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Add GPTQ Marlin 2:4 sparse structured support (#4790)

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Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
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Alexander Matveev 2024-05-16 12:56:15 -04:00 committed by GitHub
parent 9216b9cc38
commit 6979ade384
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18 changed files with 2130 additions and 40 deletions
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3
CMakeLists.txt
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@ -176,7 +176,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
list(APPEND VLLM_EXT_SRC
"csrc/quantization/aqlm/gemm_kernels.cu"
"csrc/quantization/awq/gemm_kernels.cu"
"csrc/quantization/marlin/marlin_cuda_kernel.cu"
"csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
"csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
"csrc/quantization/gptq_marlin/gptq_marlin.cu"
"csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
"csrc/custom_all_reduce.cu")

11
csrc/ops.h
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@ -125,6 +125,17 @@ torch::Tensor marlin_gemm(
int64_t size_n,
int64_t size_k);
torch::Tensor gptq_marlin_24_gemm(
torch::Tensor &a,
torch::Tensor &b_q_weight,
torch::Tensor &b_meta,
torch::Tensor &b_scales,
torch::Tensor &workspace,
int64_t num_bits,
int64_t size_m,
int64_t size_n,
int64_t size_k);
torch::Tensor gptq_marlin_gemm(
torch::Tensor &a,
torch::Tensor &b_q_weight,

3
csrc/pybind.cpp
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@ -66,7 +66,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
ops.def("aqlm_gemm", &aqlm_gemm, "Quantized GEMM for AQLM");
ops.def("aqlm_dequant", &aqlm_dequant, "Decompression method for AQLM");
ops.def("awq_gemm", &awq_gemm, "Quantized GEMM for AWQ");
ops.def("marlin_gemm", &marlin_gemm, "Marlin Optimized Quantized GEMM for GPTQ");
ops.def("marlin_gemm", &marlin_gemm, "Marlin (Dense) Optimized Quantized GEMM for GPTQ");
ops.def("gptq_marlin_24_gemm", &gptq_marlin_24_gemm, "Marlin_24 (Sparse) Optimized Quantized GEMM for GPTQ");
ops.def("gptq_marlin_gemm", &gptq_marlin_gemm, "gptq_marlin Optimized Quantized GEMM for GPTQ");
ops.def("gptq_marlin_repack", &gptq_marlin_repack, "gptq_marlin repack from GPTQ");
ops.def("awq_dequantize", &awq_dequantize, "Dequantization for AWQ");

0
csrc/quantization/marlin/LICENSE → csrc/quantization/marlin/dense/LICENSE
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0
csrc/quantization/marlin/marlin_cuda_kernel.cu → csrc/quantization/marlin/dense/marlin_cuda_kernel.cu
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203
csrc/quantization/marlin/sparse/LICENSE Normal file
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@ -0,0 +1,203 @@
Contains code from https://github.com/IST-DASLab/Sparse-Marlin/
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49
csrc/quantization/marlin/sparse/common/base.h Normal file
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@ -0,0 +1,49 @@
/*
* Copyright (C) 2024 Roberto Lopez Castro (roberto.lopez.castro@udc.es). All
* Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
namespace marlin_24 {
constexpr int ceildiv(int a, int b) { return (a + b - 1) / b; }
// Instances of `Vec` are used to organize groups of >>registers<<, as needed
// for instance as inputs to tensor core operations. Consequently, all
// corresponding index accesses must be compile-time constants, which is why we
// extensively use `#pragma unroll` throughout the kernel code to guarantee
// this.
template <typename T, int n> struct Vec {
T elems[n];
__device__ T &operator[](int i) { return elems[i]; }
};
template <int M_, int N_, int K_> struct ShapeBase {
static constexpr int M = M_, N = N_, K = K_;
};
using I4 = Vec<int, 4>;
// Matrix fragments for tensor core instructions; their precise layout is
// documented here:
// https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type
using FragA = Vec<half2, 4>;
using FragB = Vec<half2, 2>;
using FragM = Vec<uint, 1>;
using FragC = Vec<float, 4>;
using FragS = Vec<half2, 1>; // quantization scales
} // namespace marlin_24

132
csrc/quantization/marlin/sparse/common/mem.h Normal file
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@ -0,0 +1,132 @@
/*
* Copyright (C) 2024 Roberto Lopez Castro (roberto.lopez.castro@udc.es). All
* Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include "base.h"
namespace marlin_24 {
// Predicated asynchronous global->shared copy; used for inputs A where we apply
// predication to handle batchsizes that are not multiples of 16.
__device__ inline void cp_async4_pred_zfill(void *smem_ptr,
const void *glob_ptr,
bool pred = true,
const bool zfill = false) {
const int BYTES = 16;
int src_in_bytes = (zfill ? 0 : BYTES);
uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
asm volatile("{\n"
" .reg .pred p;\n"
" setp.ne.b32 p, %0, 0;\n"
" @p cp.async.cg.shared.global [%1], [%2], %3;\n"
"}\n" ::"r"((int)pred),
"r"(smem), "l"(glob_ptr), "n"(BYTES), "r"(src_in_bytes));
}
__device__ inline void cp_async4_pred(void *smem_ptr, const void *glob_ptr,
bool pred = true) {
const int BYTES = 16;
uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
asm volatile("{\n"
" .reg .pred p;\n"
" setp.ne.b32 p, %0, 0;\n"
" @p cp.async.cg.shared.global [%1], [%2], %3;\n"
"}\n" ::"r"((int)pred),
"r"(smem), "l"(glob_ptr), "n"(BYTES));
}
// Asynchronous global->shared copy
__device__ inline void cp_async4(void *smem_ptr, const void *glob_ptr) {
const int BYTES = 16;
uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
asm volatile("{\n"
" cp.async.cg.shared.global [%0], [%1], %2;\n"
"}\n" ::"r"(smem),
"l"(glob_ptr), "n"(BYTES));
}
// Async copy fence.
__device__ inline void cp_async_fence() {
asm volatile("cp.async.commit_group;\n" ::);
}
// Wait until at most `n` async copy stages are still pending.
template <int n> __device__ inline void cp_async_wait() {
asm volatile("cp.async.wait_group %0;\n" ::"n"(n));
}
// Instruction for loading a full 16x16 matrix fragment of operand A from shared
// memory, directly in tensor core layout.
__device__ inline void ldsm4(FragA &frag_a, const void *smem_ptr) {
uint32_t *a = reinterpret_cast<uint32_t *>(&frag_a);
uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
asm volatile("ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];\n"
: "=r"(a[0]), "=r"(a[1]), "=r"(a[2]), "=r"(a[3])
: "r"(smem));
}
__device__ inline void ldsm4_m(FragM &frag_m, const void *smem_ptr) {
uint32_t *a = reinterpret_cast<uint32_t *>(&frag_m);
uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
asm volatile("ldmatrix.sync.aligned.m8n8.x2.shared.b16 {%0,%1}, [%2];\n"
: "=r"(a[0]), "=r"(a[1])
: "r"(smem));
}
// Instruction for loading a full 16x16 matrix fragment of operand A from shared
// memory, directly in tensor core layout.
__device__ inline void ldsm4_t(FragA &frag_a, const void *smem_ptr) {
uint32_t *a = reinterpret_cast<uint32_t *>(&frag_a);
uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
asm volatile(
"ldmatrix.sync.aligned.m8n8.x4.trans.shared.b16 {%0,%1,%2,%3}, [%4];\n"
: "=r"(a[0]), "=r"(a[1]), "=r"(a[2]), "=r"(a[3])
: "r"(smem));
}
// Wait until barrier reaches `count`, then lock for current threadblock.
__device__ inline void barrier_acquire(int *lock, int count) {
if (threadIdx.x == 0) {
int state = -1;
do
// Guarantee that subsequent writes by this threadblock will be visible
// globally.
asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n"
: "=r"(state)
: "l"(lock));
while (state != count);
}
__syncthreads();
}
// Release barrier and increment visitation count.
__device__ inline void barrier_release(int *lock, bool reset = false) {
__syncthreads();
if (threadIdx.x == 0) {
if (reset) {
lock[0] = 0;
return;
}
int val = 1;
// Make sure that all writes since acquiring this barrier are visible
// globally, while releasing the barrier.
asm volatile("fence.acq_rel.gpu;\n");
asm volatile("red.relaxed.gpu.global.add.s32 [%0], %1;\n"
:
: "l"(lock), "r"(val));
}
}
} // namespace marlin_24

175
csrc/quantization/marlin/sparse/common/mma.h Normal file
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@ -0,0 +1,175 @@
/*
* Copyright (C) 2024 Roberto Lopez Castro (roberto.lopez.castro@udc.es). All
* Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include "base.h"
namespace marlin_24 {
// m16n8k32 sparse tensor core mma instruction with fp16 inputs and fp32
// output/accumulation.
__device__ inline void mma_sp(const FragB &a_frag0, const FragB &a_frag1,
const FragA &frag_b, FragC &frag_c, FragM &frag_m,
const int psel) {
const uint32_t *a0 = reinterpret_cast<const uint32_t *>(&a_frag0);
const uint32_t *a1 = reinterpret_cast<const uint32_t *>(&a_frag1);
const uint32_t *b = reinterpret_cast<const uint32_t *>(&frag_b);
const uint32_t *e = reinterpret_cast<const uint32_t *>(&frag_m);
float *c = reinterpret_cast<float *>(&frag_c);
if (psel == 0) {
asm volatile("mma.sp.sync.aligned.m16n8k32.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9, %10,%11}, "
"{%12,%13,%14,%15}, %16, 0x0;\n"
: "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3])
: "r"(a0[0]), "r"(a1[0]), "r"(a0[1]), "r"(a1[1]), "r"(b[0]),
"r"(b[2]), "r"(b[4]), "r"(b[6]), "f"(c[0]), "f"(c[1]),
"f"(c[2]), "f"(c[3]), "r"(e[0]));
asm volatile("mma.sp.sync.aligned.m16n8k32.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9, %10,%11}, "
"{%12,%13,%14,%15}, %16, 0x0;\n"
: "=f"(c[4]), "=f"(c[5]), "=f"(c[6]), "=f"(c[7])
: "r"(a0[0]), "r"(a1[0]), "r"(a0[1]), "r"(a1[1]), "r"(b[1]),
"r"(b[3]), "r"(b[5]), "r"(b[7]), "f"(c[4]), "f"(c[5]),
"f"(c[6]), "f"(c[7]), "r"(e[0]));
} else {
asm volatile("mma.sp.sync.aligned.m16n8k32.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9, %10,%11}, "
"{%12,%13,%14,%15}, %16, 0x1;\n"
: "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3])
: "r"(a0[0]), "r"(a1[0]), "r"(a0[1]), "r"(a1[1]), "r"(b[0]),
"r"(b[2]), "r"(b[4]), "r"(b[6]), "f"(c[0]), "f"(c[1]),
"f"(c[2]), "f"(c[3]), "r"(e[0]));
asm volatile("mma.sp.sync.aligned.m16n8k32.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9, %10,%11}, "
"{%12,%13,%14,%15}, %16, 0x1;\n"
: "=f"(c[4]), "=f"(c[5]), "=f"(c[6]), "=f"(c[7])
: "r"(a0[0]), "r"(a1[0]), "r"(a0[1]), "r"(a1[1]), "r"(b[1]),
"r"(b[3]), "r"(b[5]), "r"(b[7]), "f"(c[4]), "f"(c[5]),
"f"(c[6]), "f"(c[7]), "r"(e[0]));
}
}
// Lookup-table based 3-input logical operation; explicitly used for
// dequantization as the compiler does not seem to automatically recognize it in
// all cases.
template <int lut> __device__ inline int lop3(int a, int b, int c) {
int res;
asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
: "=r"(res)
: "r"(a), "r"(b), "r"(c), "n"(lut));
return res;
}
__device__ __forceinline__ uint2 to_half4(float c0, float c1, float c2,
float c3) {
uint2 r;
asm("{\n\t"
".reg .f16 a, b, c, d; \n\t"
"cvt.rn.f16.f32 a, %2; \n\t"
"cvt.rn.f16.f32 b, %3; \n\t"
"cvt.rn.f16.f32 c, %4; \n\t"
"cvt.rn.f16.f32 d, %5; \n\t"
"mov.b32 %0, {a, b}; \n\t"
"mov.b32 %1, {c, d}; \n\t"
"}"
: "=r"(r.x), "=r"(r.y)
: "f"(c0), "f"(c1), "f"(c2), "f"(c3));
return r;
}
// Constructs destination register by taking bytes from 2 sources (based on
// mask)
template <int start_byte, int mask>
__device__ inline uint32_t prmt(uint32_t a) {
uint32_t res;
asm volatile("prmt.b32 %0, %1, %2, %3;\n"
: "=r"(res)
: "r"(a), "n"(start_byte), "n"(mask));
return res;
}
// Efficiently dequantize an int32 value into a full B-fragment of 4 fp16
// values. We mostly follow the strategy in the link below, with some small
// changes:
// https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
__device__ inline FragB dequant_4bit(int q) {
const int LO = 0x000f000f;
const int HI = 0x00f000f0;
const int EX = 0x64006400;
// Guarantee that the `(a & b) | c` operations are LOP3s.
int lo = lop3<(0xf0 & 0xcc) | 0xaa>(q, LO, EX);
int hi = lop3<(0xf0 & 0xcc) | 0xaa>(q, HI, EX);
// We want signed int4 outputs, hence we fuse the `-8` symmetric zero point
// directly into `SUB` and `ADD`.
const int SUB = 0x64086408;
const int MUL = 0x2c002c00;
const int ADD = 0xd480d480;
FragB frag_b;
frag_b[0] = __hsub2(*reinterpret_cast<half2 *>(&lo),
*reinterpret_cast<const half2 *>(&SUB));
frag_b[1] = __hfma2(*reinterpret_cast<half2 *>(&hi),
*reinterpret_cast<const half2 *>(&MUL),
*reinterpret_cast<const half2 *>(&ADD));
return frag_b;
}
// Efficiently dequantize an int32 value into a full B-fragment of 4 fp16
// values. We mostly follow the strategy in the link below, with some small
// changes:
// https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
__device__ inline FragB dequant_8bit(int q) {
static constexpr uint32_t mask_for_elt_01 = 0x5250;
static constexpr uint32_t mask_for_elt_23 = 0x5351;
static constexpr uint32_t start_byte_for_fp16 = 0x64646464;
uint32_t lo = prmt<start_byte_for_fp16, mask_for_elt_01>(q);
uint32_t hi = prmt<start_byte_for_fp16, mask_for_elt_23>(q);
static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64806480;
FragB frag_b;
frag_b[0] = __hsub2(*reinterpret_cast<half2 *>(&lo),
*reinterpret_cast<const half2 *>(&I8s_TO_F16s_MAGIC_NUM));
frag_b[1] = __hsub2(*reinterpret_cast<half2 *>(&hi),
*reinterpret_cast<const half2 *>(&I8s_TO_F16s_MAGIC_NUM));
return frag_b;
}
// Multiply dequantized values by the corresponding quantization scale; used
// only for grouped quantization.
__device__ inline void scale(FragB &frag_b, FragS &frag_s, int i) {
half2 s = __half2half2(reinterpret_cast<__half *>(&frag_s)[i]);
frag_b[0] = __hmul2(frag_b[0], s);
frag_b[1] = __hmul2(frag_b[1], s);
}
__device__ inline void scale_floats(float *c0, float *c1, float *c2, float *c3,
FragS &s0, float *c4, float *c5, float *c6,
float *c7, FragS &s1) {
*c0 = __fmul_rn(*c0, __half2float(s0[0].x));
*c1 = __fmul_rn(*c1, __half2float(s0[0].y));
*c2 = __fmul_rn(*c2, __half2float(s0[1].x));
*c3 = __fmul_rn(*c3, __half2float(s0[1].y));
*c4 = __fmul_rn(*c4, __half2float(s1[0].x));
*c5 = __fmul_rn(*c5, __half2float(s1[0].y));
*c6 = __fmul_rn(*c6, __half2float(s1[1].x));
*c7 = __fmul_rn(*c7, __half2float(s1[1].y));
}
} // namespace marlin_24

1110
csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu Normal file
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81
tests/models/test_gptq_marlin_24.py Normal file
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@ -0,0 +1,81 @@
"""Compare the outputs of a GPTQ model to a Marlin_24 model.
Note: GPTQ and Marlin_24 do not have bitwise correctness.
As a result, in this test, we just confirm that the top selected tokens of the
Marlin/GPTQ models are in the top 3 selections of each other.
Run `pytest tests/models/test_marlin_24.py`.
"""
from dataclasses import dataclass
import pytest
import torch
from tests.models.utils import check_logprobs_close
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
marlin_not_supported = (capability <
QUANTIZATION_METHODS["marlin"].get_min_capability())
@dataclass
class ModelPair:
model_marlin: str
model_gptq: str
model_pairs = [
# 4-bit, group_size == 128
ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-4bit-g128",
model_gptq="alexm-nm/tinyllama-24-gptq-4bit-g128"),
# 4-bit, group_size == channelwise
ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-4bit-channelwise",
model_gptq="alexm-nm/tinyllama-24-gptq-4bit-channelwise"),
# 8-bit, group_size == 128
ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-8bit-g128",
model_gptq="alexm-nm/tinyllama-24-gptq-8bit-g128"),
# 8-bit, group_size == channelwise
ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-8bit-channelwise",
model_gptq="alexm-nm/tinyllama-24-gptq-8bit-channelwise"),
]
@pytest.mark.flaky(reruns=2)
@pytest.mark.skipif(marlin_not_supported,
reason="Marlin24 is not supported on this GPU type.")
@pytest.mark.parametrize("model_pair", model_pairs)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [8])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
vllm_runner,
example_prompts,
model_pair: ModelPair,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
marlin_24_model = vllm_runner(model_pair.model_marlin,
dtype=dtype,
quantization="gptq_marlin_24")
marlin_24_outputs = marlin_24_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)
del marlin_24_model
gptq_model = vllm_runner(model_pair.model_gptq,
dtype=dtype,
quantization="gptq")
gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
max_tokens,
num_logprobs)
del gptq_model
check_logprobs_close(
outputs_0_lst=gptq_outputs,
outputs_1_lst=marlin_24_outputs,
name_0="gptq",
name_1="marlin_24",
)

10
vllm/_custom_ops.py
View File

@ -153,6 +153,16 @@ def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
size_n, size_k)
# marlin_24
def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
b_meta: torch.Tensor, b_scales: torch.Tensor,
workspace: torch.Tensor, num_bits: int, size_m: int,
size_n: int, size_k: int) -> torch.Tensor:
return vllm_ops.gptq_marlin_24_gemm(a, b_q_weight, b_meta, b_scales,
workspace, num_bits, size_m, size_n,
size_k)
# aqlm
def aqlm_gemm(input: torch.Tensor, codes: torch.Tensor,
codebooks: torch.Tensor, scales: torch.Tensor,

46
vllm/config.py
View File

@ -7,14 +7,11 @@ import torch
from transformers import PretrainedConfig
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS,
get_quantization_config)
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from vllm.model_executor.models import ModelRegistry
from vllm.transformers_utils.config import get_config, get_hf_text_config
from vllm.utils import get_cpu_memory, is_cpu, is_hip, is_neuron
GPTQMarlinConfig = get_quantization_config("gptq_marlin")
if TYPE_CHECKING:
from ray.util.placement_group import PlacementGroup
@ -155,37 +152,15 @@ class ModelConfig:
quant_cfg = getattr(self.hf_config, "quantization_config", None)
if quant_cfg is not None:
quant_method = quant_cfg.get("quant_method", "").lower()
# compat: autogptq >=0.8.0 use checkpoint_format: str
# compat: autogptq <=0.7.1 is_marlin_format: bool
is_format_marlin = (quant_cfg.get("checkpoint_format") == "marlin"
or quant_cfg.get("is_marlin_format", False))
# Check which LinearMethod the GPTQ model should use.
if quant_method == "gptq":
# If serialized in Marlin format, use MarlinLinearMethod.
# TODO (@robertgshaw): migrate under GPTQMarlinLinearMethod.
if is_format_marlin:
logger.info("The model is serialized in Marlin format. "
"Using Marlin kernel.")
quant_method = "marlin"
if self.quantization == "gptq":
self.quantization = quant_method
# If convertible to Marlin format, use GPTQMarlinLinearMethod
# unless the user explicitly specified GPTQLinearMethod.
elif GPTQMarlinConfig.is_marlin_compatible(quant_cfg):
if self.quantization == "gptq":
logger.warning(
"The model is convertible to Marlin format, but "
"you specified quantization=gptq. Use "
"quantization=marlin for faster inference.")
else:
logger.info(
"The model is convertible to Marlin format. "
"Using Marlin kernel.")
quant_method = "gptq_marlin"
if self.quantization == "marlin":
self.quantization = quant_method
# Detect which checkpoint is it
for name, method in QUANTIZATION_METHODS.items():
quantization_override = method.override_quantization_method(
quant_cfg, self.quantization)
if quantization_override:
quant_method = quantization_override
self.quantization = quantization_override
break
# Verify quantization configurations.
if self.quantization is None:
@ -207,7 +182,8 @@ class ModelConfig:
raise ValueError(
f"{self.quantization} quantization is currently not "
f"supported in ROCm.")
if (self.quantization not in ["marlin", "gptq_marlin"]):
if (self.quantization
not in ["marlin", "gptq_marlin_24", "gptq_marlin"]):
logger.warning(
"%s quantization is not fully "
"optimized yet. The speed can be slower than "

11
vllm/model_executor/layers/quantization/__init__.py
View File

@ -10,18 +10,23 @@ from vllm.model_executor.layers.quantization.fp8 import Fp8Config
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQMarlinConfig)
from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
GPTQMarlin24Config)
from vllm.model_executor.layers.quantization.marlin import MarlinConfig
from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
"aqlm": AQLMConfig,
"awq": AWQConfig,
"deepspeedfp": DeepSpeedFPConfig,
"fp8": Fp8Config,
# The order of gptq methods is important for config.py iteration over
# override_quantization_method(..)
"marlin": MarlinConfig,
"gptq_marlin_24": GPTQMarlin24Config,
"gptq_marlin": GPTQMarlinConfig,
"gptq": GPTQConfig,
"squeezellm": SqueezeLLMConfig,
"gptq_marlin": GPTQMarlinConfig,
"marlin": MarlinConfig,
"deepspeedfp": DeepSpeedFPConfig
}

11
vllm/model_executor/layers/quantization/base_config.py
View File

@ -66,6 +66,17 @@ class QuantizationConfig(ABC):
"""Create a config class from the model's quantization config."""
raise NotImplementedError
@classmethod
def override_quantization_method(cls, hf_quant_cfg,
user_quant) -> Optional[str]:
"""
Detects if this quantization method can support a given checkpoint
format by overriding the user specified quantization method --
this method should only be overwritten by subclasses in exceptional
circumstances
"""
return None
@staticmethod
def get_from_keys(config: Dict[str, Any], keys: List[str]) -> Any:
"""Get a value from the model's quantization config."""

23
vllm/model_executor/layers/quantization/gptq_marlin.py
View File

@ -6,11 +6,14 @@ import torch
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
set_weight_attrs)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
logger = init_logger(__name__)
GPTQ_MARLIN_TILE = 16
GPTQ_MARLIN_MIN_THREAD_N = 64
GPTQ_MARLIN_MIN_THREAD_K = 128
@ -117,6 +120,26 @@ class GPTQMarlinConfig(QuantizationConfig):
is_sym = cls.get_from_keys(config, ["sym"])
return cls(weight_bits, group_size, desc_act, is_sym)
@classmethod
def override_quantization_method(cls, hf_quant_cfg,
user_quant) -> Optional[str]:
can_convert = cls.is_marlin_compatible(hf_quant_cfg)
is_valid_user_quant = (user_quant is None or user_quant == "marlin")
if can_convert and is_valid_user_quant:
msg = ("The model is convertible to {} during runtime."
" Using {} kernel.".format(cls.get_name(), cls.get_name()))
logger.info(msg)
return cls.get_name()
if can_convert and user_quant == "gptq":
logger.info("Detected that the model can run with gptq_marlin"
", however you specified quantization=gptq explicitly,"
" so forcing gptq. Use quantization=gptq_marlin for"
" faster inference")
return None
def get_quant_method(
self,
layer: torch.nn.Module) -> Optional["GPTQMarlinLinearMethod"]:

280
vllm/model_executor/layers/quantization/gptq_marlin_24.py Normal file
View File

@ -0,0 +1,280 @@
from typing import Any, Dict, List, Optional
import torch
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.utils import set_weight_attrs
logger = init_logger(__name__)
class GPTQMarlin24Config(QuantizationConfig):
"""Config class for Marlin24.
"""
def __init__(
self,
weight_bits: int,
group_size: int,
) -> None:
self.weight_bits = weight_bits
self.group_size = group_size
if self.weight_bits != 4 and self.weight_bits != 8:
raise ValueError("weight_bits must be 4 or 8. Got = {}".format(
self.weight_bits))
if self.group_size != 128 and self.group_size != -1:
raise ValueError(
"Currently, only group size 128 and -1 (channelwise) "
"is supported for Marlin24, but got group_size of "
f"{self.group_size}")
# 4 Bits packed into 32 bit datatype.
self.pack_factor = 32 // self.weight_bits
# Tile size used by marlin kernels.
self.tile_size = 16
# Min out_features dim
self.min_n_threads = 128
# Min in_features dim
self.min_k_threads = 128
# Max parallel problems to solve at once (improves large
# batch performance)
self.max_parallel = 16
# Permutation length used by the marlin kernels.
self.perm_len = 1024
def __repr__(self) -> str:
return "Marlin24Config(weight_bits={}, group_size={})".format(
self.weight_bits, self.group_size)
@classmethod
def get_name(cls) -> str:
return "gptq_marlin_24"
@classmethod
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
return [torch.half]
@classmethod
# Need to figure it out
def get_min_capability(cls) -> int:
return 80
@classmethod
def get_config_filenames(cls) -> List[str]:
return ["quantize_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlin24Config":
weight_bits = cls.get_from_keys(config, ["bits"])
group_size = cls.get_from_keys(config, ["group_size"])
return cls(weight_bits, group_size)
@classmethod
def override_quantization_method(cls, hf_quant_cfg,
user_quant) -> Optional[str]:
is_marlin_24_format = (
hf_quant_cfg.get("checkpoint_format") == "marlin_24")
is_valid_user_quant = (user_quant is None or user_quant == "gptq"
or user_quant == "gptq_marlin_24")
if is_marlin_24_format and is_valid_user_quant:
msg = ("The model is serialized in {} format. "
"Using {} kernel.".format(cls.get_name(), cls.get_name()))
logger.info(msg)
return cls.get_name()
return None
def get_quant_method(
self,
layer: torch.nn.Module) -> Optional["GPTQMarlin24LinearMethod"]:
if isinstance(layer, LinearBase):
return GPTQMarlin24LinearMethod(self)
return None
def get_scaled_act_names(self) -> List[str]:
return []
class GPTQMarlin24LinearMethod(LinearMethodBase):
"""Linear method for Marlin24.
Args:
quant_config: The Marlin24 quantization config.
"""
def __init__(self, quant_config: GPTQMarlin24Config):
self.quant_config = quant_config
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: List[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
del output_size # Unused.
if params_dtype != torch.float16:
raise ValueError(
f"The params dtype must be float16, but got {params_dtype}")
# Validate output_size_per_partition
output_size_per_partition = sum(output_partition_sizes)
if output_size_per_partition % self.quant_config.min_n_threads != 0:
raise ValueError(
f"Weight output_size_per_partition = "
f"{output_size_per_partition} is not divisible by "
f"min_n_threads = {self.quant_config.min_n_threads}.")
if output_size_per_partition % self.quant_config.pack_factor != 0:
raise ValueError(
f"Weight output_size_per_partition = "
f"{output_size_per_partition} is not divisible by "
f"pack_factor = {self.quant_config.pack_factor}.")
# Validate input_size_per_partition
if input_size_per_partition % self.quant_config.min_k_threads != 0:
raise ValueError(
f"Weight input_size_per_partition = "
f"{input_size_per_partition} is not divisible by "
f"min_k_threads = {self.quant_config.min_k_threads}.")
if (self.quant_config.group_size != -1 and
input_size_per_partition % self.quant_config.group_size != 0):
raise ValueError(f"Weight input_size_per_partition = "
f"{input_size_per_partition} is not divisible by "
f"group_size = {self.quant_config.group_size}.")
# Check that we have at least 4 tiles horizontally in the shard
num_tiles_per_perm = self.quant_config.perm_len // (
self.quant_config.tile_size**2)
if output_size_per_partition % num_tiles_per_perm != 0:
raise ValueError(
"Each permutation group must reside on the same gpu")
# Quantized 4Bit weights packed into Int32.
qweight = Parameter(
torch.empty(
input_size_per_partition // self.quant_config.tile_size // 2,
output_size_per_partition * self.quant_config.tile_size //
self.quant_config.pack_factor,
device="cuda",
dtype=torch.int32,
),
requires_grad=False,
)
set_weight_attrs(
qweight,
{
"input_dim": 0,
"output_dim": 1,
"packed_dim": 1,
"pack_factor": self.quant_config.pack_factor,
"marlin_tile_size": self.quant_config.tile_size,
},
)
# Meta
meta = Parameter(
torch.empty(
input_size_per_partition // 8 // 2 // 2,
output_size_per_partition * 2,
device="cuda",
dtype=torch.int16,
),
requires_grad=False,
)
set_weight_attrs(
meta,
{
"input_dim": 0,
"packed_dim": 1,
"pack_factor": 1,
"output_dim": 1,
"marlin_tile_size": 2,
},
)
# Determine if channelwise or not
input_groups = (1 if self.quant_config.group_size == -1 else
input_size_per_partition //
self.quant_config.group_size)
scales = Parameter(
torch.empty(
input_groups,
output_size_per_partition,
device="cuda",
dtype=params_dtype,
),
requires_grad=False,
)
set_weight_attrs(
scales,
{
"input_dim": None if input_groups == 1 else 0,
"output_dim": 1,
},
)
# Allocate workspace (Used for internal locking mechanism)
max_workspace_size = (
output_size_per_partition //
self.quant_config.min_n_threads) * self.quant_config.max_parallel
workspace = Parameter(torch.zeros(max_workspace_size,
device="cuda",
dtype=torch.int),
requires_grad=False)
layer.register_parameter("B_24", qweight)
set_weight_attrs(qweight, extra_weight_attrs)
layer.register_parameter("B_meta", meta)
set_weight_attrs(meta, extra_weight_attrs)
layer.register_parameter("s", scales)
set_weight_attrs(scales, extra_weight_attrs)
layer.register_parameter("workspace", workspace)
set_weight_attrs(workspace, extra_weight_attrs)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
qweight = layer.B_24
meta = layer.B_meta
scales = layer.s
workspace = layer.workspace
x_2d = x.view(-1, x.shape[-1])
size_m = x_2d.shape[0]
size_k = x_2d.shape[1]
size_n = scales.shape[1]
output_2d = ops.gptq_marlin_24_gemm(x_2d, qweight, meta, scales,
workspace,
self.quant_config.weight_bits,
size_m, size_n, size_k)
output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
if bias is not None:
output.add_(bias) # In-place add
return output

22
vllm/model_executor/layers/quantization/marlin.py
View File

@ -4,11 +4,14 @@ import torch
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.utils import set_weight_attrs
logger = init_logger(__name__)
class MarlinConfig(QuantizationConfig):
"""Config class for Marlin.
@ -72,6 +75,25 @@ class MarlinConfig(QuantizationConfig):
group_size = cls.get_from_keys(config, ["group_size"])
return cls(group_size)
@classmethod
def override_quantization_method(cls, hf_quant_cfg,
user_quant) -> Optional[str]:
# compat: autogptq >=0.8.0 use checkpoint_format: str
# compat: autogptq <=0.7.1 is_marlin_format: bool
is_marlin_format = (hf_quant_cfg.get("checkpoint_format") == "marlin"
or hf_quant_cfg.get("is_marlin_format", False))
is_valid_user_quant = (user_quant is None or user_quant == "gptq"
or user_quant == "marlin")
if is_marlin_format and is_valid_user_quant:
msg = ("The model is serialized in {} format. Using {} kernel.".
format(cls.get_name(), cls.get_name()))
logger.info(msg)
return cls.get_name()
return None
def get_quant_method(
self, layer: torch.nn.Module) -> Optional["MarlinLinearMethod"]:
if isinstance(layer, LinearBase):