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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import glob
import itertools
import os
import subprocess
import jinja2
FILE_HEAD = """
// auto generated by generate.py
// clang-format off
#include "kernel.h"
#include "marlin_template.h"
namespace MARLIN_NAMESPACE_NAME {
""".strip()
TEMPLATE = ("template __global__ void Marlin<"
"{{scalar_t}}, "
"{{w_type_id}}, "
"{{threads}}, "
"{{thread_m_blocks}}, "
"{{thread_n_blocks}}, "
"{{thread_k_blocks}}, "
"{{'true' if m_block_size_8 else 'false'}}, "
"{{stages}}, "
"{{group_blocks}}, "
"{{'true' if is_zp_float else 'false'}}>"
"( MARLIN_KERNEL_PARAMS );")
# int8 with zero point case (vllm::kU8) is also supported,
# we don't add it to reduce wheel size.
SCALAR_TYPES = [
"vllm::kU4", "vllm::kU4B8", "vllm::kU8B128", "vllm::kFE4M3fn",
"vllm::kFE2M1f"
]
THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128),
(128, 64, 128)]
THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
# group_blocks:
# = 0 : act order case
# = -1 : channelwise quantization
# > 0 : group_size=16*group_blocks
GROUP_BLOCKS = [0, 1, -1, 2, 4, 8]
DTYPES = ["fp16", "bf16"]
def remove_old_kernels():
for filename in glob.glob(os.path.dirname(__file__) + "/kernel_*.cu"):
subprocess.call(["rm", "-f", filename])
def generate_new_kernels():
for scalar_type, dtype in itertools.product(SCALAR_TYPES, DTYPES):
all_template_str_list = []
for group_blocks, m_blocks, thread_configs in itertools.product(
GROUP_BLOCKS, THREAD_M_BLOCKS, THREAD_CONFIGS):
# act order case only support gptq-int4 and gptq-int8
if group_blocks == 0 and scalar_type not in [
"vllm::kU4B8", "vllm::kU8B128"
]:
continue
if thread_configs[2] == 256:
# for small batch (m_blocks == 1), we only need (128, 128, 256)
# for large batch (m_blocks > 1), we only need (64, 256, 256)
if m_blocks <= 1 and thread_configs[0] != 128:
continue
if m_blocks > 1 and thread_configs[0] != 64:
continue
# we only support channelwise quantization and group_size == 128
# for fp8
if scalar_type == "vllm::kFE4M3fn" and group_blocks not in [-1, 8]:
continue
# nvfp4 only supports group_size == 16
if scalar_type == "vllm::kFE2M1f" and group_blocks != 1:
continue
# other quantization methods don't support group_size = 16
if scalar_type != "vllm::kFE2M1f" and group_blocks == 1:
continue
k_blocks = thread_configs[0] // 16
n_blocks = thread_configs[1] // 16
threads = thread_configs[2]
c_dtype = "half" if dtype == "fp16" else "nv_bfloat16"
is_zp_float_list = [False]
if dtype == "fp16" and scalar_type == "vllm::kU4" and \
group_blocks == 4:
# HQQ (is_zp_float = true) only supports
# 4bit quantization and fp16
is_zp_float_list.append(True)
for is_zp_float in is_zp_float_list:
template_str = jinja2.Template(TEMPLATE).render(
scalar_t=c_dtype,
w_type_id=scalar_type + ".id()",
threads=threads,
thread_m_blocks=max(m_blocks, 1),
thread_n_blocks=n_blocks,
thread_k_blocks=k_blocks,
m_block_size_8=m_blocks == 0.5,
stages="pipe_stages",
group_blocks=group_blocks,
is_zp_float=is_zp_float,
)
all_template_str_list.append(template_str)
file_content = FILE_HEAD + "\n\n"
file_content += "\n\n".join(all_template_str_list) + "\n\n}\n"
filename = f"kernel_{dtype}_{scalar_type[6:].lower()}.cu"
with open(os.path.join(os.path.dirname(__file__), filename), "w") as f:
f.write(file_content)
if __name__ == "__main__":
remove_old_kernels()
generate_new_kernels()
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