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Unverified Commit 0cdbf5e6 authored by Michael Goin's avatar Michael Goin Committed by GitHub
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[Kernel/Quant] Remove the original marlin format and qqq (#23204) 


Signed-off-by: default avatarmgoin <mgoin64@gmail.com>
parent ebe56a00
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Showing with 95 additions and 2945 deletions
.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml deleted 100644 → 0
View file @ ebe56a00
# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
model_name: "HandH1998/QQQ-Llama-3-8b-g128"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.419
- name: "exact_match,flexible-extract"
value: 0.416
limit: 1000
num_fewshot: 5
.buildkite/lm-eval-harness/configs/models-large.txt
View file @ 0cdbf5e6
......@@ -3,4 +3,3 @@ Meta-Llama-3-70B-Instruct.yaml
Mixtral-8x7B-Instruct-v0.1.yaml
Qwen2-57B-A14-Instruct.yaml
DeepSeek-V2-Lite-Chat.yaml
Meta-Llama-3-8B-QQQ.yaml
CMakeLists.txt
View file @ 0cdbf5e6
......@@ -357,9 +357,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
set(MARLIN_SRCS
"csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
"csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
"csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
"csrc/quantization/gptq_marlin/gptq_marlin.cu"
"csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
"csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
......
benchmarks/kernels/benchmark_machete.py
View file @ 0cdbf5e6
......@@ -253,28 +253,7 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
else:
assert bt.a.dtype == torch.int8
assert bt.wtype == scalar_types.uint4b8
if bt.w_ch_s is not None:
s_ch = bt.w_ch_s.to(torch.float32)
else:
s_ch = torch.ones(bt.w_ref.shape[1], dtype=torch.float32, device=device)
if bt.w_tok_s is not None:
s_tok = bt.w_tok_s.to(torch.float32)
else:
s_tok = torch.ones(bt.a.shape[0], dtype=torch.float32, device=device)
fn = lambda: ops.marlin_qqq_gemm(
a=bt.a,
b_q_weight=w_q,
s_group=w_s,
s_tok=s_tok,
s_ch=s_ch,
workspace=workspace.scratch,
size_m=bt.a.shape[0],
size_n=bt.w_ref.shape[1],
size_k=bt.w_ref.shape[0],
)
raise NotImplementedError("QQQ is not supported anymore")
return fn
......
csrc/quantization/machete/generate.py
View file @ 0cdbf5e6
......@@ -571,78 +571,79 @@ def generate():
itertools.repeat(default_heuristic))
]
# Stored as "condition": ((tile_shape_mn), (cluster_shape_mnk))
# TODO (LucasWilkinson): Further tuning required
qqq_tile_heuristic_config = {
#### M = 257+
# ((128, 256), (2, 1, 1)) Broken for QQQ types
# TODO (LucasWilkinson): Investigate further
# "M > 256 && K <= 16384 && N <= 4096": ((128, 128), (2, 1, 1)),
# "M > 256": ((128, 256), (2, 1, 1)),
"M > 256": ((128, 128), (2, 1, 1)),
#### M = 129-256
"M > 128 && K <= 4096 && N <= 4096": ((128, 64), (2, 1, 1)),
"M > 128 && K <= 8192 && N <= 8192": ((128, 128), (2, 1, 1)),
# ((128, 256), (2, 1, 1)) Broken for QQQ types
# TODO (LucasWilkinson): Investigate further
# "M > 128": ((128, 256), (2, 1, 1)),
"M > 128": ((128, 128), (2, 1, 1)),
#### M = 65-128
"M > 64 && K <= 4069 && N <= 4069": ((128, 32), (2, 1, 1)),
"M > 64 && K <= 4069 && N <= 8192": ((128, 64), (2, 1, 1)),
"M > 64 && K >= 8192 && N >= 12288": ((256, 128), (2, 1, 1)),
"M > 64": ((128, 128), (2, 1, 1)),
#### M = 33-64
"M > 32 && K <= 6144 && N <= 6144": ((128, 16), (1, 1, 1)),
# Broken for QQQ types
# TODO (LucasWilkinson): Investigate further
#"M > 32 && K >= 16384 && N >= 12288": ((256, 64), (2, 1, 1)),
"M > 32": ((128, 64), (2, 1, 1)),
#### M = 17-32
"M > 16 && K <= 12288 && N <= 8192": ((128, 32), (2, 1, 1)),
"M > 16": ((256, 32), (2, 1, 1)),
#### M = 1-16
"N >= 26624": ((256, 16), (1, 1, 1)),
None: ((128, 16), (1, 1, 1)),
}
# For now we use the same heuristic for all types
# Heuristic is currently tuned for H100s
qqq_heuristic = [
(cond, ScheduleConfig(*tile_config,
**sch_common_params)) # type: ignore
for cond, tile_config in qqq_tile_heuristic_config.items()
]
QQQ_kernel_types = [
*(TypeConfig(
a=DataType.s8,
b=VLLMDataType.u4b8,
b_group_scale=b_group_scale,
b_group_zeropoint=DataType.void,
b_channel_scale=DataType.f32,
a_token_scale=DataType.f32,
out=DataType.f16,
accumulator=DataType.s32,
) for b_group_scale in (DataType.f16, DataType.void)),
*(TypeConfig(
a=DataType.e4m3,
b=VLLMDataType.u4b8,
b_group_scale=b_group_scale,
b_group_zeropoint=DataType.void,
b_channel_scale=DataType.f32,
a_token_scale=DataType.f32,
out=DataType.f16,
accumulator=DataType.f32,
) for b_group_scale in (DataType.f16, DataType.void)),
]
impl_configs += [
ImplConfig(x[0], x[1], x[2])
for x in zip(QQQ_kernel_types,
itertools.repeat(get_unique_schedules(qqq_heuristic)),
itertools.repeat(qqq_heuristic))
]
# TODO: Support W4A8 when ready
# # Stored as "condition": ((tile_shape_mn), (cluster_shape_mnk))
# # TODO (LucasWilkinson): Further tuning required
# qqq_tile_heuristic_config = {
# #### M = 257+
# # ((128, 256), (2, 1, 1)) Broken for QQQ types
# # TODO (LucasWilkinson): Investigate further
# # "M > 256 && K <= 16384 && N <= 4096": ((128, 128), (2, 1, 1)),
# # "M > 256": ((128, 256), (2, 1, 1)),
# "M > 256": ((128, 128), (2, 1, 1)),
# #### M = 129-256
# "M > 128 && K <= 4096 && N <= 4096": ((128, 64), (2, 1, 1)),
# "M > 128 && K <= 8192 && N <= 8192": ((128, 128), (2, 1, 1)),
# # ((128, 256), (2, 1, 1)) Broken for QQQ types
# # TODO (LucasWilkinson): Investigate further
# # "M > 128": ((128, 256), (2, 1, 1)),
# "M > 128": ((128, 128), (2, 1, 1)),
# #### M = 65-128
# "M > 64 && K <= 4069 && N <= 4069": ((128, 32), (2, 1, 1)),
# "M > 64 && K <= 4069 && N <= 8192": ((128, 64), (2, 1, 1)),
# "M > 64 && K >= 8192 && N >= 12288": ((256, 128), (2, 1, 1)),
# "M > 64": ((128, 128), (2, 1, 1)),
# #### M = 33-64
# "M > 32 && K <= 6144 && N <= 6144": ((128, 16), (1, 1, 1)),
# # Broken for QQQ types
# # TODO (LucasWilkinson): Investigate further
# #"M > 32 && K >= 16384 && N >= 12288": ((256, 64), (2, 1, 1)),
# "M > 32": ((128, 64), (2, 1, 1)),
# #### M = 17-32
# "M > 16 && K <= 12288 && N <= 8192": ((128, 32), (2, 1, 1)),
# "M > 16": ((256, 32), (2, 1, 1)),
# #### M = 1-16
# "N >= 26624": ((256, 16), (1, 1, 1)),
# None: ((128, 16), (1, 1, 1)),
# }
# # For now we use the same heuristic for all types
# # Heuristic is currently tuned for H100s
# qqq_heuristic = [
# (cond, ScheduleConfig(*tile_config,
# **sch_common_params)) # type: ignore
# for cond, tile_config in qqq_tile_heuristic_config.items()
# ]
# QQQ_kernel_types = [
# *(TypeConfig(
# a=DataType.s8,
# b=VLLMDataType.u4b8,
# b_group_scale=b_group_scale,
# b_group_zeropoint=DataType.void,
# b_channel_scale=DataType.f32,
# a_token_scale=DataType.f32,
# out=DataType.f16,
# accumulator=DataType.s32,
# ) for b_group_scale in (DataType.f16, DataType.void)),
# *(TypeConfig(
# a=DataType.e4m3,
# b=VLLMDataType.u4b8,
# b_group_scale=b_group_scale,
# b_group_zeropoint=DataType.void,
# b_channel_scale=DataType.f32,
# a_token_scale=DataType.f32,
# out=DataType.f16,
# accumulator=DataType.f32,
# ) for b_group_scale in (DataType.f16, DataType.void)),
# ]
# impl_configs += [
# ImplConfig(x[0], x[1], x[2])
# for x in zip(QQQ_kernel_types,
# itertools.repeat(get_unique_schedules(qqq_heuristic)),
# itertools.repeat(qqq_heuristic))
# ]
output_dir = os.path.join(SCRIPT_DIR, "generated")
......
csrc/quantization/marlin/dense/LICENSE deleted 100644 → 0
View file @ ebe56a00
Contains code from https://github.com/IST-DASLab/marlin
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csrc/quantization/marlin/dense/common/base.h deleted 100644 → 0
View file @ ebe56a00
/*
* Modified by HandH1998
* Modified by Neural Magic
* Copyright (C) Marlin.2024 Elias Frantar
*
* 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
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]; }
};
csrc/quantization/marlin/dense/common/mem.h deleted 100644 → 0
View file @ ebe56a00
/*
* Modified by HandH1998
* Modified by Neural Magic
* Copyright (C) Marlin.2024 Elias Frantar
*
* 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
// 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(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));
}
// 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));
}
}
csrc/quantization/marlin/dense/marlin_cuda_kernel.cu deleted 100644 → 0
View file @ ebe56a00
This diff is collapsed.
csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu deleted 100644 → 0
View file @ ebe56a00
This diff is collapsed.
csrc/torch_bindings.cpp
View file @ 0cdbf5e6
......@@ -241,14 +241,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// custom types:
// https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA
// Marlin (Dense) Optimized Quantized GEMM for GPTQ.
ops.def(
"marlin_gemm(Tensor a, Tensor b_q_weight, Tensor b_scales, "
"Tensor! workspace, SymInt size_m, SymInt size_n, SymInt size_k) -> "
"Tensor",
{stride_tag});
// conditionally compiled so impl in source file
// Marlin_24 (Sparse) Optimized Quantized GEMM for GPTQ.
ops.def(
"gptq_marlin_24_gemm(Tensor a, Tensor b_q_weight, Tensor b_meta, "
......@@ -353,15 +345,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.def("ggml_moe_get_block_size", &ggml_moe_get_block_size);
#ifndef USE_ROCM
// marlin_qqq_gemm for QQQ.
ops.def(
"marlin_qqq_gemm(Tensor a, Tensor b_q_weight, "
"Tensor s_tok, Tensor s_ch, Tensor s_group, "
"Tensor! workspace, SymInt size_m, SymInt size_n, "
"SymInt size_k) -> Tensor",
{stride_tag});
// conditionally compiled so impl registration is in source file
// CUTLASS nvfp4 block scaled GEMM
ops.def(
"cutlass_scaled_fp4_mm(Tensor! out, Tensor a, Tensor b,"
......
tests/compile/test_full_graph.py
View file @ 0cdbf5e6
......@@ -53,12 +53,6 @@ def models_list(*, all: bool = True, keywords: Optional[list[str]] = None):
"quantization": "gptq_marlin_24"
}))
if is_quant_method_supported("marlin"):
TEST_MODELS.append(
("robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin", {
"quantization": "marlin"
}))
if not current_platform.is_rocm() and is_quant_method_supported("awq"):
TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {
"quantization": "AWQ"
......
tests/kernels/quantization/test_machete_mm.py
View file @ 0cdbf5e6
......@@ -95,23 +95,23 @@ TEST_TYPES = [
token_scale_type=None)
for w_type in [scalar_types.uint4, scalar_types.uint8]
for a_type in [torch.float16, torch.bfloat16]),
# QQQ style
*(TypeConfig(act_type=torch.int8,
weight_type=scalar_types.uint4b8,
output_type=torch.float16,
group_scale_type=group_scale_type,
group_zero_type=None,
channel_scale_type=torch.float,
token_scale_type=torch.float)
for group_scale_type in [None, torch.float16]),
*(TypeConfig(act_type=torch.float8_e4m3fn,
weight_type=scalar_types.uint4b8,
output_type=torch.float16,
group_scale_type=group_scale_type,
group_zero_type=None,
channel_scale_type=torch.float,
token_scale_type=torch.float)
for group_scale_type in [None, torch.float16]),
# # QQQ style
# *(TypeConfig(act_type=torch.int8,
# weight_type=scalar_types.uint4b8,
# output_type=torch.float16,
# group_scale_type=group_scale_type,
# group_zero_type=None,
# channel_scale_type=torch.float,
# token_scale_type=torch.float)
# for group_scale_type in [None, torch.float16]),
# *(TypeConfig(act_type=torch.float8_e4m3fn,
# weight_type=scalar_types.uint4b8,
# output_type=torch.float16,
# group_scale_type=group_scale_type,
# group_zero_type=None,
# channel_scale_type=torch.float,
# token_scale_type=torch.float)
# for group_scale_type in [None, torch.float16]),
]
# TODO: in future PR refactor this and `is_quant_method_supported` in the kernel
......
tests/kernels/quantization/test_marlin_gemm.py
View file @ 0cdbf5e6
......@@ -13,11 +13,7 @@ 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.qqq import (
MARLIN_QQQ_MAX_PARALLEL, MARLIN_QQQ_MIN_THREAD_N,
MARLIN_QQQ_SUPPORTED_GROUP_SIZES, MARLIN_QQQ_SUPPORTED_NUM_BITS)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
MARLIN_SUPPORTED_GROUP_SIZES, marlin_make_empty_g_idx,
marlin_make_workspace_new, marlin_permute_bias, marlin_permute_scales,
query_marlin_supported_quant_types)
......@@ -31,8 +27,6 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
marlin_weights)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
marlin_24_quantize)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test_qqq import ( # noqa: E501
marlin_qqq_quantize)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
awq_pack, gptq_pack, gptq_quantize_weights, quantize_weights, sort_weights)
from vllm.scalar_type import scalar_types
......@@ -449,68 +443,6 @@ def test_hqq_marlin_gemm(
assert max_diff < 0.04
@pytest.mark.skipif(not is_quant_method_supported("qqq"),
reason="Marlin is not supported on this GPU type.")
@pytest.mark.parametrize("k_chunk", MARLIN_K_CHUNKS)
@pytest.mark.parametrize("n_chunk", MARLIN_N_CHUNKS)
@pytest.mark.parametrize("num_bits", MARLIN_QQQ_SUPPORTED_NUM_BITS)
@pytest.mark.parametrize("group_size", MARLIN_QQQ_SUPPORTED_GROUP_SIZES)
@pytest.mark.parametrize("mnk_factors", MNK_FACTORS)
def test_marlin_qqq_gemm(
k_chunk,
n_chunk,
num_bits,
group_size,
mnk_factors,
):
int8_traits = torch.iinfo(torch.int8)
m_factor, n_factor, k_factor = mnk_factors
size_m = m_factor
size_k = k_chunk * k_factor
size_n = n_chunk * n_factor
a_input = rand_data((size_m, size_k))
b_weight = rand_data((size_k, size_n))
# Quantize activations
s_a = a_input.abs().max(dim=-1, keepdim=True)[0].div(int8_traits.max).to(
torch.float)
q_a = (a_input / s_a).round().clamp(int8_traits.min,
int8_traits.max).to(torch.int8)
# Quantize weights
w_ref, marlin_qqq_q_w, marlin_qqq_s_group, marlin_qqq_s_channel = \
marlin_qqq_quantize(b_weight, num_bits, group_size)
workspace = MarlinWorkspace(size_n, MARLIN_QQQ_MIN_THREAD_N,
MARLIN_QQQ_MAX_PARALLEL)
opcheck(torch.ops._C.marlin_qqq_gemm,
(q_a, marlin_qqq_q_w, s_a, marlin_qqq_s_channel,
marlin_qqq_s_group, workspace.scratch, a_input.shape[0],
b_weight.shape[1], a_input.shape[1]))
output = ops.marlin_qqq_gemm(
q_a,
marlin_qqq_q_w,
s_a,
marlin_qqq_s_channel,
marlin_qqq_s_group,
workspace.scratch,
a_input.shape[0],
b_weight.shape[1],
a_input.shape[1],
)
output_ref = torch.matmul(q_a.half() * s_a.half(), w_ref)
torch.cuda.synchronize()
max_diff = compute_max_diff(output, output_ref)
assert max_diff < 0.04
def test_marlin_gemm_subset_input():
quant_type = scalar_types.uint4b8
group_size = 128
......@@ -602,18 +534,3 @@ def test_marlin_gemm_with_bias(size_m):
max_diff = compute_max_diff(output, output_ref)
assert max_diff < 0.04
def test_marlin_gemm_opcheck():
size_m = 2048
size_n = 4096
size_k = 4096
a = torch.rand((size_m, size_n), device='cuda', dtype=torch.float16)
w = torch.randint(-5, 5, (256, 8192), device='cuda', dtype=torch.int32)
s = torch.full((32, size_k), 0.125, device='cuda', dtype=torch.float16)
wk = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N,
GPTQ_MARLIN_MAX_PARALLEL).scratch
x = torch.ops._C.marlin_gemm(a, w, s, wk, size_m, size_n, size_k)
y = torch.ops._C.marlin_gemm(a, w, s, wk, size_m, size_n, size_k)
torch.testing.assert_close(x, y)
opcheck(torch.ops._C.marlin_gemm, (a, w, s, wk, size_m, size_n, size_k))
tests/quantization/test_configs.py
View file @ 0cdbf5e6
......@@ -22,22 +22,12 @@ class ModelPair:
MODEL_ARG_EXPTYPES = [
# AUTOGPTQ
# compat: autogptq <=0.7.1 is_marlin_format: bool
# Model Serialized in Marlin Format should always use Marlin kernel.
("neuralmagic/TinyLlama-1.1B-Chat-v1.0-marlin", None, "marlin"),
("neuralmagic/TinyLlama-1.1B-Chat-v1.0-marlin", "marlin", "marlin"),
("neuralmagic/TinyLlama-1.1B-Chat-v1.0-marlin", "gptq", "marlin"),
("neuralmagic/TinyLlama-1.1B-Chat-v1.0-marlin", "awq", "ERROR"),
# Model Serialized in Exllama Format.
("TheBloke/Llama-2-7B-Chat-GPTQ", None, "gptq_marlin"),
("TheBloke/Llama-2-7B-Chat-GPTQ", "marlin", "gptq_marlin"),
("TheBloke/Llama-2-7B-Chat-GPTQ", "gptq", "gptq"),
("TheBloke/Llama-2-7B-Chat-GPTQ", "awq", "ERROR"),
# compat: autogptq >=0.8.0 use checkpoint_format: str
# Model Serialized in Marlin Format should always use Marlin kernel.
("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-Marlin-4bit", None, "marlin"),
("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-Marlin-4bit", "marlin", "marlin"),
("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-Marlin-4bit", "gptq", "marlin"),
("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-Marlin-4bit", "awq", "ERROR"),
# Model Serialized in Exllama Format.
("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit", None, "gptq_marlin"),
("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit", "marlin", "gptq_marlin"),
......
tests/quantization/test_lm_head.py
View file @ 0cdbf5e6
......@@ -11,7 +11,6 @@ import torch
from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQMarlinLinearMethod)
from vllm.model_executor.layers.quantization.marlin import MarlinLinearMethod
from vllm.model_executor.layers.vocab_parallel_embedding import (
UnquantizedEmbeddingMethod)
......@@ -19,9 +18,7 @@ PROMPT = "On the surface of Mars, we found"
MODELS_QUANT = [
("ModelCloud/Qwen1.5-1.8B-Chat-GPTQ-4bits-dynamic-cfg-with-lm_head", True),
("ModelCloud/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit-10-25-2024", False),
("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", False),
("neuralmagic/Meta-Llama-3-8B-Instruct-FP8", False)
]
......@@ -41,8 +38,7 @@ def test_lm_head(
lm_head_layer = model.lm_head
if lm_head_quantized:
assert isinstance(lm_head_layer.quant_method,
(GPTQLinearMethod, GPTQMarlinLinearMethod,
MarlinLinearMethod))
(GPTQLinearMethod, GPTQMarlinLinearMethod))
else:
assert isinstance(lm_head_layer.quant_method,
UnquantizedEmbeddingMethod)
......
tests/weight_loading/models.txt
View file @ 0cdbf5e6
......@@ -26,9 +26,5 @@ compressed-tensors, nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-W8A8-testing
awq, casperhansen/mixtral-instruct-awq, main
awq_marlin, casperhansen/mixtral-instruct-awq, main
fp8, neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV, main
marlin, nm-testing/zephyr-beta-7b-marlin-g128, main
marlin, robertgshaw2/zephyr-7b-beta-channelwise-marlin, main
qqq, HandH1998/QQQ-Llama-3-8b-g128, main
qqq, HandH1998/QQQ-Llama-3-8b, main
hqq, nm-testing/Llama-3.2-1B-Instruct-HQQ, main
None, mgleize/fairseq2-dummy-Llama-3.2-1B, main
\ No newline at end of file
vllm/_custom_ops.py
View file @ 0cdbf5e6
......@@ -387,14 +387,6 @@ def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor,
torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
# marlin
def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
b_scales: torch.Tensor, workspace: torch.Tensor, size_m: int,
size_n: int, size_k: int) -> torch.Tensor:
return torch.ops._C.marlin_gemm(a, b_q_weight, b_scales, workspace, size_m,
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,
......@@ -437,25 +429,6 @@ if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
is_zp_float: bool = False) -> torch.Tensor:
return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)
@register_fake("_C::marlin_qqq_gemm")
def _marlin_qqq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
s_tok: torch.Tensor, s_ch: torch.Tensor,
s_group: torch.Tensor, workspace: torch.Tensor,
size_m: torch.SymInt, size_n: torch.SymInt,
size_k: torch.SymInt) -> torch.Tensor:
return torch.empty((size_m, size_n),
dtype=torch.float16,
device=a.device)
@register_fake("_C::marlin_gemm")
def _marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
b_scales: torch.Tensor, workspace: torch.Tensor,
size_m: torch.SymInt, size_n: torch.SymInt,
size_k: torch.SymInt) -> torch.Tensor:
return torch.empty((size_m, size_n),
dtype=torch.float16,
device=a.device)
@register_fake("_C::awq_dequantize")
def _awq_dequantize_fake(qweight: torch.Tensor, scales: torch.Tensor,
zeros: torch.Tensor, split_k_iters: torch.SymInt,
......@@ -1348,15 +1321,6 @@ def scaled_int8_quant(
return output, input_scales, input_azp
# qqq ops
def marlin_qqq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
s_tok: torch.Tensor, s_ch: torch.Tensor,
s_group: torch.Tensor, workspace: torch.Tensor,
size_m: int, size_n: int, size_k: int) -> torch.Tensor:
return torch.ops._C.marlin_qqq_gemm(a, b_q_weight, s_tok, s_ch, s_group,
workspace, size_m, size_n, size_k)
# gguf
def ggml_dequantize(W: torch.Tensor, quant_type: int, m: int, n: int,
dtype: Optional[torch.dtype]) -> torch.Tensor:
......
vllm/config/__init__.py
View file @ 0cdbf5e6
......@@ -1112,9 +1112,9 @@ class ModelConfig:
def _verify_quantization(self) -> None:
supported_quantization = me_quant.QUANTIZATION_METHODS
optimized_quantization_methods = [
"fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
"awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
"quark", "modelopt_fp4", "bitblas", "gptq_bitblas", "inc"
"fp8", "modelopt", "gptq_marlin_24", "gptq_marlin", "awq_marlin",
"fbgemm_fp8", "compressed-tensors", "experts_int8", "quark",
"modelopt_fp4", "bitblas", "gptq_bitblas", "inc"
]
if self.quantization is not None:
self.quantization = cast(me_quant.QuantizationMethods,
......@@ -1137,7 +1137,6 @@ class ModelConfig:
# `override_quantization_method` method) must be checked in order
# of preference (this is particularly important for GPTQ).
overrides = [
"marlin",
"bitblas",
"gptq_marlin_24",
"gptq_marlin",
......
vllm/lora/layers.py
View file @ 0cdbf5e6
......@@ -48,9 +48,6 @@ def _get_lora_device(base_layer: nn.Module) -> torch.device:
# GPTQ/AWQ
elif hasattr(base_layer, "qweight"):
return base_layer.qweight.device
# marlin
elif hasattr(base_layer, "B"):
return base_layer.B.device
# HQQ marlin
elif hasattr(base_layer, "W_q"):
return base_layer.W_q.device
......
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