适配w8a8_marlin

python/sglang/srt/configs/model_config.py

@@ -616,6 +616,7 @@ class ModelConfig:
             "mxfp4",
             "slimquant_w4a8_marlin",
             "w8a8_int8",
+            "slimquant_marlin",
         ]
         optimized_quantization_methods = [
             "fp8",
@@ -636,6 +637,7 @@ class ModelConfig:
             "w4afp8",
             "petit_nvfp4",
             "slimquant_w4a8_marlin",
+            "slimquant_marlin",
         ]
         compatible_quantization_methods = {
             "modelopt_fp4": ["modelopt"],

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

@@ -58,6 +58,7 @@ from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
 from sglang.srt.layers.quantization.w8a8_fp8 import W8A8Fp8Config
 from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
 from sglang.srt.layers.quantization.slimquant_w4a8_marlin import SlimQuantW4A8Int8MarlinConfig
+from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors_marlin import SlimQuantCompressedTensorsMarlinConfig
 from sglang.srt.utils import is_cuda, is_hip, mxfp_supported
 
 _is_mxfp_supported = mxfp_supported()
@@ -84,7 +85,8 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "w4afp8": W4AFp8Config,
     "petit_nvfp4": PetitNvFp4Config,
     "fbgemm_fp8": FBGEMMFp8Config,
-    "slimquant_w4a8_marlin":SlimQuantW4A8Int8MarlinConfig,
+    "slimquant_w4a8_marlin": SlimQuantW4A8Int8MarlinConfig,
+    "slimquant_marlin": SlimQuantCompressedTensorsMarlinConfig,
 }
 
 

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

@@ -44,6 +44,7 @@ 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
+from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
 
 logger = logging.getLogger(__name__)
 
@@ -636,3 +637,47 @@ class CompressedTensorsLinearMethod(LinearMethodBase):
         if scheme is None:
             raise ValueError("A scheme must be defined for each layer")
         return scheme.apply_weights(layer, x, bias=bias)
+
+class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from compressed-tensors
+    checkpoints.
+    """
+
+    def __init__(self, quant_config: CompressedTensorsConfig):
+        self.validate_kv_cache_scheme(quant_config.kv_cache_scheme)
+        super().__init__(quant_config)
+
+    @staticmethod
+    def validate_kv_cache_scheme(kv_cache_scheme: Optional[dict[str, Any]]):
+        """
+        Validator for the kv cache scheme. Useful for controlling the
+        kv cache quantization schemes, that are being supported in vLLM
+        :param kv_cache_scheme: the compressed-tensors kv cache scheme
+        """
+        if kv_cache_scheme is None:
+            return
+
+        type_ = kv_cache_scheme.get("type")
+        num_bits = kv_cache_scheme.get("num_bits")
+
+        if type_ != "float" and num_bits != 8:
+            raise NotImplementedError(
+                "Currently supported kv cache quantization is "
+                "num_bits=8, type=float, however "
+                f"received num_bits={num_bits}, type={type_}")
+
+        strategy = kv_cache_scheme.get("strategy")
+        if strategy != "tensor":
+            raise NotImplementedError(
+                "Only support per-tensor scaling factor "
+                "for compressed-tensors KV cache. "
+                f"Expected strategy: tensor, found strategy: {strategy}")
+
+        is_symmetric = kv_cache_scheme.get("symmetric")
+        if not is_symmetric:
+            raise NotImplementedError(
+                "Only support symmetric scaling factor "
+                "for compressed-tensors KV cache. "
+                f"However found symmetric: {is_symmetric}")
+

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

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+from typing import TYPE_CHECKING, Any, Literal, Optional, cast
+import torch
+from compressed_tensors.config import SparsityCompressionConfig
+from compressed_tensors.quantization import QuantizationArgs
+import logging
+
+from sglang.srt.layers.linear import LinearBase
+from sglang.srt.layers.quantization.unquant import UnquantizedEmbeddingMethod
+from sglang.srt.layers.quantization.base_config import (
+    LinearMethodBase,
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import CompressedTensorsConfig, CompressedTensorsLinearMethod, CompressedTensorsKVCacheMethod
+from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors_moe_marlin import CompressedTensorsMarlinMoEMethod
+from sglang.srt.layers.quantization.compressed_tensors.utils import (
+    should_ignore_layer)
+from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
+import os
+
+# if TYPE_CHECKING:
+#     from vllm.model_executor.models.utils import WeightsMapper
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["CompressedTensorsLinearMethod"]
+
+SPARSITY_CONFIG_NAME: Literal["sparsity_config"] = "sparsity_config"
+QUANTIZATION_SCHEME_MAP_TYPE = dict[str, Optional[dict[str, QuantizationArgs]]]
+
+
+class SlimQuantCompressedTensorsMarlinConfig(CompressedTensorsConfig):
+    def __init__(
+        self,
+        target_scheme_map: dict[str, Any],
+        ignore: list[str],
+        quant_format: str,
+        sparsity_scheme_map: dict[str, SparsityCompressionConfig],
+        sparsity_ignore_list: list[str],
+        kv_cache_scheme: Optional[dict[str, Any]] = None,
+        config: Optional[dict[str, Any]] = None,
+        packed_modules_mapping: Optional[dict[str, list[str]]] = None,   
+    ):
+        super().__init__(
+            target_scheme_map,
+            ignore,
+            quant_format,
+            sparsity_scheme_map,
+            sparsity_ignore_list,
+            kv_cache_scheme,
+            config,
+            packed_modules_mapping,  
+        )
+
+    
+    @classmethod
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[str]:
+        if hf_quant_cfg.get("quant_method") == "compressed-tensors" \
+                and user_quant == "slimquant_marlin":
+            return cls.get_name()
+        return None
+    @classmethod
+    def get_name(cls) -> str:
+        return "slimquant_marlin"
+
+    def get_quant_method(
+            self,
+            layer: torch.nn.Module,
+            prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+        from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE  # Avoid circular import
+        # from sglang.srt.layers.radix_attention import RadixAttention
+        # Check if the layer is skipped for quantization.
+        if should_ignore_layer(prefix,
+                               ignore=self.ignore,
+                               fused_mapping=self.packed_modules_mapping):
+            return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
+        if isinstance(layer, LinearBase):
+            scheme = self.get_scheme(layer=layer, layer_name=prefix)
+            if scheme is None:
+                return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
+            layer.scheme = scheme
+            return CompressedTensorsLinearMethod(self)
+        # if isinstance(layer, RadixAttention):
+        #     return CompressedTensorsKVCacheMethod(self)
+        if isinstance(layer, FusedMoE):
+            return CompressedTensorsMarlinMoEMethod.get_moe_method(self, layer)
+        return None

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

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import enum
+from enum import Enum
+from typing import Callable, Optional
+import torch
+
+from compressed_tensors.quantization import (QuantizationStrategy)
+import logging
+from torch.nn.parameter import Parameter
+
+from sglang.srt.layers.quantization.base_config import FusedMoEMethodBase
+
+from sglang.srt.utils import set_weight_attrs
+from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
+try:
+    from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin
+except Exception:
+    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "CompressedTensorsW8A8Int8MarlinMoEMethod",
+]
+
+def get_w8a8_int8_marlin_weights(
+         weight,
+         k_tile=64):
+    # 7168, 512
+    weight = weight.T
+    size_k, size_n = weight.shape
+    assert size_k // k_tile
+    weight = weight.reshape(size_k // k_tile, k_tile, size_n)
+    weight = weight.transpose(1, 2)
+    weight = weight.reshape(size_k // k_tile, size_n * k_tile)
+
+    return weight
+
+class CompressedTensorsMarlinMoEMethod(FusedMoEMethodBase):
+    @staticmethod
+    def get_moe_method(
+        quant_config: "SlimQuantCompressedTensorsMarlinConfig",  # type: ignore # noqa E501
+        layer: torch.nn.Module,
+    ) -> "CompressedTensorsMarlinMoEMethod":
+        # are supported + check if the layer is being ignored.
+        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_dynamic_token_w8a8(weight_quant, input_quant):
+            return CompressedTensorsW8A8Int8MarlinMoEMethod(quant_config)
+        else:
+            raise RuntimeError(
+                f"Slimquant_marlin does not support the FusedMoe scheme: {weight_quant}, {input_quant}")
+
+class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsMarlinConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        self.weight_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "weights")
+        self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        per_channel = (
+            self.weight_quant.strategy == QuantizationStrategy.CHANNEL
+            and self.input_quant.strategy == QuantizationStrategy.TOKEN)
+        if not per_channel:
+            raise ValueError(
+                "For INT8 Fused MoE layers, we require channelwise, "
+                "dynamic per token quantization. Found "
+                f"{self.weight_quant}, {self.input_quant}")
+
+        self.static_input_scales = not self.input_quant.dynamic
+        if self.static_input_scales:
+            raise ValueError(
+                "For INT8 Fused MoE layers, we require channelwise, "
+                "dynamic per token quantization. Found static input scales.")
+
+        
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
+        params_dtype = torch.int8
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_size,
+            dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        assert self.weight_quant.strategy == QuantizationStrategy.CHANNEL
+        w13_weight_scale = torch.nn.Parameter(torch.ones(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            1,
+            dtype=torch.float32),
+                                              requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                        hidden_size,
+                                                        1,
+                                                        dtype=torch.float32),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add PER-CHANNEL quantization for FusedMoE.weight_loader.
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value})
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        assert not self.static_input_scales
+        layer.w13_input_scale = None
+        layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        w1_marlin_list = []
+        for ii in range(layer.w13_weight.shape[0]):
+            w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
+            w1_marlin_list.append(w1_marlin_in)
+        w1_marlin = torch.stack(w1_marlin_list, dim=0)
+
+        w2_marlin_list = []
+        for ii in range(layer.w2_weight.shape[0]):
+            w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
+            w2_marlin_list.append(w2_marlin_in)
+        w2_marlin = torch.stack(w2_marlin_list, dim=0)
+
+        layer.w13_weight = Parameter(w1_marlin, requires_grad=False)
+        layer.w2_weight = Parameter(w2_marlin, requires_grad=False)
+
+    def create_moe_runner(
+        self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
+    ):
+        self.moe_runner_config = moe_runner_config
+
+    # def apply(
+    #     self,
+    #     layer: torch.nn.Module,
+    #     x: torch.Tensor,
+    #     router_logits: torch.Tensor,
+    #     top_k: int,
+    #     renormalize: bool,
+    #     use_grouped_topk: bool = False,
+    #     topk_group: Optional[int] = None,
+    #     num_expert_group: Optional[int] = None,
+    #     global_num_experts: int = -1,
+    #     expert_map: Optional[torch.Tensor] = None,
+    #     custom_routing_function: Optional[Callable] = None,
+    #     scoring_func: str = "softmax",
+    #     e_score_correction_bias: Optional[torch.Tensor] = None,
+    #     apply_router_weight_on_input: bool = False,
+    #     activation: str = "silu",
+    #     enable_eplb: bool = False,
+    #     use_nn_moe: Optional[bool] = False,
+    #     routed_scaling_factor: Optional[float] = None,
+    #     use_fused_gate: Optional[bool] = False,
+    #     expert_load_view: Optional[torch.Tensor] = None,
+    #     logical_to_physical_map: Optional[torch.Tensor] = None,
+    #     logical_replica_count: Optional[torch.Tensor] = None,
+    #     shared_output: Optional[torch.Tensor] = None,
+    # ) -> torch.Tensor:
+    #     from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+    #     if enable_eplb:
+    #         raise NotImplementedError(
+    #             "EPLB not supported for "
+    #             "`CompressedTensorsW8A8Int8MoEMethod` yet.")
+
+
+    #     topk_weights, topk_ids = FusedMoE.select_experts(
+    #         hidden_states=x,
+    #         router_logits=router_logits,
+    #         use_grouped_topk=use_grouped_topk,
+    #         top_k=top_k,
+    #         renormalize=renormalize,
+    #         topk_group=topk_group,
+    #         num_expert_group=num_expert_group,
+    #         custom_routing_function=custom_routing_function,
+    #         scoring_func=scoring_func,
+    #         routed_scaling_factor=routed_scaling_factor,
+    #         use_fused_gate=use_fused_gate,
+    #         e_score_correction_bias=e_score_correction_bias)
+
+    #     return fused_experts_impl_int8_marlin(
+    #         hidden_states=x,
+    #         w1=layer.w13_weight,
+    #         w2=layer.w2_weight,
+    #         topk_weights=topk_weights,
+    #         topk_ids=topk_ids,
+    #         inplace=True,
+    #         activation=activation,
+    #         apply_router_weight_on_input=apply_router_weight_on_input,
+    #         use_int8_w8a8=True,
+    #         per_channel_quant=True,
+    #         global_num_experts=global_num_experts,
+    #         expert_map=expert_map,
+    #         w1_scale=layer.w13_weight_scale,
+    #         w2_scale=layer.w2_weight_scale,
+    #         a1_scale=layer.w13_input_scale,
+    #         a2_scale=layer.w2_input_scale,
+    #         use_nn_moe=False,
+    #         shared_output=shared_output,
+    #         routed_scaling_factor=routed_scaling_factor)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        dispatch_output,
+    ) :
+        from sglang.srt.layers.moe.token_dispatcher.standard import StandardCombineInput
+        x = dispatch_output.hidden_states
+        topk_output = dispatch_output.topk_output
+        from sglang.srt.layers.moe.topk import apply_topk_weights_cpu
+
+        topk_weights, topk_ids, _ = topk_output
+        x, topk_weights = apply_topk_weights_cpu(
+            self.moe_runner_config.apply_router_weight_on_input, topk_weights, x
+        )
+        output = fused_experts_impl_int8_marlin(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=layer.moe_runner_config.activation,
+            apply_router_weight_on_input=self.moe_runner_config.apply_router_weight_on_input,
+            use_int8_w8a8=True,
+            per_channel_quant=True,
+            global_num_experts=layer.moe_runner_config.num_experts,
+            w1_scale=(layer.w13_weight_scale),
+            w2_scale=(layer.w2_weight_scale),
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            use_nn_moe=False,
+        )
+        return StandardCombineInput(hidden_states=output)
\ No newline at end of file

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -15,10 +15,11 @@ from sglang.srt.layers.parameter import (
 from sglang.srt.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme,
 )
-from sglang.srt.layers.quantization.int8_kernel import per_token_quant_int8
+# from sglang.srt.layers.quantization.int8_kernel import per_token_quant_int8
+from lmslim.layers.gemm.int8_utils import per_token_quant_int8
 from sglang.srt.layers.quantization.utils import requantize_with_max_scale
 from sglang.srt.utils import is_cuda
-
+from lmslim import quant_ops
 _is_cuda = is_cuda()
 if _is_cuda:
     from sgl_kernel import int8_scaled_mm
@@ -168,6 +169,6 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
         # TODO: add cutlass_scaled_mm_azp support
         x_q, x_scale = per_token_quant_int8(x)
 
-        return int8_scaled_mm(
+        return quant_ops.triton_scaled_mm(
             x_q, layer.weight, x_scale, layer.weight_scale, out_dtype=x.dtype, bias=bias
         )

python/sglang/srt/layers/quantization/slimquant_w4a8_marlin.py

@@ -95,7 +95,7 @@ class SlimQuantW4A8Int8MarlinConfig(QuantizationConfig):
     def override_quantization_method(
             cls, hf_quant_cfg, user_quant) -> Optional[str]:
         if hf_quant_cfg.get("quant_method") == "slimquant_w4a8" \
-                and user_quant == "slimquant_w4a8_marlin":
+                and user_quant in ("slimquant_w4a8_marlin", "slimquant_marlin"):
             return cls.get_name()
         return None
     def get_quant_method(

python/sglang/srt/server_args.py

@@ -94,6 +94,7 @@ QUANTIZATION_CHOICES = [
     "mxfp4",
     "compressed-tensors",  # for Ktransformers
     "slimquant_w4a8_marlin",
+    "slimquant_marlin",
 ]
 
 ATTENTION_BACKEND_CHOICES = [