[6/n]decouple quantization implementation from vLLM dependency (#10750)

python/sglang/srt/layers/quantization/__init__.py

@@ -10,10 +10,6 @@ import torch
 try:
     from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
     from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
-    from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (
-        CompressedTensorsW8A8Fp8MoEMethod,
-        CompressedTensorsWNA16MoEMethod,
-    )
     from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfig
     from vllm.model_executor.layers.quantization.experts_int8 import ExpertsInt8Config
     from vllm.model_executor.layers.quantization.gguf import GGUFConfig
@@ -175,51 +171,3 @@ def monkey_patch_isinstance_for_vllm_base_layer(reverse: bool = False):
         return original_isinstance(obj, classinfo)
 
     builtins.isinstance = patched_isinstance
-
-
-def monkey_patch_moe_apply(class_obj: "FusedMoEMethodBase"):
-    """
-    Monkey patch the apply function of vllm's FusedMoEMethodBase.
-    Convert sglang arguments to vllm arguments.
-    """
-    original_apply = class_obj.apply
-    sig = inspect.signature(original_apply)
-    param_names = list(sig.parameters.keys())
-    has_correction_bias = "e_score_correction_bias" in param_names
-
-    def new_apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        topk_output: TopKOutput,
-        *,
-        activation: str = "silu",
-        apply_router_weight_on_input: bool = False,
-        inplace: bool = True,
-        no_combine: bool = False,
-        routed_scaling_factor: Optional[float] = None,
-    ):
-        assert activation == "silu"
-        assert inplace and not no_combine
-
-        kwargs = {
-            "self": self,
-            "layer": layer,
-            "x": x,
-            "topk_output": topk_output,
-        }
-        return original_apply(**kwargs)
-
-    setattr(class_obj, "apply", new_apply)
-
-
-def monkey_patch_quant_configs():
-    """Apply all monkey patches in one place."""
-
-    monkey_patch_moe_apply(CompressedTensorsW8A8Fp8MoEMethod)
-    monkey_patch_moe_apply(CompressedTensorsWNA16MoEMethod)
-
-
-# Only apply monkey patches if vllm is available
-if VLLM_AVAILABLE:
-    monkey_patch_quant_configs()

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -30,10 +30,12 @@ from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors_moe im
     CompressedTensorsMoEMethod,
 )
 from sglang.srt.layers.quantization.compressed_tensors.schemes import (
+    WNA16_SUPPORTED_BITS,
     CompressedTensorsScheme,
     CompressedTensorsW8A8Fp8,
     CompressedTensorsW8A8Int8,
     CompressedTensorsW8A16Fp8,
+    CompressedTensorsWNA16,
 )
 from sglang.srt.layers.quantization.compressed_tensors.utils import (
     find_matched_target,
@@ -43,23 +45,6 @@ from sglang.srt.layers.quantization.compressed_tensors.utils import (
 from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
 from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 
-try:
-    from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_24 import (
-        CompressedTensors24,
-    )
-    from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w4a16_sparse24 import (
-        W4A16SPARSE24_SUPPORTED_BITS,
-        CompressedTensorsW4A16Sparse24,
-    )
-    from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_wNa16 import (
-        WNA16_SUPPORTED_BITS,
-        CompressedTensorsWNA16,
-    )
-
-    VLLM_AVAILABLE = True
-except ImportError:
-    VLLM_AVAILABLE = False
-
 logger = logging.getLogger(__name__)
 
 __all__ = ["CompressedTensorsLinearMethod"]
@@ -380,19 +365,6 @@ class CompressedTensorsConfig(QuantizationConfig):
 
         # Detect If Mixed Precision
         if self._is_wNa16_group_channel(weight_quant, input_quant):
-            if not VLLM_AVAILABLE:
-                raise ImportError(
-                    "vllm is not installed, to use CompressedTensorsW4A16Sparse24 and CompressedTensorsWNA16, please install vllm"
-                )
-            if (
-                self.quant_format == CompressionFormat.marlin_24.value
-                and weight_quant.num_bits in W4A16SPARSE24_SUPPORTED_BITS
-            ):
-                return CompressedTensorsW4A16Sparse24(
-                    strategy=weight_quant.strategy,
-                    num_bits=weight_quant.num_bits,
-                    group_size=weight_quant.group_size,
-                )
             if (
                 self.quant_format == CompressionFormat.pack_quantized.value
                 and weight_quant.num_bits in WNA16_SUPPORTED_BITS
@@ -403,6 +375,10 @@ class CompressedTensorsConfig(QuantizationConfig):
                     group_size=weight_quant.group_size,
                     actorder=weight_quant.actorder,
                 )
+            else:
+                raise ImportError(
+                    "Other method (CompressedTensorsW4A16Sparse24) is not supported now"
+                )
 
         if is_activation_quantization_format(self.quant_format):
             if self._is_fp8_w8a8(weight_quant, input_quant):
@@ -426,10 +402,6 @@ class CompressedTensorsConfig(QuantizationConfig):
 
             # note: input_quant can be None
             if self._is_fp8_w8a16(weight_quant, input_quant):
-                if not VLLM_AVAILABLE:
-                    raise ImportError(
-                        "vllm is not installed, to use CompressedTensorsW8A16Fp8, please install vllm"
-                    )
                 is_static_input_scheme = input_quant and not input_quant.dynamic
                 return CompressedTensorsW8A16Fp8(
                     strategy=weight_quant.strategy,
@@ -470,7 +442,7 @@ class CompressedTensorsConfig(QuantizationConfig):
 
         # Find the "target" in the compressed-tensors config
         # that our layer conforms to.
-        # TODO (@robertgshaw): add compressed-tensors as dep
+        # TODO : add compressed-tensors as dep
         # so we do not have to re-write these functions
         # need to make accelerate optional in ct to do this
 
@@ -508,24 +480,7 @@ class CompressedTensorsConfig(QuantizationConfig):
             input_quant=input_quant,
             sparsity_scheme=sparsity_scheme,
         ):
-            if not VLLM_AVAILABLE:
+            raise ImportError("CompressedTensors24 is not supported now")
-                raise ImportError(
-                    "vllm is not installed, to use CompressedTensors24, please install vllm"
-                )
-            # Have a valid sparsity scheme
-            # Validate layer is supported by Cutlass 2:4 Kernel
-            model_compression_config = (
-                None
-                if sparsity_scheme is None or sparsity_scheme.format == "dense"
-                else self.config
-            )
-
-            scheme = CompressedTensors24(
-                quantized=weight_quant is not None or input_quant is not None,
-                weight_quant=weight_quant,
-                input_quant=input_quant,
-                model_compression_config=model_compression_config,
-            )
         elif weight_quant is None:
             logger.warning_once(
                 "Acceleration for non-quantized schemes is "

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -6,7 +6,7 @@ import enum
 import logging
 import re
 from enum import Enum
-from typing import TYPE_CHECKING, List
+from typing import TYPE_CHECKING
 
 try:
     from sgl_kernel import fused_marlin_moe
@@ -31,9 +31,13 @@ from sglang.srt.environ import envs
 from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
 from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
 from sglang.srt.layers.quantization.base_config import FusedMoEMethodBase
-from sglang.srt.layers.quantization.compressed_tensors import WNA16_SUPPORTED_BITS
+from sglang.srt.layers.quantization.compressed_tensors.schemes import (
+    WNA16_SUPPORTED_BITS,
+)
 from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz, scaled_fp8_quant
 from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
+from sglang.srt.layers.quantization.gptq import gptq_marlin_moe_repack
+from sglang.srt.layers.quantization.marlin_utils import marlin_moe_permute_scales
 from sglang.srt.layers.quantization.utils import (
     all_close_1d,
     per_tensor_dequantize,
@@ -42,6 +46,7 @@ from sglang.srt.layers.quantization.utils import (
 from sglang.srt.utils import (
     get_bool_env_var,
     get_compiler_backend,
+    is_cuda,
     is_hip,
     set_weight_attrs,
 )
@@ -57,6 +62,8 @@ if TYPE_CHECKING:
     )
 
 _is_hip = is_hip()
+_is_cuda = is_cuda()
+
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 
 if _use_aiter:
@@ -64,12 +71,9 @@ if _use_aiter:
 
     from sglang.srt.layers.moe.rocm_moe_utils import rocm_fused_experts_tkw1
 
-try:
-    import vllm  # noqa: F401
 
-    VLLM_AVAILABLE = True
+if _is_cuda:
-except ImportError:
+    from sgl_kernel import fused_marlin_moe
-    VLLM_AVAILABLE = False
 
 logger = logging.getLogger(__name__)
 
@@ -127,10 +131,8 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
         weight_quant = quant_config.target_scheme_map["Linear"].get("weights")
         input_quant = quant_config.target_scheme_map["Linear"].get("input_activations")
         if quant_config._is_wNa16_group_channel(weight_quant, input_quant):
-            if not VLLM_AVAILABLE:
+
-                raise ImportError(
+            logger.info_once("Using CompressedTensorsWNA16MarlinMoEMethod")
-                    "vllm is not installed, to use CompressedTensorsWNA16MoEMethod, please install vllm."
-                )
             return CompressedTensorsWNA16MoEMethod(quant_config)
         elif quant_config._is_fp8_w8a8(weight_quant, input_quant):
             return CompressedTensorsW8A8Fp8MoEMethod(quant_config)
@@ -432,9 +434,6 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
     ):
         if self.num_gpu_experts != -1:
             num_experts = self.num_gpu_experts
-        # assert (
-        #     params_dtype == torch.float16
-        # ), "float16 is required for MoE compressed models. Set dtype=torch.float16"  # noqa: E501
 
         # Will transpose the loaded weight along the
         # intermediate and hidden dim sizes. Will
@@ -573,44 +572,6 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             getattr(layer, name).copy_(new_t)
             del new_t
 
-        def get_scale_perms(num_bits: int):
-            scale_perm: List[int] = []
-            for i in range(8):
-                scale_perm.extend([i + 8 * j for j in range(8)])
-            scale_perm_single: List[int] = []
-            for i in range(4):
-                scale_perm_single.extend(
-                    [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]
-                )
-            return scale_perm, scale_perm_single
-
-        def marlin_permute_scales(
-            s: torch.Tensor, size_k: int, size_n: int, group_size: int, num_bits: int
-        ):
-            scale_perm, scale_perm_single = get_scale_perms(num_bits)
-            if group_size < size_k and group_size != -1:
-                s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
-            else:
-                s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
-            s = s.reshape((-1, size_n)).contiguous()
-            return s
-
-        def marlin_moe_permute_scales(
-            s: torch.Tensor, size_k: int, size_n: int, group_size: int, num_bits: int
-        ):
-            num_experts = s.shape[0]
-            output = torch.empty(
-                (num_experts, s.shape[1], s.shape[2]), device=s.device, dtype=s.dtype
-            )
-            for e in range(num_experts):
-                output[e] = marlin_permute_scales(
-                    s[e], size_k, size_n, group_size, num_bits
-                )
-            return output
-
-        size_k2 = layer.w2_weight_packed.shape[2]
-        size_k13 = layer.w13_weight_packed.shape[2]
-
         num_experts = layer.w13_weight_g_idx.shape[0]
         device = layer.w13_weight_g_idx.device
 
@@ -657,42 +618,39 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
                 requires_grad=False,
             )
 
-        from vllm import _custom_ops as vllm_ops
+        marlin_w13_qweight = gptq_marlin_moe_repack(
-
-        marlin_w13_qweight = vllm_ops.gptq_marlin_moe_repack(
             layer.w13_weight_packed,
             layer.w13_g_idx_sort_indices,
             layer.w13_weight_packed.shape[1] * self.packed_factor,
             layer.w13_weight_packed.shape[2],
             self.num_bits,
         )
-        replace_tensor("w13_weight_packed", marlin_w13_qweight)
+        replace_parameter(layer, "w13_weight_packed", marlin_w13_qweight)
-        marlin_w2_qweight = vllm_ops.gptq_marlin_moe_repack(
+        marlin_w2_qweight = gptq_marlin_moe_repack(
             layer.w2_weight_packed,
             layer.w2_g_idx_sort_indices,
             layer.w2_weight_packed.shape[1] * self.packed_factor,
             layer.w2_weight_packed.shape[2],
             self.num_bits,
         )
-        replace_tensor("w2_weight_packed", marlin_w2_qweight)
+        replace_parameter(layer, "w2_weight_packed", marlin_w2_qweight)
         # Repack scales
         marlin_w13_scales = marlin_moe_permute_scales(
             layer.w13_weight_scale,
-            size_k13,
+            layer.w13_weight_packed.shape[2],
             layer.w13_weight_scale.shape[2],
             self.group_size,
-            self.num_bits,
         )
-        replace_tensor("w13_weight_scale", marlin_w13_scales)
+        replace_parameter(layer, "w13_weight_scale", marlin_w13_scales)
+
         marlin_w2_scales = marlin_moe_permute_scales(
             layer.w2_weight_scale,
             layer.w2_weight_scale.shape[1]
             * (self.group_size if self.group_size != -1 else self.packed_factor),
-            size_k2,
+            layer.w2_weight_scale.shape[2],
             self.group_size,
-            self.num_bits,
         )
-        replace_tensor("w2_weight_scale", marlin_w2_scales)
+        replace_parameter(layer, "w2_weight_scale", marlin_w2_scales)
 
     def create_moe_runner(
         self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
@@ -716,7 +674,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
 
         topk_weights, topk_ids, router_logits = topk_output
 
-        output = torch.ops.vllm.fused_marlin_moe(
+        output = fused_marlin_moe(
             x,
             layer.w13_weight_packed,
             layer.w2_weight_packed,

python/sglang/srt/layers/quantization/compressed_tensors/schemes/__init__.py

@@ -4,10 +4,13 @@ from .compressed_tensors_scheme import CompressedTensorsScheme
 from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8
 from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8
 from .compressed_tensors_w8a16_fp8 import CompressedTensorsW8A16Fp8
+from .compressed_tensors_wNa16 import WNA16_SUPPORTED_BITS, CompressedTensorsWNA16
 
 __all__ = [
     "CompressedTensorsScheme",
     "CompressedTensorsW8A8Fp8",
     "CompressedTensorsW8A16Fp8",
     "CompressedTensorsW8A8Int8",
+    "CompressedTensorsWNA16",
+    "WNA16_SUPPORTED_BITS",
 ]

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py

@@ -14,24 +14,11 @@ from sglang.srt.layers.parameter import (
 from sglang.srt.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme,
 )
-from sglang.srt.layers.quantization.utils import convert_to_channelwise
+from sglang.srt.layers.quantization.marlin_utils_fp8 import (
-
-try:
-    from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear,
     prepare_fp8_layer_for_marlin,
 )
-
+from sglang.srt.layers.quantization.utils import convert_to_channelwise
-    MARLIN_FP8_AVAILABLE = True
-except ImportError:
-    MARLIN_FP8_AVAILABLE = False
-
-    def apply_fp8_marlin_linear(*args, **kwargs):
-        raise ImportError("vllm is not installed")
-
-    def prepare_fp8_layer_for_marlin(*args, **kwargs):
-        raise ImportError("vllm is not installed")
-
 
 __all__ = ["CompressedTensorsW8A16Fp8"]
 
@@ -43,11 +30,6 @@ class CompressedTensorsW8A16Fp8(CompressedTensorsScheme):
         self.strategy = strategy
         self.is_static_input_scheme = is_static_input_scheme
 
-        if not MARLIN_FP8_AVAILABLE:
-            raise ImportError(
-                "vllm is not installed. To use CompressedTensorsW8A16Fp8, please install vllm"
-            )
-
     @classmethod
     def get_min_capability(cls) -> int:
         # ampere and up

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -0,0 +1,339 @@
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+from typing import Callable, Optional
+
+import torch
+from compressed_tensors.quantization import ActivationOrdering
+
+# yapf conflicts with isort for this block
+# yapf: disable
+from sglang.srt.layers.parameter import (
+    BasevLLMParameter,
+    ChannelQuantScaleParameter,
+    GroupQuantScaleParameter,
+    PackedColumnParameter,
+    PackedvLLMParameter,
+    RowvLLMParameter,
+    permute_param_layout_,
+)
+from sglang.srt.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme,
+)
+from sglang.srt.layers.quantization.marlin_utils import (
+    MarlinLinearLayerConfig,
+    apply_gptq_marlin_linear,
+    check_marlin_supports_shape,
+    marlin_is_k_full,
+    marlin_make_empty_g_idx,
+    marlin_make_workspace,
+    marlin_permute_scales,
+    marlin_repeat_scales_on_all_ranks,
+    marlin_sort_g_idx,
+    marlin_zero_points,
+)
+from sglang.srt.layers.quantization.utils import (
+    get_scalar_types,
+    replace_parameter,
+    unpack_cols,
+)
+from sglang.srt.utils import is_cuda
+
+_is_cuda = is_cuda()
+
+if _is_cuda:
+    from sgl_kernel import gptq_marlin_repack
+
+
+ScalarType, scalar_types = get_scalar_types()
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["CompressedTensorsWNA16"]
+WNA16_SUPPORTED_TYPES_MAP = {
+    4: scalar_types.uint4b8,
+    8: scalar_types.uint8b128
+}
+WNA16_ZP_SUPPORTED_TYPES_MAP = {4: scalar_types.uint4, 8: scalar_types.uint8}
+WNA16_SUPPORTED_BITS = list(WNA16_SUPPORTED_TYPES_MAP.keys())
+
+
+class CompressedTensorsWNA16(CompressedTensorsScheme):
+    _kernel_backends_being_used: set[str] = set()
+
+    def __init__(self,
+                 strategy: str,
+                 num_bits: int,
+                 group_size: Optional[int] = None,
+                 symmetric: Optional[bool] = True,
+                 actorder: Optional[ActivationOrdering] = None):
+
+        self.pack_factor = 32 // num_bits
+        self.strategy = strategy
+        self.symmetric = symmetric
+        self.group_size = -1 if group_size is None else group_size
+        self.has_g_idx = actorder == ActivationOrdering.GROUP
+
+        if self.group_size == -1 and self.strategy != "channel":
+            raise ValueError("Marlin kernels require group quantization or "
+                             "channelwise quantization, but found no group "
+                             "size and strategy is not channelwise.")
+
+        if num_bits not in WNA16_SUPPORTED_TYPES_MAP:
+            raise ValueError(
+                f"Unsupported num_bits = {num_bits}. "
+                f"Supported num_bits = {WNA16_SUPPORTED_TYPES_MAP.keys()}")
+
+        self.quant_type = (WNA16_ZP_SUPPORTED_TYPES_MAP[num_bits]
+                           if not self.symmetric else
+                           WNA16_SUPPORTED_TYPES_MAP[num_bits])
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere and up
+        return 80
+
+    def create_weights(self, layer: torch.nn.Module, output_size: int,
+                       input_size: int, output_partition_sizes: list[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        output_size_per_partition = sum(output_partition_sizes)
+
+        self.kernel_config = MarlinLinearLayerConfig(
+            full_weight_shape=(input_size, output_size),
+            partition_weight_shape=(
+                input_size_per_partition,
+                output_size_per_partition,
+            ),
+            weight_type=self.quant_type,
+            act_type=params_dtype,
+            group_size=self.group_size,
+            zero_points=not self.symmetric,
+            has_g_idx=self.has_g_idx
+        )
+
+        # If group_size is -1, we are in channelwise case.
+        group_size = self.group_size if self.group_size != -1 else input_size
+        row_parallel = (input_size != input_size_per_partition)
+        partition_scales = not marlin_repeat_scales_on_all_ranks(
+            self.has_g_idx, self.group_size, row_parallel)
+
+        scales_and_zp_size = input_size // group_size
+
+        if partition_scales:
+            assert input_size_per_partition % group_size == 0
+            scales_and_zp_size = input_size_per_partition // group_size
+
+        weight = PackedvLLMParameter(input_dim=1,
+                                     output_dim=0,
+                                     weight_loader=weight_loader,
+                                     packed_factor=self.pack_factor,
+                                     packed_dim=1,
+                                     data=torch.empty(
+                                         output_size_per_partition,
+                                         input_size_per_partition //
+                                         self.pack_factor,
+                                         dtype=torch.int32,
+                                     ))
+
+        weight_scale_args = {
+            "weight_loader":
+            weight_loader,
+            "data":
+            torch.empty(
+                output_size_per_partition,
+                scales_and_zp_size,
+                dtype=params_dtype,
+            )
+        }
+
+        zeros_args = {
+            "weight_loader":
+            weight_loader,
+            "data":
+            torch.zeros(
+                output_size_per_partition // self.pack_factor,
+                scales_and_zp_size,
+                dtype=torch.int32,
+            )
+        }
+
+        if not partition_scales:
+            weight_scale = ChannelQuantScaleParameter(output_dim=0,
+                                                      **weight_scale_args)
+
+            if not self.symmetric:
+                qzeros = PackedColumnParameter(output_dim=0,
+                                               packed_dim=0,
+                                               packed_factor=self.pack_factor,
+                                               **zeros_args)
+        else:
+            weight_scale = GroupQuantScaleParameter(output_dim=0,
+                                                    input_dim=1,
+                                                    **weight_scale_args)
+            if not self.symmetric:
+                qzeros = PackedvLLMParameter(input_dim=1,
+                                             output_dim=0,
+                                             packed_dim=0,
+                                             packed_factor=self.pack_factor,
+                                             **zeros_args)
+
+        # A 2D array defining the original shape of the weights
+        # before packing
+        weight_shape = BasevLLMParameter(data=torch.empty(2,
+                                                          dtype=torch.int64),
+                                         weight_loader=weight_loader)
+
+        layer.register_parameter("weight_packed", weight)
+        layer.register_parameter("weight_scale", weight_scale)
+        layer.register_parameter("weight_shape", weight_shape)
+
+        if not self.symmetric:
+            layer.register_parameter("weight_zero_point", qzeros)
+
+        # group index (for activation reordering)
+        if self.has_g_idx:
+            weight_g_idx = RowvLLMParameter(data=torch.empty(
+                input_size_per_partition,
+                dtype=torch.int32,
+            ),
+                                            input_dim=0,
+                                            weight_loader=weight_loader)
+            layer.register_parameter("weight_g_idx", weight_g_idx)
+
+    # Checkpoints are serialized in compressed-tensors format, which is
+    # different from the format the kernel may want. Handle repacking here.
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Default names since marlin requires empty parameters for these,
+        # TODO: remove this requirement from marlin (allow optional tensors)
+        self.w_q_name = "weight_packed"
+        self.w_s_name = "weight_scale"
+        self.w_zp_name = "weight_zero_point"
+        self.w_gidx_name = "weight_g_idx"
+
+        device = getattr(layer, self.w_q_name).device
+        c = self.kernel_config
+
+        check_marlin_supports_shape(
+            c.partition_weight_shape[1],  # out_features
+            c.partition_weight_shape[0],  # in_features
+            c.full_weight_shape[0],  # in_features
+            c.group_size,
+        )
+
+        row_parallel = c.partition_weight_shape[0] != c.full_weight_shape[0]
+        self.is_k_full = marlin_is_k_full(c.has_g_idx, row_parallel)
+
+        # Allocate marlin workspace.
+        self.workspace = marlin_make_workspace(device)
+
+        def _transform_param(
+            layer: torch.nn.Module, name: Optional[str], fn: Callable
+        ) -> None:
+            if name is not None and getattr(layer, name, None) is not None:
+
+                old_param = getattr(layer, name)
+                new_param = fn(old_param)
+                # replace the parameter with torch.nn.Parameter for TorchDynamo
+                # compatibility
+                replace_parameter(
+                    layer, name, torch.nn.Parameter(new_param.data, requires_grad=False)
+                )
+
+        def transform_w_q(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
+            x.data = gptq_marlin_repack(
+                x.data.contiguous(),
+                perm=layer.g_idx_sort_indices,
+                size_k=c.partition_weight_shape[0],
+                size_n=c.partition_weight_shape[1],
+                num_bits=c.weight_type.size_bits,
+            )
+            return x
+
+        def transform_w_s(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1)
+            x.data = marlin_permute_scales(
+                x.data.contiguous(),
+                size_k=c.partition_weight_shape[0],
+                size_n=c.partition_weight_shape[1],
+                group_size=c.group_size,
+            )
+            return x
+
+        if c.has_g_idx:
+            g_idx, g_idx_sort_indices = marlin_sort_g_idx(
+                getattr(layer, self.w_gidx_name)
+            )
+            _transform_param(layer, self.w_gidx_name, lambda _: g_idx)
+            layer.g_idx_sort_indices = g_idx_sort_indices
+        else:
+            setattr(layer, self.w_gidx_name, marlin_make_empty_g_idx(device))
+            layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
+
+        if c.zero_points:
+            grouped_k = (
+                c.partition_weight_shape[0] // c.group_size if c.group_size != -1 else 1
+            )
+            _transform_param(
+                layer,
+                self.w_zp_name,
+                lambda x: marlin_zero_points(
+                    unpack_cols(
+                        x.t(),
+                        c.weight_type.size_bits,
+                        grouped_k,
+                        c.partition_weight_shape[1],
+                    ),
+                    size_k=grouped_k,
+                    size_n=c.partition_weight_shape[1],
+                    num_bits=c.weight_type.size_bits,
+                ),
+            )
+        else:
+            setattr(layer, self.w_zp_name, marlin_make_empty_g_idx(device))
+        _transform_param(layer, self.w_q_name, transform_w_q)
+        _transform_param(layer, self.w_s_name, transform_w_s)
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+        c = self.kernel_config
+
+        def _get_weight_params(
+            layer: torch.nn.Module,
+        ) -> tuple[
+            torch.Tensor,  # w_q
+            torch.Tensor,  # w_s
+            Optional[torch.Tensor],  # w_zp,
+            Optional[torch.Tensor],  # w_gidx
+        ]:
+            return (
+                getattr(layer, self.w_q_name),
+                getattr(layer, self.w_s_name),
+                getattr(layer, self.w_zp_name or "", None),
+                getattr(layer, self.w_gidx_name or "", None),
+            )
+
+        w_q, w_s, w_zp, w_gidx = _get_weight_params(layer)
+
+        # `process_weights_after_loading` will ensure w_zp and w_gidx are not
+        #  None for marlin
+        return apply_gptq_marlin_linear(
+            input=x,
+            weight=w_q,
+            weight_scale=w_s,
+            weight_zp=w_zp,  # type: ignore
+            g_idx=w_gidx,  # type: ignore
+            g_idx_sort_indices=layer.g_idx_sort_indices,
+            workspace=self.workspace,
+            wtype=c.weight_type,
+            input_size_per_partition=c.partition_weight_shape[0],
+            output_size_per_partition=c.partition_weight_shape[1],
+            is_k_full=self.is_k_full,
+            bias=bias,
+        )

python/sglang/srt/layers/quantization/marlin_utils.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import logging
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Optional
 
 import numpy
@@ -57,6 +58,17 @@ MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
 USE_FP32_REDUCE_DEFAULT = True
 
 
+@dataclass
+class MarlinLinearLayerConfig:
+    full_weight_shape: tuple[int, int]  # [in, out]
+    partition_weight_shape: tuple[int, int]
+    weight_type: ScalarType
+    act_type: torch.dtype
+    group_size: int
+    zero_points: bool
+    has_g_idx: bool
+
+
 # For binary size and compile time, we don't support the same types for with and
 #  without runtime zero-point. We support common cases, i.e. AWQ and GPTQ.
 #  TODO: we may want to move this into the C++ so its closer to the actual impl