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vllm/benchmarks/kernels/benchmark_marlin.py

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import torch
import torch.utils.benchmark as benchmark
from benchmark_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
GPTQ_MARLIN_24_MAX_PARALLEL,
GPTQ_MARLIN_24_MIN_THREAD_N,
GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES,
GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES,
)
from vllm.model_executor.layers.quantization.utils.allspark_utils import (
ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD,
ALLSPARK_SUPPORTED_QUANT_TYPES,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
GPTQ_MARLIN_MAX_PARALLEL,
GPTQ_MARLIN_MIN_THREAD_N,
MARLIN_SUPPORTED_GROUP_SIZES,
query_marlin_supported_quant_types,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
FP4_MARLIN_SUPPORTED_GROUP_SIZES,
rand_marlin_weight_fp4_like,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
marlin_quant_fp8_torch,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
MarlinWorkspace,
awq_marlin_quantize,
marlin_quantize,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
marlin_24_quantize,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
gptq_pack,
gptq_quantize_weights,
quantize_weights,
sort_weights,
)
from vllm.scalar_type import ScalarType, scalar_types
from vllm.utils import FlexibleArgumentParser
DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]
ACT_ORDER_OPTS = [False, True]
K_FULL_OPTS = [False, True]
def bench_run(
results: list[benchmark.Measurement],
model: str,
act_order: bool,
is_k_full: bool,
quant_type: ScalarType,
group_size: int,
size_m: int,
size_k: int,
size_n: int,
):
label = "Quant Matmul"
sub_label = "{}, act={} k_full={}, q={}, g={}, MKN=({}x{}x{})".format(
model, act_order, is_k_full, str(quant_type), group_size, size_m, size_k, size_n
)
print(f"Testing: {sub_label}")
a = torch.randn(size_m, size_k).to(torch.half).cuda()
b = torch.rand(size_k, size_n).to(torch.half).cuda()
has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]
if act_order and (group_size == -1 or group_size == size_k or has_zp):
return
if size_k % group_size != 0:
return
marlin_24_supported = (
quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
)
repack_supported = (
quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
and group_size in MARLIN_SUPPORTED_GROUP_SIZES
)
allspark_supported = (
quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
and group_size == -1
and not act_order
and is_k_full
)
def gen_marlin_params():
# Marlin quant
marlin_g_idx = marlin_sort_indices = marlin_zp = marlin_s2 = None
if quant_type == scalar_types.float4_e2m1f:
if group_size != 16 or act_order:
return
marlin_w_ref, marlin_q_w, marlin_s, marlin_s2 = rand_marlin_weight_fp4_like(
b.T, group_size
)
elif quant_type == scalar_types.float8_e4m3fn:
if group_size not in [-1, 128] or act_order:
return
marlin_w_ref, marlin_q_w, marlin_s = marlin_quant_fp8_torch(b.T, group_size)
elif group_size == 16:
return
elif has_zp:
marlin_w_ref, marlin_q_w, marlin_s, marlin_zp = awq_marlin_quantize(
b, quant_type, group_size
)
else:
marlin_w_ref, marlin_q_w, marlin_s, marlin_g_idx, marlin_sort_indices, _ = (
marlin_quantize(b, quant_type, group_size, act_order)
)
return (
marlin_w_ref,
marlin_q_w,
marlin_s,
marlin_s2,
marlin_zp,
marlin_g_idx,
marlin_sort_indices,
)
def gen_marlin_24_params():
marlin_24_w_ref = marlin_24_q_w_comp = marlin_24_meta = marlin_24_s = None
if marlin_24_supported:
(marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s) = (
marlin_24_quantize(b, quant_type, group_size)
)
return (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s)
def gen_repack_params():
q_w_gptq = None
repack_sort_indices = None
if repack_supported:
(w_ref, q_w, s, g_idx, rand_perm) = gptq_quantize_weights(
b, quant_type, group_size, act_order
)
q_w_gptq = gptq_pack(q_w, quant_type.size_bits, size_k, size_n)
# For act_order, sort the "weights" and "g_idx"
# so that group ids are increasing
repack_sort_indices = torch.empty(0, dtype=torch.int, device=b.device)
if act_order:
(q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
return q_w_gptq, repack_sort_indices
def gen_allspark_params():
qw_reorder = s_reorder = zp_reorder = sm_count = sm_version = (
CUBLAS_M_THRESHOLD
) = None
nonlocal allspark_supported
if allspark_supported:
properties = torch.cuda.get_device_properties(b.device.index)
sm_count = properties.multi_processor_count
sm_version = properties.major * 10 + properties.minor
supported_arch = sm_version >= 80 and sm_version < 90
allspark_supported = allspark_supported and supported_arch
if supported_arch:
w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size, has_zp)
qw = qw.to(torch.uint8)
qw_reorder, s_reorder, zp_reorder = ops.allspark_repack_weight(
qw, s, zp, has_zp
)
CUBLAS_M_THRESHOLD = ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD
return (
qw_reorder,
s_reorder,
zp_reorder,
sm_count,
sm_version,
CUBLAS_M_THRESHOLD,
)
(
marlin_w_ref,
marlin_q_w,
marlin_s,
marlin_s2,
marlin_zp,
marlin_g_idx,
marlin_sort_indices,
) = gen_marlin_params()
marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s = (
gen_marlin_24_params()
)
q_w_gptq, repack_sort_indices = gen_repack_params()
qw_reorder, s_reorder, zp_reorder, sm_count, sm_version, CUBLAS_M_THRESHOLD = (
gen_allspark_params()
)
# Prepare
marlin_workspace = MarlinWorkspace(
size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
)
marlin_24_workspace = MarlinWorkspace(
size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
)
globals = {
# Gen params
"quant_type": quant_type,
"group_size": group_size,
"size_m": size_m,
"size_n": size_n,
"size_k": size_k,
"a": a,
# Marlin params
"marlin_w_ref": marlin_w_ref,
"marlin_q_w": marlin_q_w,
"marlin_s": marlin_s,
"marlin_s2": marlin_s2,
"marlin_zp": marlin_zp,
"marlin_g_idx": marlin_g_idx,
"marlin_sort_indices": marlin_sort_indices,
"marlin_workspace": marlin_workspace,
"is_k_full": is_k_full,
# Marlin_24 params
"marlin_24_w_ref": marlin_24_w_ref,
"marlin_24_q_w_comp": marlin_24_q_w_comp,
"marlin_24_meta": marlin_24_meta,
"marlin_24_s": marlin_24_s,
"marlin_24_workspace": marlin_24_workspace,
# GPTQ params
"q_w_gptq": q_w_gptq,
"repack_sort_indices": repack_sort_indices,
# AllSpark W8A16 params
"qw_reorder": qw_reorder,
"s_reorder": s_reorder,
"zp_reorder": zp_reorder,
"sm_count": sm_count,
"sm_version": sm_version,
"CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD,
# Kernels
"gptq_marlin_gemm": ops.gptq_marlin_gemm,
"gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
"gptq_marlin_repack": ops.gptq_marlin_repack,
"allspark_w8a16_gemm": ops.allspark_w8a16_gemm,
}
min_run_time = 1
# Warmup pytorch
for _ in range(5):
torch.matmul(a, marlin_w_ref)
results.append(
benchmark.Timer(
stmt="torch.matmul(a, marlin_w_ref)",
globals=globals,
label=label,
sub_label=sub_label,
description="pytorch_gemm",
).blocked_autorange(min_run_time=min_run_time)
)
results.append(
benchmark.Timer(
stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_gemm",
).blocked_autorange(min_run_time=min_run_time)
)
results.append(
benchmark.Timer(
stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_gemm_fp32",
).blocked_autorange(min_run_time=min_run_time)
)
if marlin_24_supported:
results.append(
benchmark.Timer(
stmt="output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_24_gemm",
).blocked_autorange(min_run_time=min_run_time)
)
if repack_supported:
results.append(
benchmark.Timer(
stmt="q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_repack",
).blocked_autorange(min_run_time=min_run_time)
)
if allspark_supported:
results.append(
benchmark.Timer(
stmt="output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="allspark_w8a16_gemm_fp32",
).blocked_autorange(min_run_time=min_run_time)
)
def main(args):
print("Benchmarking models:")
for i, model in enumerate(args.models):
print(f"[{i}] {model}")
results: list[benchmark.Measurement] = []
for model in args.models:
for layer in WEIGHT_SHAPES[model]:
size_k = layer[0]
size_n = layer[1]
if len(args.limit_k) > 0 and size_k not in args.limit_k:
continue
if len(args.limit_n) > 0 and size_n not in args.limit_n:
continue
for act_order in ACT_ORDER_OPTS:
if (
len(args.limit_act_order) > 0
and act_order not in args.limit_act_order
):
continue
for is_k_full in K_FULL_OPTS:
if (
len(args.limit_k_full) > 0
and is_k_full not in args.limit_k_full
):
continue
for quant_type in query_marlin_supported_quant_types():
if (
len(args.limit_num_bits) > 0
and quant_type.size_bits not in args.limit_num_bits
):
continue
for group_size in (
MARLIN_SUPPORTED_GROUP_SIZES
+ FP4_MARLIN_SUPPORTED_GROUP_SIZES
):
if (
len(args.limit_group_size) > 0
and group_size not in args.limit_group_size
):
continue
# For act_order, the group_size must be less than
# size_k
if act_order and (group_size == size_k or group_size == -1):
continue
for size_m in args.batch_sizes:
bench_run(
results,
model,
act_order,
is_k_full,
quant_type,
group_size,
size_m,
size_k,
size_n,
)
compare = benchmark.Compare(results)
compare.print()
# For quick benchmarking use:
# python benchmark_marlin.py --batch-sizes 1 16 32 --limit-k 4096 --limit-n 4096 --limit-group-size 128 --limit-num-bits 4 --limit-act-order 0 --limit-k-full 1 # noqa E501
#
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark Marlin across specified models/shapes/batches"
)
parser.add_argument(
"--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys(),
)
parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
parser.add_argument("--limit-k", nargs="+", type=int, default=[])
parser.add_argument("--limit-n", nargs="+", type=int, default=[])
parser.add_argument("--limit-group-size", nargs="+", type=int, default=[])
parser.add_argument("--limit-num-bits", nargs="+", type=int, default=[])
parser.add_argument("--limit-act-order", nargs="+", type=int, default=[])
parser.add_argument("--limit-k-full", nargs="+", type=int, default=[])
args = parser.parse_args()
main(args)
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