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[Kernel] [Quantization] Add MXFP4 and bias support for marlin kernel (#22428)

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Signed-off-by: rongfu.leng <rongfu.leng@daocloud.io>
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This commit is contained in:
Jinzhen Lin
2025-08-15 02:23:22 +08:00
committed by GitHub
parent ab9f2cfd19
commit 33c63e9547
34 changed files with 1128 additions and 324 deletions
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95
tests/kernels/quantization/test_marlin_gemm.py
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@@ -19,10 +19,11 @@ from vllm.model_executor.layers.quantization.qqq import (
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_scales,
marlin_make_workspace_new, marlin_permute_bias, marlin_permute_scales,
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)
FP4_MARLIN_SUPPORTED_GROUP_SIZES, rand_marlin_weight_mxfp4_like,
rand_marlin_weight_nvfp4_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 (
@@ -39,7 +40,7 @@ from vllm.scalar_type import scalar_types
ACT_ORDER_OPTS = [False, True]
K_FULL_OPTS = [False, True]
USE_ATOMIC_ADD_OPTS = [False, True]
USE_FP32_REDUCE_OPTS = [False, True]
USE_FP32_REDUCE_OPTS = [True]
MARLIN_K_CHUNKS = [128]
MARLIN_N_CHUNKS = [64, 256]
@@ -202,17 +203,10 @@ def test_awq_marlin_repack(k_chunk, n_chunk, quant_type, group_size,
@pytest.mark.parametrize("is_k_full", K_FULL_OPTS)
@pytest.mark.parametrize("use_atomic_add", USE_ATOMIC_ADD_OPTS)
@pytest.mark.parametrize("use_fp32_reduce", USE_FP32_REDUCE_OPTS)
def test_gptq_marlin_gemm(
k_chunk,
n_chunk,
quant_type,
group_size,
mnk_factors,
act_order,
is_k_full,
use_atomic_add,
use_fp32_reduce,
):
@pytest.mark.parametrize("dtype", DTYPES)
def test_gptq_marlin_gemm(k_chunk, n_chunk, quant_type, group_size,
mnk_factors, act_order, is_k_full, use_atomic_add,
use_fp32_reduce, dtype):
m_factor, n_factor, k_factor = mnk_factors
has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]
@@ -231,14 +225,23 @@ def test_gptq_marlin_gemm(
if size_k % group_size != 0:
return
a_input = rand_data((size_m, size_k))
b_weight = rand_data((size_k, size_n))
a_input = rand_data((size_m, size_k), dtype)
b_weight = rand_data((size_k, size_n), dtype)
if quant_type == scalar_types.float4_e2m1f:
if group_size != 16 or act_order:
if group_size not in [16, 32] or act_order:
return
w_ref, marlin_q_w, marlin_s, marlin_s2 = rand_marlin_weight_fp4_like(
b_weight.T, group_size)
if group_size == 32 and dtype == torch.float16:
return
if group_size == 16:
w_ref, marlin_q_w, marlin_s, marlin_s2 = \
rand_marlin_weight_nvfp4_like(b_weight.T, group_size)
else:
w_ref, marlin_q_w, marlin_s = \
rand_marlin_weight_mxfp4_like(b_weight.T, group_size)
marlin_s2 = None
g_idx = None
sort_indices = None
marlin_zp = None
@@ -272,8 +275,8 @@ def test_gptq_marlin_gemm(
workspace = marlin_make_workspace_new(w_ref.device)
opcheck(torch.ops._C.gptq_marlin_gemm,
(a_input, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, g_idx,
sort_indices, workspace, quant_type.id, a_input.shape[0],
(a_input, None, marlin_q_w, None, marlin_s, marlin_s2, marlin_zp,
g_idx, sort_indices, workspace, quant_type.id, a_input.shape[0],
b_weight.shape[1], a_input.shape[1], is_k_full, use_atomic_add,
use_fp32_reduce, False),
test_utils=DEFAULT_OPCHECK_TEST_UTILS)
@@ -282,6 +285,7 @@ def test_gptq_marlin_gemm(
a_input,
None,
marlin_q_w,
None,
marlin_s,
marlin_s2,
marlin_zp,
@@ -418,6 +422,7 @@ def test_hqq_marlin_gemm(
a_input,
None,
marlin_w_q,
None,
marlin_s,
None,
marlin_zp,
@@ -531,6 +536,7 @@ def test_marlin_gemm_subset_input():
a_input,
None,
marlin_q_w,
None,
marlin_s,
None,
marlin_zp,
@@ -555,6 +561,53 @@ def test_marlin_gemm_subset_input():
assert max_diff < 0.04
@pytest.mark.parametrize("size_m", [1, 256])
def test_marlin_gemm_with_bias(size_m):
quant_type = scalar_types.uint4b8
group_size = 128
size_k, size_n = 1024, 2048
a_input = rand_data((size_m, size_k))
b_weight = rand_data((size_k, size_n))
b_bias = rand_data((size_n, )) * 10
marlin_bias = marlin_permute_bias(b_bias)
w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, _ = marlin_quantize(
b_weight, quant_type, group_size, False)
marlin_zp = marlin_make_empty_g_idx(marlin_s.device)
workspace = marlin_make_workspace_new(a_input.device)
output = ops.gptq_marlin_gemm(
a_input,
None,
marlin_q_w,
marlin_bias,
marlin_s,
None,
marlin_zp,
g_idx,
sort_indices,
workspace,
quant_type,
a_input.shape[0],
b_weight.shape[1],
a_input.shape[1],
is_k_full=True,
use_atomic_add=False,
use_fp32_reduce=True,
is_zp_float=False,
)
output_ref = torch.matmul(a_input, w_ref) + b_bias.view(1, -1)
torch.cuda.synchronize()
max_diff = compute_max_diff(output, output_ref)
assert max_diff < 0.04
def test_marlin_gemm_opcheck():
size_m = 2048
size_n = 4096