(
out, a, b, a_scales, b_scales);
}
}
} // namespace vllm

168
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm90_fp8_dispatch.cuh Normal file
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@ -0,0 +1,168 @@
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cute/tensor.hpp"
#include "cutlass/tensor_ref.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/gemm/kernel/tile_scheduler_params.h"
#include "cutlass/epilogue/dispatch_policy.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass_extensions/gemm/dispatch_policy.hpp"
#include "cutlass_extensions/gemm/collective/collective_builder.hpp"
#include "cutlass_gemm_caller.cuh"
namespace vllm {
using namespace cute;
template <typename OutType, int GroupSizeM_, int GroupSizeN_, int GroupSizeK_,
int TileSizeM_ = 128, class ClusterShape = Shape<_1, _2, _1>>
struct cutlass_3x_gemm_fp8_blockwise {
using GroupSizeM = Int<GroupSizeM_>;
using GroupSizeN = Int<GroupSizeN_>;
using GroupSizeK = Int<GroupSizeK_>;
using TileSizeM = Int<TileSizeM_>;
static_assert(TileSizeM_ % GroupSizeM_ == 0,
"TileSizeM must be a multiple of GroupSizeM");
using ElementAB = cutlass::float_e4m3_t;
using ElementA = ElementAB;
using LayoutA = cutlass::layout::RowMajor;
static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
using ElementB = ElementAB;
using LayoutB = cutlass::layout::ColumnMajor;
static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
using ElementD = OutType;
using StrideD = Stride<int64_t, Int<1>, Int<0>>;
static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
using ElementC = void;
using StrideC = StrideD;
static constexpr int AlignmentC = AlignmentD;
using ElementAccumulator = float;
using ElementBlockScale = float;
using ElementCompute = float;
using ArchTag = cutlass::arch::Sm90;
using OperatorClass = cutlass::arch::OpClassTensorOp;
using TileShape = Shape<TileSizeM, GroupSizeN, GroupSizeK>;
using KernelSchedule = cutlass::gemm::
KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<
GroupSizeM_>;
using EpilogueSchedule = cutlass::epilogue::TmaWarpSpecializedCooperative;
using EpilogueTileType = cutlass::epilogue::collective::EpilogueTileAuto;
using StoreEpilogueCompute = typename cutlass::epilogue::fusion::Sm90EVT<
cutlass::epilogue::fusion::Sm90AccFetch>;
using CollectiveEpilogue =
typename cutlass::epilogue::collective::CollectiveBuilder<
ArchTag, OperatorClass, TileShape, ClusterShape, EpilogueTileType,
ElementAccumulator, ElementCompute, ElementC, StrideC, AlignmentC,
ElementD, StrideD, AlignmentD, EpilogueSchedule,
StoreEpilogueCompute>::CollectiveOp;
using CollectiveMainloop =
typename cutlass::gemm::collective::CollectiveBuilder<
ArchTag, OperatorClass, ElementA, LayoutA, AlignmentA, ElementB,
LayoutB, AlignmentB, ElementAccumulator, TileShape, ClusterShape,
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
sizeof(typename CollectiveEpilogue::SharedStorage))>,
KernelSchedule>::CollectiveOp;
using KernelType = enable_sm90_or_later<cutlass::gemm::kernel::GemmUniversal<
Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue,
cutlass::gemm::PersistentScheduler>>;
struct GemmKernel : public KernelType {};
using StrideA = typename GemmKernel::StrideA;
using StrideB = typename GemmKernel::StrideB;
};
template <typename Gemm>
void cutlass_gemm_caller_blockwise(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales) {
using GemmKernel = typename Gemm::GemmKernel;
using ElementAB = typename Gemm::ElementAB;
using ElementD = typename Gemm::ElementD;
auto prob_shape = c3x::get_problem_shape(a, b);
int32_t m = get<0>(prob_shape), n = get<1>(prob_shape),
k = get<2>(prob_shape);
int64_t lda = a.stride(0);
int64_t ldb = b.stride(1);
int64_t ldc = out.stride(0);
using StrideA = Stride<int64_t, Int<1>, int64_t>;
using StrideB = Stride<int64_t, Int<1>, int64_t>;
using StrideC = typename Gemm::StrideC;
StrideA a_stride{lda, Int<1>{}, 0};
StrideB b_stride{ldb, Int<1>{}, 0};
StrideC c_stride{ldc, Int<1>{}, Int<0>{}};
auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
auto a_scales_ptr = static_cast<float*>(a_scales.data_ptr());
auto b_scales_ptr = static_cast<float*>(b_scales.data_ptr());
// Check is the t is contiguous and is 1D or 2D with one of the dimensions
// being 1 (i.e. a row or column vector)
auto is_contiguous_vector = [](const torch::Tensor& t) {
auto t_sizes = t.sizes();
return t.is_contiguous() &&
(t.dim() == 1 ||
(t.dim() == 2 &&
*std::min_element(t_sizes.begin(), t_sizes.end()) == 1));
};
// TODO(lucas): lets clean-up the kernel so that we pass in Strides so
// we don't have to deal with enforcing implicit layouts
TORCH_CHECK(a_scales.size(0) == m / Gemm::GroupSizeM::value);
TORCH_CHECK(a_scales.size(1) == k / Gemm::GroupSizeK::value);
TORCH_CHECK(a_scales.stride(0) == 1 || is_contiguous_vector(a_scales),
"a_scales must be M major");
TORCH_CHECK(b_scales.size(0) == k / Gemm::GroupSizeK::value);
TORCH_CHECK(b_scales.size(1) == n / Gemm::GroupSizeN::value);
TORCH_CHECK(b_scales.stride(0) == 1 || is_contiguous_vector(b_scales),
"b_scales must be K major");
typename GemmKernel::MainloopArguments mainloop_args{
a_ptr, a_stride, b_ptr, b_stride, a_scales_ptr, b_scales_ptr};
auto c_ptr = static_cast<ElementD*>(out.data_ptr());
typename GemmKernel::EpilogueArguments epilogue_args{
{}, c_ptr, c_stride, c_ptr, c_stride};
c3x::cutlass_gemm_caller<GemmKernel>(a.device(), prob_shape, mainloop_args,
epilogue_args);
}
template <typename OutType>
void cutlass_gemm_blockwise_sm90_fp8_dispatch(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales) {
cutlass_gemm_caller_blockwise<
cutlass_3x_gemm_fp8_blockwise<OutType, 1, 128, 128>>(out, a, b, a_scales,
b_scales);
}
} // namespace vllm

33
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_kernels.hpp Normal file
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@ -0,0 +1,33 @@
#pragma once
#include <torch/all.h>
namespace vllm {
void cutlass_scaled_mm_sm90_fp8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias);
void cutlass_scaled_mm_sm90_int8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias);
void cutlass_scaled_mm_azp_sm90_int8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
torch::Tensor const& azp_adj,
std::optional<torch::Tensor> const& azp,
std::optional<torch::Tensor> const& bias);
void cutlass_scaled_mm_blockwise_sm90_fp8(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales);
} // namespace vllm

24
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu Normal file
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@ -0,0 +1,24 @@
#include "scaled_mm_kernels.hpp"
#include "scaled_mm_sm90_fp8_dispatch.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
namespace vllm {
void cutlass_scaled_mm_sm90_fp8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias) {
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) {
TORCH_CHECK(bias->dtype() == out.dtype(),
"currently bias dtype must match output dtype ", out.dtype());
return cutlass_scaled_mm_sm90_fp8_epilogue<c3x::ScaledEpilogueBias>(
out, a, b, a_scales, b_scales, *bias);
} else {
return cutlass_scaled_mm_sm90_fp8_epilogue<c3x::ScaledEpilogue>(
out, a, b, a_scales, b_scales);
}
}
} // namespace vllm

26
csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90_fp8_dispatch.cuh → csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8_dispatch.cuh
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@ -1,6 +1,7 @@
#pragma once #pragma once
#include "scaled_mm_c3x.cuh" #include "scaled_mm.cuh"
#include "cutlass_gemm_caller.cuh"
/** /**
* This file defines Gemm kernel configurations for SM90 (fp8) based on the Gemm * This file defines Gemm kernel configurations for SM90 (fp8) based on the Gemm
@ -9,6 +10,8 @@
namespace vllm { namespace vllm {
using c3x::cutlass_gemm_caller;
template <typename InType, typename OutType, template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue> template <typename, typename, typename> typename Epilogue>
struct sm90_fp8_config_default { struct sm90_fp8_config_default {
@ -93,4 +96,25 @@ inline void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out,
} }
} }
template <template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_scaled_mm_sm90_fp8_epilogue(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_args) {
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::bfloat16_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::half_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
}
}
} // namespace vllm } // namespace vllm

24
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_int8.cu Normal file
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@ -0,0 +1,24 @@
#include "scaled_mm_kernels.hpp"
#include "scaled_mm_sm90_int8_dispatch.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
namespace vllm {
void cutlass_scaled_mm_sm90_int8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias) {
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) {
TORCH_CHECK(bias->dtype() == out.dtype(),
"currently bias dtype must match output dtype ", out.dtype());
return cutlass_scaled_mm_sm90_int8_epilogue<c3x::ScaledEpilogueBias>(
out, a, b, a_scales, b_scales, *bias);
} else {
return cutlass_scaled_mm_sm90_int8_epilogue<c3x::ScaledEpilogue>(
out, a, b, a_scales, b_scales);
}
}
} // namespace vllm

25
csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90_int8_dispatch.cuh → csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_int8_dispatch.cuh
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@ -1,6 +1,7 @@
#pragma once #pragma once
#include "scaled_mm_c3x.cuh" #include "scaled_mm.cuh"
#include "cutlass_gemm_caller.cuh"
/** /**
* This file defines Gemm kernel configurations for SM90 (int8) based on the * This file defines Gemm kernel configurations for SM90 (int8) based on the
@ -9,6 +10,8 @@
namespace vllm { namespace vllm {
using c3x::cutlass_gemm_caller;
template <typename InType, typename OutType, template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue> template <typename, typename, typename> typename Epilogue>
struct sm90_int8_config_default { struct sm90_int8_config_default {
@ -137,4 +140,24 @@ inline void cutlass_gemm_sm90_int8_dispatch(torch::Tensor& out,
} }
} }
template <template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_scaled_mm_sm90_int8_epilogue(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_args) {
TORCH_CHECK(a.dtype() == torch::kInt8);
TORCH_CHECK(b.dtype() == torch::kInt8);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::bfloat16_t,
Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::half_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
}
}
} // namespace vllm } // namespace vllm

104
csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu
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@ -1,52 +1,13 @@
#include <cudaTypedefs.h> #include <cudaTypedefs.h>
#include "c3x/scaled_mm_kernels.hpp"
#if defined CUDA_VERSION && CUDA_VERSION >= 12000 #include "core/math.hpp"
#include "scaled_mm_c3x_sm90_fp8_dispatch.cuh"
#include "scaled_mm_c3x_sm90_int8_dispatch.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
using namespace vllm;
/* /*
This file defines quantized GEMM operations using the CUTLASS 3.x API, for This file defines quantized GEMM operations using the CUTLASS 3.x API, for
NVIDIA GPUs with sm90a (Hopper) or later. NVIDIA GPUs with sm90a (Hopper) or later.
*/ */
template <template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_scaled_mm_sm90_epilogue(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_args) {
if (a.dtype() == torch::kInt8) {
TORCH_CHECK(b.dtype() == torch::kInt8);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::bfloat16_t,
Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::half_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
}
} else {
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::bfloat16_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::half_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
}
}
}
void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a, void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
torch::Tensor const& b, torch::Tensor const& b,
torch::Tensor const& a_scales, torch::Tensor const& a_scales,
@ -54,14 +15,50 @@ void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
std::optional<torch::Tensor> const& bias) { std::optional<torch::Tensor> const& bias) {
TORCH_CHECK(a_scales.dtype() == torch::kFloat32); TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
TORCH_CHECK(b_scales.dtype() == torch::kFloat32); TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
if (bias) {
TORCH_CHECK(bias->dtype() == c.dtype(), using GroupShape = std::array<int64_t, 2>;
"currently bias dtype must match output dtype ", c.dtype());
return cutlass_scaled_mm_sm90_epilogue<c3x::ScaledEpilogueBias>( int M = a.size(0), N = b.size(1), K = a.size(1);
c, a, b, a_scales, b_scales, *bias);
GroupShape a_scale_group_shape = [&, &s = a_scales]() -> GroupShape {
if (s.numel() == 1) return {M, K}; // tensor-wise
if (s.dim() == 2)
return {ceil_div(a.size(0), s.size(0)), ceil_div(a.size(1), s.size(1))};
TORCH_CHECK(false, "Unsupported scale shape for scale_a");
}();
GroupShape b_scale_group_shape = [&, &s = b_scales]() -> GroupShape {
if (s.numel() == 1) return {K, N}; // tensor-wise
if (s.dim() == 2)
return {ceil_div(b.size(0), s.size(0)), ceil_div(b.size(1), s.size(1))};
TORCH_CHECK(false, "Unsupported scale shape for scale_b");
}();
if ((a_scale_group_shape == GroupShape{M, K} ||
a_scale_group_shape == GroupShape{1, K}) &&
(b_scale_group_shape == GroupShape{K, N} ||
b_scale_group_shape == GroupShape{K, 1})) {
// "standard per-tensor/per-token/per-channel" scaling
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (a.dtype() == torch::kFloat8_e4m3fn) {
vllm::cutlass_scaled_mm_sm90_fp8(c, a, b, a_scales, b_scales, bias);
} else { } else {
return cutlass_scaled_mm_sm90_epilogue<c3x::ScaledEpilogue>( TORCH_CHECK(a.dtype() == torch::kInt8);
c, a, b, a_scales, b_scales); vllm::cutlass_scaled_mm_sm90_int8(c, a, b, a_scales, b_scales, bias);
}
} else if (a_scale_group_shape == GroupShape{1, 128} &&
b_scale_group_shape == GroupShape{128, 128}) {
// 1x128 per-token group scales for activations
// 128x128 blockwise scales for weights
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn &&
b.dtype() == torch::kFloat8_e4m3fn,
"Currently only FP8 is supported for A group shape 1x128 and "
"B group shape 128x128");
TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");
vllm::cutlass_scaled_mm_blockwise_sm90_fp8(c, a, b, a_scales, b_scales);
} else {
TORCH_CHECK(false, "Unsupported scale group shapes for CUTLASS 3.x GEMM");
} }
} }
@ -75,13 +72,6 @@ void cutlass_scaled_mm_azp_sm90(torch::Tensor& out, torch::Tensor const& a,
TORCH_CHECK(a_scales.dtype() == torch::kFloat32); TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
TORCH_CHECK(b_scales.dtype() == torch::kFloat32); TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
if (azp) { vllm::cutlass_scaled_mm_azp_sm90_int8(out, a, b, a_scales, b_scales, azp_adj,
return cutlass_scaled_mm_sm90_epilogue<c3x::ScaledEpilogueBiasAzpToken>( azp, bias);
out, a, b, a_scales, b_scales, azp_adj, *azp, bias);
} else {
return cutlass_scaled_mm_sm90_epilogue<c3x::ScaledEpilogueBiasAzp>(
out, a, b, a_scales, b_scales, azp_adj, bias);
} }
}
#endif

3
csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu
View File

@ -89,15 +89,12 @@ void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
TORCH_CHECK(a.dim() == 2 && b.dim() == 2 && c.dim() == 2); TORCH_CHECK(a.dim() == 2 && b.dim() == 2 && c.dim() == 2);
TORCH_CHECK(c.size(0) == a.size(0) && a.size(1) == b.size(0) && TORCH_CHECK(c.size(0) == a.size(0) && a.size(1) == b.size(0) &&
b.size(1) == c.size(1)); b.size(1) == c.size(1));
TORCH_CHECK(a_scales.numel() == 1 || a_scales.numel() == a.size(0));
TORCH_CHECK(b_scales.numel() == 1 || b_scales.numel() == b.size(1));
// Check for strides and alignment // Check for strides and alignment
TORCH_CHECK(a.stride(1) == 1 && c.stride(1) == 1); // Row-major TORCH_CHECK(a.stride(1) == 1 && c.stride(1) == 1); // Row-major
TORCH_CHECK(b.stride(0) == 1); // Column-major TORCH_CHECK(b.stride(0) == 1); // Column-major
TORCH_CHECK(c.stride(0) % 16 == 0 && TORCH_CHECK(c.stride(0) % 16 == 0 &&
b.stride(1) % 16 == 0); // 16 Byte Alignment b.stride(1) % 16 == 0); // 16 Byte Alignment
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) { if (bias) {
TORCH_CHECK(bias->numel() == b.size(1) && bias->is_contiguous() && TORCH_CHECK(bias->numel() == b.size(1) && bias->is_contiguous() &&

4
csrc/quantization/machete/machete_mainloop.cuh
View File

@ -272,6 +272,10 @@ struct MacheteCollectiveMma {
using PipelineState = cutlass::PipelineState<DispatchPolicy::Stages>; using PipelineState = cutlass::PipelineState<DispatchPolicy::Stages>;
using PipelineParams = typename MainloopPipeline::Params; using PipelineParams = typename MainloopPipeline::Params;
// One threads per CTA are producers (1 for operand tile)
static constexpr int NumProducerThreadEvents = 1;
using ScaleTileShape = decltype(make_shape(shape<0>(TileShape{}), using ScaleTileShape = decltype(make_shape(shape<0>(TileShape{}),
shape<1>(SmemLayoutAtomScale{}))); shape<1>(SmemLayoutAtomScale{})));

188
tests/kernels/test_cutlass.py
View File

@ -10,6 +10,7 @@ import torch
from tests.kernels.utils import opcheck from tests.kernels.utils import opcheck
from vllm import _custom_ops as ops from vllm import _custom_ops as ops
from vllm.platforms import current_platform from vllm.platforms import current_platform
from vllm.utils import cdiv
from .utils import baseline_scaled_mm, to_fp8, to_int8 from .utils import baseline_scaled_mm, to_fp8, to_int8
@ -39,6 +40,11 @@ CUDA_DEVICES = [
f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2) f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
] ]
# -1 means full extent in that dimension
TENSORWISE_GROUP_SHAPE = (-1, -1)
PER_TOKEN_GROUP_SHAPE = (1, -1)
PER_OUT_CH_GROUP_SHAPE = (-1, 1)
capability = current_platform.get_device_capability() capability = current_platform.get_device_capability()
capability = capability[0] * 10 + capability[1] capability = capability[0] * 10 + capability[1]
@ -47,11 +53,22 @@ def rand_int8(shape: tuple, device: str = "cuda"):
return to_int8(torch.rand(shape, device=device) * 255 - 128) return to_int8(torch.rand(shape, device=device) * 255 - 128)
def group_scale_helper(shape, group_shape):
return [shape[i] if s < 0 else s for i, s in enumerate(group_shape)]
def scale_shape(shape, group_shape):
assert len(shape) == len(group_shape)
group_shape = group_scale_helper(shape, group_shape)
return tuple(
cdiv(shape[i], group_shape[i]) for i in range(len(group_shape)))
def cutlass_fp8_gemm_helper(m: int, def cutlass_fp8_gemm_helper(m: int,
n: int, n: int,
k: int, k: int,
per_token_act_quant: bool, a_scale_group_shape: tuple,
per_out_channel_weight_quant: bool, b_scale_group_shape: tuple,
use_bias: bool, use_bias: bool,
out_dtype: Type[torch.dtype] = torch.bfloat16, out_dtype: Type[torch.dtype] = torch.bfloat16,
device: str = "cuda"): device: str = "cuda"):
@ -60,13 +77,17 @@ def cutlass_fp8_gemm_helper(m: int,
a = to_fp8(torch.randn((m, k), device=device)) a = to_fp8(torch.randn((m, k), device=device))
b = to_fp8(torch.randn((n, k), device=device).t()) b = to_fp8(torch.randn((n, k), device=device).t())
m_a_scales = m if per_token_act_quant else 1 a_scales_shape = scale_shape(a.shape, a_scale_group_shape)
n_b_scales = n if per_out_channel_weight_quant else 1 b_scales_shape = scale_shape(b.shape, b_scale_group_shape)
scale_a = (torch.randn(a_scales_shape, device=device, dtype=torch.float32))
scale_b = (torch.randn(b_scales_shape, device=device, dtype=torch.float32))
# make scales M-major for blockwise quant, doesn't affect 1D scales
scale_a = scale_a.t().contiguous().t()
# make scales K-major for blockwise quant, doesn't affect 1D scales
scale_b = scale_b.t().contiguous().t()
scale_a = (torch.randn((m_a_scales, 1), device=device,
dtype=torch.float32))
scale_b = (torch.randn((1, n_b_scales), device=device,
dtype=torch.float32))
if use_bias: if use_bias:
bias = torch.rand((n, ), device=device, dtype=out_dtype) * 10 bias = torch.rand((n, ), device=device, dtype=out_dtype) * 10
else: else:
@ -84,8 +105,8 @@ def cutlass_fp8_gemm_helper(m: int,
def cutlass_int8_gemm_helper(m: int, def cutlass_int8_gemm_helper(m: int,
n: int, n: int,
k: int, k: int,
per_token_act_quant: bool, a_scale_group_shape: tuple,
per_out_channel_weight_quant: bool, b_scale_group_shape: tuple,
use_bias: bool, use_bias: bool,
out_dtype: Type[torch.dtype] = torch.bfloat16, out_dtype: Type[torch.dtype] = torch.bfloat16,
device: str = "cuda"): device: str = "cuda"):
@ -94,13 +115,11 @@ def cutlass_int8_gemm_helper(m: int,
a = to_int8(torch.randn((m, k), device=device) * 5) a = to_int8(torch.randn((m, k), device=device) * 5)
b = to_int8(torch.randn((n, k), device=device).t() * 5) b = to_int8(torch.randn((n, k), device=device).t() * 5)
m_a_scales = m if per_token_act_quant else 1 a_scales_shape = scale_shape(a.shape, a_scale_group_shape)
n_b_scales = n if per_out_channel_weight_quant else 1 b_scales_shape = scale_shape(b.shape, b_scale_group_shape)
scale_a = (torch.randn((m_a_scales, 1), device=device, scale_a = (torch.randn(a_scales_shape, device=device, dtype=torch.float32))
dtype=torch.float32)) scale_b = (torch.randn(b_scales_shape, device=device, dtype=torch.float32))
scale_b = (torch.randn((1, n_b_scales), device=device,
dtype=torch.float32))
if use_bias: if use_bias:
bias = torch.rand((n, ), device=device, dtype=out_dtype) * 10 bias = torch.rand((n, ), device=device, dtype=out_dtype) * 10
@ -117,82 +136,135 @@ def cutlass_int8_gemm_helper(m: int,
@pytest.mark.parametrize("m,n,k", MNK_FACTORS) @pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
@pytest.mark.skipif(not current_platform.has_device_capability(89), @pytest.mark.skipif(not current_platform.has_device_capability(89),
reason="FP8 is not supported on this GPU type.") reason="FP8 is not supported on this GPU type.")
def test_cutlass_fp8_gemm(m: int, n: int, k: int, per_act_token: bool, def test_cutlass_fp8_gemm(m: int, n: int, k: int, a_scale_group_shape,
per_out_ch: bool, use_bias: bool): b_scale_group_shape, use_bias: bool):
cutlass_fp8_gemm_helper(m, n, k, per_act_token, per_out_ch, use_bias) cutlass_fp8_gemm_helper(m, n, k, a_scale_group_shape, b_scale_group_shape,
use_bias)
@pytest.mark.parametrize("m,n,k", MNK_FACTORS) @pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape,b_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [((1, 128), (128, 128))])
@pytest.mark.parametrize("use_bias", [False])
@pytest.mark.skipif(not current_platform.has_device_capability(90),
reason="FP8 blockwise is not supported on this GPU type.")
def test_cutlass_fp8_blockwise_scale_gemm(m: int, n: int, k: int,
a_scale_group_shape,
b_scale_group_shape, use_bias: bool):
if k % b_scale_group_shape[0] != 0 or n % b_scale_group_shape[1] != 0:
return
if m % a_scale_group_shape[0] != 0 or k % a_scale_group_shape[1] != 0:
return
cutlass_fp8_gemm_helper(m, n, k, a_scale_group_shape, b_scale_group_shape,
use_bias)
@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("a_scale_group_shape",
[PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
def test_cutlass_int8_gemm(m: int, n: int, k: int, per_act_token: bool, def test_cutlass_int8_gemm(m: int, n: int, k: int, a_scale_group_shape,
per_out_ch: bool, use_bias: bool): b_scale_group_shape, use_bias: bool):
cutlass_int8_gemm_helper(m, n, k, per_act_token, per_out_ch, use_bias) cutlass_int8_gemm_helper(m, n, k, a_scale_group_shape, b_scale_group_shape,
use_bias)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("out_dtype", [torch.bfloat16, torch.float16])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
def test_cutlass_int8_gemm_output_dtype(per_act_token: bool, per_out_ch: bool, def test_cutlass_int8_gemm_output_dtype(a_scale_group_shape,
b_scale_group_shape,
out_dtype: Type[torch.dtype], out_dtype: Type[torch.dtype],
use_bias: bool): use_bias: bool):
cutlass_int8_gemm_helper(512, cutlass_int8_gemm_helper(512,
512, 512,
512, 512,
per_act_token, a_scale_group_shape,
per_out_ch, b_scale_group_shape,
use_bias, use_bias,
out_dtype=out_dtype) out_dtype=out_dtype)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("out_dtype", [torch.bfloat16, torch.float16])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
@pytest.mark.skipif(not current_platform.has_device_capability(89), @pytest.mark.skipif(not current_platform.has_device_capability(89),
reason="FP8 is not supported on this GPU type.") reason="FP8 is not supported on this GPU type.")
def test_cutlass_fp8_gemm_output_dtype(per_act_token: bool, per_out_ch: bool, def test_cutlass_fp8_gemm_output_dtype(a_scale_group_shape,
b_scale_group_shape,
out_dtype: Type[torch.dtype], out_dtype: Type[torch.dtype],
use_bias: bool): use_bias: bool):
cutlass_fp8_gemm_helper(512, cutlass_fp8_gemm_helper(512,
512, 512,
512, 512,
per_act_token, a_scale_group_shape,
per_out_ch, b_scale_group_shape,
use_bias, use_bias,
out_dtype=out_dtype) out_dtype=out_dtype)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape,b_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [((1, 128), (128, 128))])
@pytest.mark.parametrize("out_dtype", [torch.bfloat16, torch.float16])
@pytest.mark.parametrize("use_bias", [False])
@pytest.mark.skipif(not current_platform.has_device_capability(90),
reason="FP8 blockwise is not supported on this GPU type.")
def test_cutlass_fp8_blockwise_scale_gemm_dtype(a_scale_group_shape,
b_scale_group_shape,
out_dtype: Type[torch.dtype],
use_bias: bool):
cutlass_fp8_gemm_helper(512,
512,
512,
a_scale_group_shape,
b_scale_group_shape,
use_bias,
out_dtype=out_dtype)
@pytest.mark.parametrize("a_scale_group_shape",
[PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
@pytest.mark.parametrize("device", CUDA_DEVICES) @pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.skipif(not current_platform.has_device_capability(89), @pytest.mark.skipif(not current_platform.has_device_capability(89),
reason="FP8 is not supported on this GPU type.") reason="FP8 is not supported on this GPU type.")
def test_cutlass_fp8_gemm_devices(per_act_token: bool, per_out_ch: bool, def test_cutlass_fp8_gemm_devices(a_scale_group_shape, b_scale_group_shape,
use_bias: bool, device: str): use_bias: bool, device: str):
cutlass_fp8_gemm_helper(512, 512, 512, per_act_token, per_out_ch, use_bias, cutlass_fp8_gemm_helper(512, 512, 512, a_scale_group_shape,
torch.bfloat16, device) b_scale_group_shape, use_bias, torch.bfloat16,
device)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
@pytest.mark.parametrize("device", CUDA_DEVICES) @pytest.mark.parametrize("device", CUDA_DEVICES)
def test_cutlass_int8_gemm_devices(per_act_token: bool, per_out_ch: bool, def test_cutlass_int8_gemm_devices(a_scale_group_shape, b_scale_group_shape,
use_bias: bool, device: str): use_bias: bool, device: str):
cutlass_int8_gemm_helper(512, cutlass_int8_gemm_helper(512,
512, 512,
512, 512,
per_act_token, a_scale_group_shape,
per_out_ch, b_scale_group_shape,
use_bias, use_bias,
out_dtype=torch.bfloat16, out_dtype=torch.bfloat16,
device=device) device=device)
@ -203,28 +275,32 @@ def test_cutlass_int8_gemm_devices(per_act_token: bool, per_out_ch: bool,
# of a large power of two. In any case, the kernel will have a naive fallback # of a large power of two. In any case, the kernel will have a naive fallback
# when N and K are not divisible by 16. But M is the number of tokens and the # when N and K are not divisible by 16. But M is the number of tokens and the
# kernel must handle any M thrown at it. # kernel must handle any M thrown at it.
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
@pytest.mark.skipif(not current_platform.has_device_capability(89), @pytest.mark.skipif(not current_platform.has_device_capability(89),
reason="FP8 is not supported on this GPU type.") reason="FP8 is not supported on this GPU type.")
def test_cutlass_fp8_gemm_m_sweep(per_act_token: bool, per_out_ch: bool, def test_cutlass_fp8_gemm_m_sweep(a_scale_group_shape, b_scale_group_shape,
use_bias: bool): use_bias: bool):
for nk in range(32, 128, 32): for nk in range(32, 128, 32):
for m in range(1, 128): for m in range(1, 128):
cutlass_fp8_gemm_helper(m, nk, nk, per_act_token, per_out_ch, cutlass_fp8_gemm_helper(m, nk, nk, a_scale_group_shape,
use_bias) b_scale_group_shape, use_bias)
@pytest.mark.parametrize("per_act_token", [True, False]) @pytest.mark.parametrize("a_scale_group_shape",
@pytest.mark.parametrize("per_out_ch", [True, False]) [PER_TOKEN_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("b_scale_group_shape",
[PER_OUT_CH_GROUP_SHAPE, TENSORWISE_GROUP_SHAPE])
@pytest.mark.parametrize("use_bias", [True, False]) @pytest.mark.parametrize("use_bias", [True, False])
def test_cutlass_int8_gemm_m_sweep(per_act_token: bool, per_out_ch: bool, def test_cutlass_int8_gemm_m_sweep(a_scale_group_shape, b_scale_group_shape,
use_bias: bool): use_bias: bool):
for nk in range(32, 128, 32): for nk in range(32, 128, 32):
for m in range(1, 128): for m in range(1, 128):
cutlass_int8_gemm_helper(m, nk, nk, per_act_token, per_out_ch, cutlass_int8_gemm_helper(m, nk, nk, a_scale_group_shape,
use_bias) b_scale_group_shape, use_bias)
@pytest.mark.parametrize("m", [32, 64, 128]) @pytest.mark.parametrize("m", [32, 64, 128])

32
tests/kernels/utils.py
View File

@ -1119,8 +1119,36 @@ def baseline_scaled_mm(a: torch.Tensor,
scale_b: torch.Tensor, scale_b: torch.Tensor,
out_dtype: Type[torch.dtype], out_dtype: Type[torch.dtype],
bias: Optional[torch.Tensor] = None) -> torch.Tensor: bias: Optional[torch.Tensor] = None) -> torch.Tensor:
output = (scale_a * (scale_b * (torch.mm(
a.to(dtype=torch.float32), b.to(dtype=torch.float32))))).to(out_dtype) # We treat N-dimensional group scaling as extended numpy-style broadcasting
# in numpy simply stretches dimensions with an extent of 1 to match the
# the target shape by repeating the data along that dimension (broadcasting)
# , we extend these semantics to say if the extent of a dimension in the
# source shape is not 1 and does not match the target shape we repeat each
# element along that dimension src_shape[dim] // target_shape[dim] times
# example if we have:
# a = [[1, 2], and target_shape = (2, 4)
# [3, 4]]
# then we would expand a to:
# a = [[1, 1, 2, 2],
# [3, 3, 4, 4]]
# NOTE this function this function does not explicitly broadcast dimensions
# with an extent of 1, since this can be done implicitly by pytorch
def group_broadcast(t, shape):
for i, s in enumerate(shape):
if t.shape[i] != s and t.shape[i] != 1:
assert s % t.shape[i] == 0
t = t.unsqueeze(i + 1)\
.expand(*t.shape[:i+1], s // t.shape[i], *t.shape[i+1:])\
.flatten(i, i + 1)
return t
scale_a = group_broadcast(scale_a, a.shape)
scale_b = group_broadcast(scale_b, b.shape)
output = torch.mm((scale_a * a.to(dtype=torch.float32)),
(scale_b * b.to(dtype=torch.float32))).to(out_dtype)
if bias is not None: if bias is not None:
output = output + bias output = output + bias

22
vllm/_custom_ops.py
View File

@ -441,6 +441,28 @@ def cutlass_scaled_mm(a: torch.Tensor,
scale_b: torch.Tensor, scale_b: torch.Tensor,
out_dtype: torch.dtype, out_dtype: torch.dtype,
bias: Optional[torch.Tensor] = None) -> torch.Tensor: bias: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
`cutlass_scaled_mm` implements a fused version of
`output = torch.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
where scale_a * a and scale_b * b are implemented using numpy-style
broadcasting.
In order to support blockwise scaling like found in DeepSeek V3 we also
support extended "group" broadcast rules. We extend the numpy-style
broadcasting rules with the following rule:
"if the extent of a dimension in the source shape is between 1 and
corresponding extent in the target shape we repeat each element along
that dimension src_shape[dim] // target_shape[dim] times consecutively"
example if we have:
a = [[1, 2], and target_shape = (2, 4)
[3, 4]]
then we would expand a to:
a = [[1, 1, 2, 2],
[3, 3, 4, 4]]
currently we only support the case:
scale_a.shape * [1, 128] == a.shape
scale_b.shape * [128, 128] == b.shape
"""
assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0) assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16) assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
assert bias is None or bias.shape[0] == b.shape[ assert bias is None or bias.shape[0] == b.shape[