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[Kernel/Quant] Remove the original marlin format and qqq (#23204)

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Signed-off-by: mgoin <mgoin64@gmail.com>
This commit is contained in:
Michael Goin
2025-08-20 15:13:36 -04:00
committed by GitHub
parent ebe56a0064
commit 0cdbf5e61c
26 changed files with 92 additions and 3698 deletions
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83
tests/kernels/quantization/test_marlin_gemm.py
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@@ -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))