Support compressed tensors fp8w8a8 (#4743)

.github/workflows/vllm-dependency-test.yml

+name: VLLM Dependency Test
+
+on:
+  push:
+    branches: [ main ]
+    paths:
+      - "python/pyproject.toml"
+      - "python/sglang/**"
+      - "test/**"
+      - "docs/**"
+      - "scripts/**"
+  pull_request:
+    branches: [ main ]
+    paths:
+      - "python/pyproject.toml"
+      - "python/sglang/**"
+      - "test/**"
+      - "docs/**"
+      - "scripts/**"
+
+concurrency:
+  group: vllm-dependency-test-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  vllm-dependency-test:
+    if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') &&
+        github.event.pull_request.draft == false
+    runs-on: 1-gpu-runner
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install dependencies
+        env:
+          FLASHINFER_REPO: 'https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python'
+        run: |
+          bash scripts/ci_install_dependency.sh
+          pip install "vllm>=0.6.4.post1,<=0.7.2"
+
+      - name: Run VLLM dependency tests
+        timeout-minutes: 60
+        run: |
+          cd test/srt
+          python3 run_suite.py --suite vllm_dependency_test --timeout-per-file 3600

python/pyproject.toml

@@ -47,7 +47,6 @@ srt = [
     "sgl-kernel==0.0.5.post3",
     "flashinfer_python==0.2.3",
     "torch==2.5.1",
-    "vllm>=0.6.4.post1,<=0.7.2",
     "cuda-python",
     "outlines>=0.0.44,<=0.1.11",
 ]

python/sglang/srt/configs/model_config.py

@@ -22,7 +22,11 @@ import torch
 from transformers import PretrainedConfig
 
 from sglang.srt.hf_transformers_utils import get_config, get_context_length
-from sglang.srt.layers.quantization import QUANTIZATION_METHODS
+from sglang.srt.layers.quantization import (
+    BASE_QUANTIZATION_METHODS,
+    QUANTIZATION_METHODS,
+    VLLM_AVAILABLE,
+)
 from sglang.srt.utils import get_bool_env_var, is_hip
 
 logger = logging.getLogger(__name__)
@@ -235,7 +239,12 @@ class ModelConfig:
 
     # adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
     def _verify_quantization(self) -> None:
-        supported_quantization = [*QUANTIZATION_METHODS]
+        # Select supported quantization methods based on vllm availability
+        if VLLM_AVAILABLE:
+            supported_quantization = [*QUANTIZATION_METHODS]
+        else:
+            supported_quantization = [*BASE_QUANTIZATION_METHODS]
+
         rocm_supported_quantization = [
             "awq",
             "gptq",
@@ -273,7 +282,11 @@ class ModelConfig:
             quant_method = quant_cfg.get("quant_method", "").lower()
 
             # Detect which checkpoint is it
-            for _, method in QUANTIZATION_METHODS.items():
+            # Only iterate through currently available quantization methods
+            available_methods = (
+                QUANTIZATION_METHODS if VLLM_AVAILABLE else BASE_QUANTIZATION_METHODS
+            )
+            for _, method in available_methods.items():
                 quantization_override = method.override_quantization_method(
                     quant_cfg, self.quantization
                 )

python/sglang/srt/distributed/parallel_state.py

@@ -1316,7 +1316,10 @@ vllm_get_world_group = None
 
 
 def monkey_patch_vllm_parallel_state(reverse: bool = False):
-    import vllm.distributed.parallel_state as vllm_parrlel_state
+    try:
+        import vllm.distributed.parallel_state as vllm_parrlel_state
+    except ImportError:
+        return
 
     global vllm_get_pp_group, vllm_get_tp_group, vllm_get_world_group
     if vllm_get_pp_group is None:

python/sglang/srt/layers/linear.py

@@ -23,6 +23,7 @@ from sglang.srt.layers.parameter import (
     PackedvLLMParameter,
     PerTensorScaleParameter,
     RowvLLMParameter,
+    _ColumnvLLMParameter,
 )
 from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
@@ -423,8 +424,6 @@ class ColumnParallelLinear(LinearBase):
             assert loaded_weight.numel() == 1
             loaded_weight = loaded_weight.reshape(1)
 
-        from sglang.srt.layers.parameter import _ColumnvLLMParameter
-
         if isinstance(param, _ColumnvLLMParameter):
             param.load_column_parallel_weight(
                 loaded_weight,
@@ -1247,7 +1246,7 @@ class RowParallelLinear(LinearBase):
             assert loaded_weight.numel() == 1
             loaded_weight = loaded_weight.reshape(1)
 
-        if isinstance(param, BasevLLMParameter):
+        if isinstance(param, RowvLLMParameter):
             # This `BasevLLMParameter` is defined in sglang/srt/layers/parameter.py,
             # It supports additional parameters like tp_rank and use_presharded_weights.
             param.load_row_parallel_weight(

python/sglang/srt/layers/moe/fused_moe_native.py

@@ -8,7 +8,6 @@ from typing import Callable, Optional
 import torch
 from torch.nn import functional as F
 
-from sglang.srt.layers.activation import GeluAndMul, SiluAndMul
 from sglang.srt.layers.moe.topk import select_experts
 
 
@@ -69,6 +68,8 @@ def moe_forward_native(
     activation: str = "silu",
 ) -> torch.Tensor:
 
+    from sglang.srt.layers.activation import GeluAndMul, SiluAndMul
+
     topk_weights, topk_ids = select_experts(
         hidden_states=x,
         router_logits=router_logits,

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -305,6 +305,7 @@ class FusedMoE(torch.nn.Module):
         self.use_presharded_weights = use_presharded_weights
         self.inplace = inplace
         self.no_combine = no_combine
+        self.local_num_experts = num_experts
 
         if quant_config is None:
             self.quant_method: Optional[QuantizeMethodBase] = (
@@ -629,8 +630,6 @@ class FusedMoE(torch.nn.Module):
             custom_routing_function=self.custom_routing_function,
             correction_bias=self.correction_bias,
             activation=self.activation,
-            inplace=self.inplace,
-            no_combine=self.no_combine,
         )
 
         if self.reduce_results and self.tp_size > 1:

python/sglang/srt/layers/moe/topk.py

@@ -17,11 +17,12 @@ from typing import Callable, Optional
 import torch
 import torch.nn.functional as F
 
-from sglang.srt.utils import get_compiler_backend, is_cuda
+from sglang.srt.utils import get_compiler_backend, is_cuda, is_hip
 
 _is_cuda = is_cuda()
+_is_hip = is_hip()
 
-from sglang.srt.managers.utils import ExpertDistributionRecorder
+from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
 
 expert_distribution_recorder = ExpertDistributionRecorder()
 
@@ -53,10 +54,10 @@ def fused_topk(
     topk: int,
     renormalize: bool,
 ):
-    if _is_cuda:
+    if _is_cuda or _is_hip:
         from sgl_kernel import topk_softmax
     else:
-        from vllm import _custom_ops as ops
+        from vllm import _custom_ops as vllm_ops
 
     assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
 
@@ -70,7 +71,7 @@ def fused_topk(
         M, topk, dtype=torch.int32, device=hidden_states.device
     )
 
-    if _is_cuda:
+    if _is_cuda or _is_hip:
         topk_softmax(
             topk_weights,
             topk_ids,
@@ -78,7 +79,7 @@ def fused_topk(
             gating_output.float(),
         )
     else:
-        ops.topk_softmax(
+        vllm_ops.topk_softmax(
             topk_weights,
             topk_ids,
             token_expert_indicies,

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

@@ -12,9 +12,6 @@ try:
     from vllm.model_executor.layers.quantization.awq import AWQConfig
     from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
     from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
-    from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (
-        CompressedTensorsConfig,
-    )
     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.fbgemm_fp8 import FBGEMMFp8Config
@@ -26,6 +23,8 @@ try:
     from vllm.model_executor.layers.quantization.qqq import QQQConfig
     from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
 
+    from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
+
     VLLM_AVAILABLE = True
 except ImportError:
     VLLM_AVAILABLE = False
@@ -44,8 +43,10 @@ except ImportError:
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
+from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import (
+    CompressedTensorsConfig,
+)
 from sglang.srt.layers.quantization.fp8 import Fp8Config
-from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
 from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
 from sglang.srt.layers.quantization.w8a8_fp8 import W8A8Fp8Config
 from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
@@ -55,10 +56,9 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "fp8": Fp8Config,
     "blockwise_int8": BlockInt8Config,
     "modelopt": ModelOptFp8Config,
-    "gptq_marlin": GPTQMarlinConfig,
-    "gptq": GPTQConfig,
     "w8a8_int8": W8A8Int8Config,
     "w8a8_fp8": W8A8Fp8Config,
+    "compressed-tensors": CompressedTensorsConfig,
 }
 
 # Add vllm-dependent methods if available
@@ -74,10 +74,11 @@ if VLLM_AVAILABLE:
         "gguf": GGUFConfig,
         "gptq_marlin_24": GPTQMarlin24Config,
         "awq_marlin": AWQMarlinConfig,
-        "compressed-tensors": CompressedTensorsConfig,
         "bitsandbytes": BitsAndBytesConfig,
         "qqq": QQQConfig,
         "experts_int8": ExpertsInt8Config,
+        "gptq_marlin": GPTQMarlinConfig,
+        "gptq": GPTQConfig,
     }
     QUANTIZATION_METHODS.update(VLLM_QUANTIZATION_METHODS)
 

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

@@ -38,6 +38,11 @@ class QuantizeMethodBase(ABC):
 class QuantizationConfig(ABC):
     """Base class for quantization configs."""
 
+    def __init__(self):
+        super().__init__()
+        # mapping is updated by models as they initialize
+        self.packed_modules_mapping: Dict[str, List[str]] = dict()
+
     @abstractmethod
     def get_name(self) -> str:
         """Name of the quantization method."""

python/sglang/srt/layers/quantization/compressed_tensors/README.md

+# quantization compressed_tensors module
+
+To support compressed_tensors format quantization models, we adapted https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors into SGLang.
+
+
+For practical purposes, we have only applied the compressed_tensors format of `w8a8_fp8`. If you have requirements for other formats, you can submit an issue through this [link](https://github.com/sgl-project/sglang/issues).

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

+# 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 contextlib import suppress
+from typing import Any, Dict, List, Literal, NamedTuple, Optional, Tuple, cast
+
+import torch
+from compressed_tensors.config import (
+    CompressionFormat,
+    SparsityCompressionConfig,
+    SparsityStructure,
+)
+from compressed_tensors.quantization import (
+    QuantizationArgs,
+    QuantizationStrategy,
+    QuantizationType,
+)
+from pydantic import BaseModel
+
+from sglang.srt.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.quantization.base_config import (
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
+    CompressedTensorsMoEMethod,
+)
+from sglang.srt.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme,
+    CompressedTensorsW8A8Fp8,
+)
+from sglang.srt.layers.quantization.compressed_tensors.utils import (
+    find_matched_target,
+    is_activation_quantization_format,
+    should_ignore_layer,
+)
+from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
+
+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 DeviceCapability(NamedTuple):
+    major: int
+    minor: int
+
+    def as_version_str(self) -> str:
+        return f"{self.major}.{self.minor}"
+
+    def to_int(self) -> int:
+        """
+        Express device capability as an integer ``<major><minor>``.
+
+        It is assumed that the minor version is always a single digit.
+        """
+        assert 0 <= self.minor < 10
+        return self.major * 10 + self.minor
+
+
+class CompressedTensorsConfig(QuantizationConfig):
+
+    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,
+    ):
+        super().__init__()
+        self.ignore = ignore
+        self.quant_format = quant_format
+        # Map from [target -> scheme]
+        self.target_scheme_map = target_scheme_map
+        self.kv_cache_scheme = kv_cache_scheme
+        self.sparsity_scheme_map = sparsity_scheme_map
+        self.sparsity_ignore_list = sparsity_ignore_list
+        self.config = config
+
+    def get_linear_method(self) -> "CompressedTensorsLinearMethod":
+        return CompressedTensorsLinearMethod(self)
+
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    def get_name(self) -> str:
+        return "compressed_tensors"
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_quant_method(
+        self,
+        layer: torch.nn.Module,
+        prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+
+        # Check if the layer is skipped for quantization.
+        # TODO (@robertgshaw2): support module names
+        if should_ignore_layer(
+            prefix, ignore=self.ignore, fused_mapping=self.packed_modules_mapping
+        ):
+            return UnquantizedLinearMethod()
+        if isinstance(layer, LinearBase):
+            scheme = self.get_scheme(layer=layer, layer_name=prefix)
+            if scheme is None:
+                return UnquantizedLinearMethod()
+            layer.scheme = scheme
+            return CompressedTensorsLinearMethod(self)
+        if isinstance(layer, FusedMoE):
+            return CompressedTensorsMoEMethod.get_moe_method(self)
+        return None
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
+        ignore: List[str] = cast(List[str], config.get("ignore", []))
+        quant_format = cast(str, config.get("format"))
+        target_scheme_map = cls._quantization_scheme_map_from_config(config=config)
+        sparsity_scheme_map, sparsity_ignore_list = cls._parse_sparsity_config(
+            config=config
+        )
+
+        return cls(
+            target_scheme_map=target_scheme_map,
+            ignore=ignore,
+            quant_format=quant_format,
+            sparsity_scheme_map=sparsity_scheme_map,
+            sparsity_ignore_list=sparsity_ignore_list,
+            config=config,
+        )
+
+    @classmethod
+    def _parse_sparsity_config(
+        cls, config: Dict[str, Any]
+    ) -> Tuple[Dict[str, SparsityCompressionConfig], List[str]]:
+        """
+        :param config: The `quantization_config` dictionary from config.json
+        :return: A tuple with two elements
+            1. A dictionary mapping target layer names to their corresponding
+                sparsity_config
+            2. A list of layer names to ignore for sparsity
+        """
+        if not (sparsity_config := config.get(SPARSITY_CONFIG_NAME)):
+            return dict(), []
+
+        sparsity_config = SparsityCompressionConfig.model_validate(sparsity_config)
+        sparse_scheme_map: Dict[str, SparsityCompressionConfig] = {
+            target: sparsity_config for target in sparsity_config.targets or list()
+        }
+        sparsity_ignore_list = sparsity_config.ignore or list()
+        return sparse_scheme_map, sparsity_ignore_list
+
+    @classmethod
+    def _quantization_scheme_map_from_config(
+        cls, config: Dict[str, Any]
+    ) -> QUANTIZATION_SCHEME_MAP_TYPE:
+        """
+        :param config: The `quantization_config` dictionary from config.json
+        :return: A dictionary mapping target layer names to their corresponding
+            quantization_args for weights and input activations
+        """
+        target_scheme_map: Dict[str, Any] = dict()
+        quant_format = cast(str, config.get("format"))
+
+        # The quant_config has multiple config_groups, each containing
+        # an input_activations key with details about how the activations are
+        # quantized, a weights key indicating how the weights are quantized,
+        # and a list of targets under the `targets` key, dictating which
+        # layers are impacted by the quantization details. The quantization
+        # details follow the structure defined by the QuantizationArgs
+        # pydantic model, which is used to verify the structure of the
+        # quant_config and also store the details for later use.
+
+        config_groups = config.get("config_groups", dict())
+        for _, quant_config in config_groups.items():
+            targets = quant_config.get("targets")
+            for target in targets:
+                target_scheme_map[target] = {}
+                target_scheme_map[target]["weights"] = QuantizationArgs.model_validate(
+                    quant_config.get("weights")
+                )
+
+                target_scheme_map[target]["input_activations"] = None
+                if is_activation_quantization_format(quant_format):
+                    input_activations = quant_config.get("input_activations")
+                    # The only case where we have activation quant supported
+                    # but no input_activations provided in the config
+                    # should be w8a16fp8 w8a16fp8 can also run for cases where
+                    # there is an input_quant but it is ignored
+                    if not input_activations:
+                        assert (
+                            target_scheme_map[target]["weights"].type
+                            == QuantizationType.FLOAT
+                        )
+                    else:
+                        target_scheme_map[target]["input_activations"] = (
+                            QuantizationArgs.model_validate(  # noqa: E501
+                                quant_config.get("input_activations")
+                            )
+                        )
+        return target_scheme_map
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    def _check_scheme_supported(self, min_capability: int, error: bool = True) -> bool:
+        capability_tuple = DeviceCapability(*torch.cuda.get_device_capability())
+
+        if capability_tuple is not None:
+            capability = capability_tuple.to_int()
+            supported = capability >= min_capability
+            if error and not supported:
+                raise RuntimeError(
+                    "Quantization scheme is not supported for ",
+                    f"the current GPU. Min capability: {min_capability}. ",
+                    f"Current capability: {capability}.",
+                )
+            return supported
+        else:
+            return False
+
+    def _is_static_tensor_w8a8(
+        self, weight_quant: BaseModel, input_quant: BaseModel
+    ) -> bool:
+        is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
+        weight_strategy = (
+            weight_quant.strategy == QuantizationStrategy.TENSOR.value
+            or weight_quant.strategy == QuantizationStrategy.CHANNEL.value
+        )
+        is_tensor = (
+            weight_strategy
+            and input_quant.strategy == QuantizationStrategy.TENSOR.value
+        )
+        is_static = not weight_quant.dynamic and not input_quant.dynamic
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_tensor and weight_quant.symmetric and is_static
+
+    def _is_dynamic_token_w8a8(
+        self, weight_quant: BaseModel, input_quant: BaseModel
+    ) -> bool:
+        is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
+        weight_strategy = (
+            weight_quant.strategy == QuantizationStrategy.TENSOR.value
+            or weight_quant.strategy == QuantizationStrategy.CHANNEL.value
+        )
+        is_token = (
+            weight_strategy and input_quant.strategy == QuantizationStrategy.TOKEN.value
+        )
+        is_dynamic = not weight_quant.dynamic and input_quant.dynamic
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_token and weight_quant.symmetric and is_dynamic
+
+    def _is_fp8_w8a8(self, weight_quant: BaseModel, input_quant: BaseModel) -> bool:
+        # Confirm weights and activations quantized.
+        if weight_quant is None or input_quant is None:
+            return False
+
+        # Confirm weight scheme is supported.
+        is_floating_point = (
+            weight_quant.type == QuantizationType.FLOAT
+            and input_quant.type == QuantizationType.FLOAT
+        )
+        is_symmetric_weight = weight_quant.symmetric
+        is_static_weight = not weight_quant.dynamic
+        is_per_tensor_or_channel_weight = weight_quant.strategy in [
+            QuantizationStrategy.TENSOR,
+            QuantizationStrategy.CHANNEL,
+        ]
+        if not (
+            is_floating_point
+            and is_symmetric_weight
+            and is_static_weight
+            and is_per_tensor_or_channel_weight
+        ):
+            return False
+
+        # Dynamic quantization is always supported if weights supported.
+        if input_quant.dynamic:
+            return True
+
+        # Confirm activation scheme is supported.
+        is_symmetric_activation = input_quant.symmetric
+        is_per_tensor_activation = input_quant.strategy == QuantizationStrategy.TENSOR
+        return is_symmetric_activation and is_per_tensor_activation
+
+    def _is_fp8_w8a16(self, weight_quant: BaseModel, input_quant: BaseModel) -> bool:
+        # Confirm weights quantized.
+        if weight_quant is None:
+            return False
+
+        # Confirm we have floating points.
+        if weight_quant.type != QuantizationType.FLOAT:
+            return False
+
+        # Confirm weight scheme is supported.
+        is_symmetric_weight = weight_quant.symmetric
+        is_static_weight = not weight_quant.dynamic
+        is_per_tensor_or_channel_weight = weight_quant.strategy in [
+            QuantizationStrategy.TENSOR,
+            QuantizationStrategy.CHANNEL,
+        ]
+        if not (
+            is_symmetric_weight
+            and is_static_weight  # noqa: SIM103
+            and is_per_tensor_or_channel_weight
+        ):
+            return False
+
+        # All conditions satisfied.
+        return True
+
+    def _is_wNa16_group_channel(
+        self, weight_quant: BaseModel, input_quant: BaseModel
+    ) -> bool:
+        input_quant_none = input_quant is None
+        is_symmetric = weight_quant.symmetric
+        is_channel_group = (
+            weight_quant.strategy == QuantizationStrategy.CHANNEL.value
+            or weight_quant.strategy == QuantizationStrategy.GROUP.value
+        )
+        is_static = not weight_quant.dynamic
+
+        return is_channel_group and input_quant_none and is_symmetric and is_static
+
+    def _get_scheme_from_parts(
+        self, weight_quant: BaseModel, input_quant: BaseModel
+    ) -> "CompressedTensorsScheme":
+
+        # 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
+            ):
+                return CompressedTensorsWNA16(
+                    num_bits=weight_quant.num_bits,
+                    strategy=weight_quant.strategy,
+                    group_size=weight_quant.group_size,
+                    actorder=weight_quant.actorder,
+                )
+
+        if is_activation_quantization_format(self.quant_format):
+            if self._is_fp8_w8a8(weight_quant, input_quant):
+                is_fp8_w8a8_supported = self._check_scheme_supported(
+                    CompressedTensorsW8A8Fp8.get_min_capability(), error=False
+                )
+                if is_fp8_w8a8_supported:
+                    return CompressedTensorsW8A8Fp8(
+                        strategy=weight_quant.strategy,
+                        is_static_input_scheme=(
+                            input_quant and not input_quant.dynamic
+                        ),
+                    )
+                else:
+                    # note: input_quant will be present for converted models;
+                    # will be ignored during inference post loading
+                    return CompressedTensorsW8A16Fp8(
+                        strategy=weight_quant.strategy,
+                        is_static_input_scheme=not input_quant.dynamic,
+                    )
+
+            # 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,
+                    is_static_input_scheme=is_static_input_scheme,
+                )
+
+            if self._is_static_tensor_w8a8(weight_quant, input_quant):
+                return CompressedTensorsW8A8Int8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=True,
+                    input_symmetric=input_quant.symmetric,
+                )
+
+            if self._is_dynamic_token_w8a8(weight_quant, input_quant):
+                return CompressedTensorsW8A8Int8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=False,
+                    input_symmetric=input_quant.symmetric,
+                )
+
+        raise NotImplementedError("No compressed-tensors compatible scheme was found.")
+
+    def get_scheme(
+        self, layer: torch.nn.Module, layer_name: Optional[str] = None
+    ) -> Optional["CompressedTensorsScheme"]:
+        """
+        compressed-tensors supports non uniform in the following way:
+
+        targets of config_groups: There can be N config_groups which each
+            have a quantization scheme. Each config_group has a list of targets
+            which can be a full layer_name, a regex for a layer_name, or
+            an nn.Module name.
+
+        Detect whether a layer_name is found in any target and
+        use the quantization scheme corresponding to the matched target
+        to select the CompressedTensorsScheme used for infernece.
+        """
+
+        # Find the "target" in the compressed-tensors config
+        # that our layer conforms to.
+        # TODO (@robertgshaw): 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
+
+        # Will be empty for models with only sparsity
+        weight_quant = input_quant = None
+        if self.target_scheme_map:
+            matched_target = find_matched_target(
+                layer_name=layer_name,
+                module=layer,
+                targets=self.target_scheme_map.keys(),
+                fused_mapping=self.packed_modules_mapping,
+            )
+
+            scheme_dict = self.target_scheme_map[matched_target]
+            weight_quant = scheme_dict.get("weights")
+            input_quant = scheme_dict.get("input_activations")
+
+        # Find the sparsity scheme of the layer
+        # assume that fused layers inerhit first component's sparsity scheme
+        sparsity_targets = self.sparsity_scheme_map.keys() - set(
+            self.sparsity_ignore_list
+        )
+        sparsity_scheme: Optional[SparsityCompressionConfig] = None
+        with suppress(ValueError):
+            matched_target = find_matched_target(
+                layer_name=layer_name,
+                module=layer,
+                targets=sparsity_targets,
+                fused_mapping=self.packed_modules_mapping,
+            )
+            sparsity_scheme = self.sparsity_scheme_map[matched_target]
+
+        if self.supports_cutlass_24(
+            weight_quant=weight_quant,
+            input_quant=input_quant,
+            sparsity_scheme=sparsity_scheme,
+        ):
+            if not VLLM_AVAILABLE:
+                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 "
+                "not supported by Compressed Tensors. "
+                "Falling back to UnquantizedLinearMethod"
+            )
+            return None
+
+        else:
+            # Find the quant_scheme
+            scheme = self._get_scheme_from_parts(  # type: ignore
+                weight_quant=weight_quant,
+                input_quant=input_quant,
+            )
+
+        # Raise error if device does not support the scheme
+        # (e.g. fp8 needs ada lovelace)
+        self._check_scheme_supported(scheme.get_min_capability())
+        logger.debug("Using scheme: %s for %s", scheme.__class__.__name__, layer_name)
+        return scheme
+
+    def get_cache_scale(self, name: str) -> Optional[str]:
+        """
+        Check whether the param name matches the format for k/v cache scales
+        in compressed-tensors. If this is the case, return its equivalent
+        param name expected by vLLM
+
+        :param name: param name
+        :return: matching param name for KV cache scale in vLLM
+        """
+        if name.endswith(".output_scale") and ".k_proj" in name:
+            return name.replace(".k_proj.output_scale", ".attn.k_scale")
+        if name.endswith(".output_scale") and ".v_proj" in name:
+            return name.replace(".v_proj.output_scale", ".attn.v_scale")
+        # If no matches, return None
+        return None
+
+    @staticmethod
+    def supports_cutlass_24(
+        weight_quant: Optional[QuantizationArgs],
+        input_quant: Optional[QuantizationArgs],
+        sparsity_scheme: Optional[SparsityCompressionConfig] = None,
+    ) -> bool:
+        """
+        Check if the layer is supported by the Cutlass 2:4 Kernel
+        Conditions:
+            - Overarching condition: Sparsity Structure is 2:4
+            - Unquantized cases are supported
+            - Weight only quantization is not-supported
+            - Supported weight quantization strategies are TENSOR and CHANNEL
+            - Supported input quantization strategies are TENSOR and TOKEN
+            - Only 8 bit quantization is supported
+
+        :return: True if the layer is supported by the Cutlass 2:4 Kernel
+            False otherwise
+        """
+        if sparsity_scheme is None:
+            return False
+
+        is_valid_sparsity_structure: bool = (
+            sparsity_scheme.sparsity_structure == SparsityStructure.TWO_FOUR.value
+        )
+
+        valid_compressors = {
+            CompressionFormat.dense.value,
+            CompressionFormat.sparse_24_bitmask.value,
+        }
+
+        is_valid_sparsity = (
+            is_valid_sparsity_structure and sparsity_scheme.format in valid_compressors
+        )
+
+        if not is_valid_sparsity:
+            return False
+
+        # Unquantized cases are supported
+        if weight_quant is None and input_quant is None:
+            return True
+
+        # Weight only quantization is not-supported
+        if weight_quant is not None and input_quant is None:
+            return False
+
+        supported_weight_quant_strategies = [
+            QuantizationStrategy.TENSOR.value,
+            QuantizationStrategy.CHANNEL.value,
+        ]
+
+        assert weight_quant is not None
+        assert input_quant is not None
+        if weight_quant.strategy not in supported_weight_quant_strategies:
+            return False
+
+        supported_input_quant_strategies = [
+            QuantizationStrategy.TENSOR.value,
+            QuantizationStrategy.TOKEN.value,
+        ]
+
+        if input_quant.strategy not in supported_input_quant_strategies:
+            return False
+
+        return weight_quant.num_bits == input_quant.num_bits == 8
+
+
+class CompressedTensorsLinearMethod(LinearMethodBase):
+
+    def __init__(self, quantization_config: CompressedTensorsConfig):
+        self.quantization_config = quantization_config
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.scheme.process_weights_after_loading(layer)
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        """
+        Use the CompressedTensorsScheme associated with each layer to create
+        the necessary parameters for the layer. See LinearMethodBase for param
+        details
+        """
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.scheme.create_weights(
+            layer=layer,
+            input_size=input_size,
+            input_size_per_partition=input_size_per_partition,
+            output_partition_sizes=output_partition_sizes,
+            output_size=output_size,
+            params_dtype=params_dtype,
+            weight_loader=weight_loader,
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ):
+        """
+        Use the output of create_weights and the CompressedTensorsScheme
+        associated with the layer to apply the forward pass with the
+        layer input.  See LinearMethodBase for param details
+
+        """
+
+        scheme = layer.scheme
+        if scheme is None:
+            raise ValueError("A scheme must be defined for each layer")
+        return scheme.apply_weights(layer, x, bias=bias)

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

+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
+# SPDX-License-Identifier: Apache-2.0
+
+import enum
+import logging
+from enum import Enum
+from typing import Callable, List, Optional
+
+import torch
+from compressed_tensors import CompressionFormat
+from compressed_tensors.quantization import QuantizationStrategy
+
+from sglang.srt.layers.moe.fused_moe_triton import (
+    FusedMoE,
+    FusedMoEMethodBase,
+    FusedMoeWeightScaleSupported,
+)
+from sglang.srt.layers.moe.topk import select_experts
+from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
+from sglang.srt.layers.quantization.utils import (
+    all_close_1d,
+    is_cuda,
+    is_fp8_fnuz,
+    per_tensor_dequantize,
+    replace_parameter,
+)
+from sglang.srt.utils import set_weight_attrs
+
+_is_cuda = is_cuda()
+
+if _is_cuda:
+    from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
+else:
+    from vllm import _custom_ops as vllm_ops
+
+try:
+    import vllm
+
+    VLLM_AVAILABLE = True
+except ImportError:
+    VLLM_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+
+class GPTQMarlinState(Enum):
+    REPACK = enum.auto()
+    READY = enum.auto()
+
+
+__all__ = [
+    "CompressedTensorsMoEMethod",
+    "CompressedTensorsW8A8Fp8MoEMethod",
+    "CompressedTensorsWNA16MoEMethod",
+]
+
+
+class CompressedTensorsMoEMethod(FusedMoEMethodBase):
+
+    @staticmethod
+    def get_moe_method(
+        quant_config: "CompressedTensorsConfig",  # type: ignore # noqa E501
+    ) -> "CompressedTensorsMoEMethod":
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # 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_wNa16_group_channel(weight_quant, input_quant):
+            if not VLLM_AVAILABLE:
+                raise ImportError(
+                    "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)
+        else:
+            raise RuntimeError(
+                f"Unsupported FusedMoe scheme: {weight_quant}, {input_quant}"
+            )
+
+
+class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+        self, quant_config: "CompressedTensorsConfig"  # 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"
+        )
+
+        if not (
+            self.weight_quant.strategy == QuantizationStrategy.TENSOR
+            and self.input_quant.strategy == QuantizationStrategy.TENSOR
+        ):
+            raise ValueError(
+                "For FP8 Fused MoE layers, only per-tensor scales "
+                "for weights and activations are supported. Found "
+                f"{self.weight_quant}, {self.input_quant}"
+            )
+
+        self.static_input_scales = not self.input_quant.dynamic
+
+    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,
+    ):
+
+        params_dtype = torch.float8_e4m3fn
+
+        # 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
+        # Allocate 2 scales for w1 and w3 respectively.
+        # They will be combined to a single scale after weight loading.
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, 2, 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, dtype=torch.float32), requires_grad=False
+        )
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
+        )
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        if self.static_input_scales:
+            w13_input_scale = torch.nn.Parameter(
+                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+            )
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(
+                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+            )
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Fp8 moe kernels require a single activation scale.
+        # We take the max of all the scales in case they differ.
+        if self.static_input_scales:
+            if layer.w13_input_scale is None or layer.w2_input_scale is None:
+                raise ValueError(
+                    "QuantConfig has static quantization, but found "
+                    "activation scales are None."
+                )
+            if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
+                layer.w2_input_scale
+            ):
+                logger.warning(
+                    "Found input_scales that are not equal for "
+                    "fp8 MoE layer. Using the maximum across experts "
+                    "for each layer."
+                )
+            layer.w13_input_scale = torch.nn.Parameter(
+                layer.w13_input_scale.max(), requires_grad=False
+            )
+            layer.w2_input_scale = torch.nn.Parameter(
+                layer.w2_input_scale.max(), requires_grad=False
+            )
+
+        if is_fp8_fnuz():
+            # Normalize the weights and scales
+            w13_weight, w13_weight_scale, w13_input_scale = (
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w13_weight, layer.w13_weight_scale, layer.w13_input_scale
+                )
+            )
+            w2_weight, w2_weight_scale, w2_input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                layer.w2_weight, layer.w2_weight_scale, layer.w2_input_scale
+            )
+            # Reset the parameter
+            layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
+            layer.w13_weight_scale = torch.nn.Parameter(
+                w13_weight_scale, requires_grad=False
+            )
+            if w13_input_scale is not None:
+                layer.w13_input_scale = torch.nn.Parameter(
+                    w13_input_scale, requires_grad=False
+                )
+            layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
+            layer.w2_weight_scale = torch.nn.Parameter(
+                w2_weight_scale, requires_grad=False
+            )
+            if w2_input_scale is not None:
+                layer.w2_input_scale = torch.nn.Parameter(
+                    w2_input_scale, requires_grad=False
+                )
+
+        # Fp8 moe kernel needs single weight scale for w13 per expert.
+        # We take the max then dequant and requant each expert.
+        assert layer.w13_weight_scale is not None
+        shard_size = layer.intermediate_size_per_partition
+        max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+        for expert_id in range(layer.local_num_experts):
+            start = 0
+            for shard_id in range(2):
+                dq_weight = per_tensor_dequantize(
+                    layer.w13_weight[expert_id][start : start + shard_size, :],
+                    layer.w13_weight_scale[expert_id][shard_id],
+                )
+
+                if _is_cuda:
+                    layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
+                        sgl_scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
+                    )
+                else:
+                    layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
+                        vllm_ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
+                    )
+                start += shard_size
+
+        layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False)
+
+    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",
+        correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
+    ) -> torch.Tensor:
+        from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
+
+        topk_weights, topk_ids = 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,
+            correction_bias=correction_bias,
+        )
+
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            use_fp8_w8a8=True,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+        )
+
+
+class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+        self, quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # are supported + check if the layer is being ignored.
+        config = self.quant_config.target_scheme_map["Linear"].get("weights")
+        self.num_bits = config.num_bits
+        self.packed_factor = 32 // config.num_bits
+        self.strategy = config.strategy
+        self.group_size = config.group_size
+        self.actorder = config.actorder
+        assert config.symmetric, "Only symmetric quantization is supported for MoE"
+
+        if not (
+            self.quant_config.quant_format == CompressionFormat.pack_quantized.value
+            and self.num_bits in WNA16_SUPPORTED_BITS
+        ):
+            raise ValueError(
+                "For Fused MoE layers, only ",
+                f"{CompressionFormat.pack_quantized.value} ",
+                "is supported for the following bits: ",
+                f"{WNA16_SUPPORTED_BITS}",
+            )
+
+    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,
+    ):
+
+        assert (
+            params_dtype == torch.float16
+        ), "float16 is required for MoE compressed models. Set dtype=torch.float16"  # noqa: E501
+
+        intermediate_size_full = extra_weight_attrs.pop("intermediate_size_full")
+
+        # Will transpose the loaded weight along the
+        # intermediate and hidden dim sizes. Will
+        # shard for TP along the transposed dims
+        extra_weight_attrs.update(
+            {"is_transposed": True, "quant_method": self.strategy}
+        )
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size // self.packed_factor,
+                2 * intermediate_size_per_partition,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_packed", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size_per_partition // self.packed_factor,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight_packed", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # In the case where we have actorder/g_idx,
+        # we do not partition the w2 scales
+        load_full_w2 = self.actorder and self.group_size != -1
+        w2_scales_size = (
+            intermediate_size_full if load_full_w2 else intermediate_size_per_partition
+        )
+
+        self.is_k_full = (not self.actorder) or (
+            intermediate_size_per_partition == intermediate_size_full
+        )
+
+        if self.strategy == "channel":
+            num_groups_w2 = num_groups_w13 = 1
+            self.group_size = -1
+        else:
+            num_groups_w2 = w2_scales_size // self.group_size
+            num_groups_w13 = hidden_size // self.group_size
+
+        w13_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                num_groups_w13,
+                2 * intermediate_size_per_partition,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_scale", w13_scale)
+        set_weight_attrs(w13_scale, extra_weight_attrs)
+
+        w2_scale = torch.nn.Parameter(
+            torch.ones(num_experts, num_groups_w2, hidden_size, dtype=params_dtype),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight_scale", w2_scale)
+        set_weight_attrs(w2_scale, extra_weight_attrs)
+        set_weight_attrs(w2_scale, {"load_full_w2": load_full_w2})
+
+        w2_weight_shape = torch.nn.Parameter(
+            torch.empty(num_experts, 2), requires_grad=False
+        )
+        layer.register_parameter("w2_weight_shape", w2_weight_shape)
+        set_weight_attrs(w2_weight_shape, extra_weight_attrs)
+        w13_weight_shape = torch.nn.Parameter(
+            torch.empty(num_experts, 2), requires_grad=False
+        )
+
+        layer.register_parameter("w13_weight_shape", w13_weight_shape)
+        set_weight_attrs(w13_weight_shape, extra_weight_attrs)
+
+        w13_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_g_idx", w13_g_idx)
+        set_weight_attrs(w13_g_idx, extra_weight_attrs)
+
+        w2_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size_per_partition,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight_g_idx", w2_g_idx)
+        set_weight_attrs(w2_g_idx, extra_weight_attrs)
+
+        w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx_sort_indices", w13_g_idx_sort_indices)
+        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
+
+        w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size_per_partition,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx_sort_indices", w2_g_idx_sort_indices)
+        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
+
+        layer.a13_scale = None
+        layer.a2_scale = None
+        layer.marlin_state = GPTQMarlinState.REPACK
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        def replace_tensor(name, new_t):
+            # It is important to use resize_() here since it ensures
+            # the same buffer is reused
+            getattr(layer, name).resize_(new_t.shape)
+            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
+
+        # when running models with grouped act order,
+        # resort to g_idx values provided in checkpoint
+        if self.actorder == "group":
+            w13_g_idx_sort_indices = torch.empty_like(layer.w13_weight_g_idx)
+            w2_g_idx_sort_indices = torch.empty_like(layer.w2_weight_g_idx)
+            w13_sorted_g_idx = torch.empty_like(layer.w13_weight_g_idx)
+            w2_sorted_g_idx = torch.empty_like(layer.w2_weight_g_idx)
+
+            for e in range(num_experts):
+                w13_g_idx_sort_indices[e] = torch.argsort(layer.w13_weight_g_idx[e]).to(
+                    torch.int32
+                )
+                w2_g_idx_sort_indices[e] = torch.argsort(layer.w2_weight_g_idx[e]).to(
+                    torch.int32
+                )
+                w13_sorted_g_idx[e] = layer.w13_weight_g_idx[e][
+                    w13_g_idx_sort_indices[e]
+                ]
+                w2_sorted_g_idx[e] = layer.w2_weight_g_idx[e][w2_g_idx_sort_indices[e]]
+
+            replace_parameter(layer, "w13_weight_g_idx", w13_sorted_g_idx)
+            replace_parameter(layer, "w2_weight_g_idx", w2_sorted_g_idx)
+            replace_parameter(layer, "w13_g_idx_sort_indices", w13_g_idx_sort_indices)
+            replace_parameter(layer, "w2_g_idx_sort_indices", w2_g_idx_sort_indices)
+
+        else:
+            layer.w13_weight_g_idx = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+                requires_grad=False,
+            )
+            layer.w2_weight_g_idx = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+                requires_grad=False,
+            )
+            layer.w13_g_idx_sort_indices = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+                requires_grad=False,
+            )
+            layer.w2_g_idx_sort_indices = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+                requires_grad=False,
+            )
+
+        marlin_w13_qweight = 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)
+        marlin_w2_qweight = ops.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)
+        # Repack scales
+        marlin_w13_scales = marlin_moe_permute_scales(
+            layer.w13_weight_scale,
+            size_k13,
+            layer.w13_weight_scale.shape[2],
+            self.group_size,
+            self.num_bits,
+        )
+        replace_tensor("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,
+            self.group_size,
+            self.num_bits,
+        )
+        replace_tensor("w2_weight_scale", marlin_w2_scales)
+
+    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",
+        correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
+    ) -> torch.Tensor:
+        assert activation == "silu", "Only SiLU activation is supported."
+        if not VLLM_AVAILABLE:
+            raise ImportError(
+                "vllm is not installed, to use fused_marlin_moe, please install vllm"
+            )
+        if expert_map is not None:
+            raise NotImplementedError(
+                "Expert Parallelism is not supported for " "fused Marlin MoE method."
+            )
+
+        topk_weights, topk_ids = 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,
+            correction_bias=correction_bias,
+        )
+
+        return torch.ops.vllm.fused_marlin_moe(
+            x,
+            layer.w13_weight_packed,
+            layer.w2_weight_packed,
+            layer.w13_weight_scale,
+            layer.w2_weight_scale,
+            router_logits,
+            topk_weights,
+            topk_ids,
+            g_idx1=layer.w13_weight_g_idx,
+            g_idx2=layer.w2_weight_g_idx,
+            sort_indices1=layer.w13_g_idx_sort_indices,
+            sort_indices2=layer.w2_g_idx_sort_indices,
+            num_bits=self.num_bits,
+            is_k_full=self.is_k_full,
+        )

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

+# SPDX-License-Identifier: Apache-2.0
+
+from .compressed_tensors_scheme import CompressedTensorsScheme
+from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8
+
+__all__ = [
+    "CompressedTensorsScheme",
+    "CompressedTensorsW8A8Fp8",
+]

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

+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
+# SPDX-License-Identifier: Apache-2.0
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import torch
+
+__all__ = ["CompressedTensorsScheme"]
+
+
+class CompressedTensorsScheme(ABC):
+    """
+    Abstract class used to describe the weight creation and forward pass
+    of different quantization schemes supported by CompressedTensors.
+    """
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        """
+        Get minimum device capability.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def create_weights(self, *args, **kwargs):
+        """
+        Weight creation for the particular scheme. Inputs to this function
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_weights(
+        self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor]
+    ):
+        """
+        Run the forward pass for the particular scheme. This is where
+        scheme-specific dequant/quant steps/kernels should be applied.
+
+        :param layer: torch.nn.Module with the registered weights and
+            other parameters relevant to the particular scheme.
+        :param x: input to the layer
+        :param bias: bias parameter
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        """
+        Called after weight loading is complete for any cleanup that
+        needs to occur.
+        """
+        raise NotImplementedError

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Callable, List, Optional
+
+import torch
+from compressed_tensors.quantization import QuantizationStrategy
+from torch.nn import Parameter
+
+from sglang.srt.layers.parameter import (
+    ChannelQuantScaleParameter,
+    ModelWeightParameter,
+    PerTensorScaleParameter,
+)
+from sglang.srt.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme,
+)
+from sglang.srt.layers.quantization.fp8_utils import (
+    Fp8LinearOp,
+    maybe_create_device_identity,
+    normalize_e4m3fn_to_e4m3fnuz,
+)
+from sglang.srt.layers.quantization.utils import is_fp8_fnuz, requantize_with_max_scale
+
+__all__ = ["CompressedTensorsW8A8Fp8"]
+
+
+class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+        self.fp8_linear = Fp8LinearOp(use_per_token_if_dynamic=True)
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # lovelace and up
+        return 89
+
+    def process_weights_after_loading(self, layer) -> None:
+        # If per tensor, when we have a fused module (e.g. QKV) with per
+        # tensor scales (thus N scales being passed to the kernel),
+        # requantize so we can always run per tensor
+        if self.strategy == QuantizationStrategy.TENSOR:
+            max_w_scale, weight = requantize_with_max_scale(
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                logical_widths=layer.logical_widths,
+            )
+
+            if is_fp8_fnuz():
+                input_scale = getattr(layer, "input_scale", None)
+
+                weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    weight=weight, weight_scale=max_w_scale, input_scale=input_scale
+                )
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale, requires_grad=False)
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+
+        # If channelwise, scales are already lined up, so just transpose.
+        elif self.strategy == QuantizationStrategy.CHANNEL:
+            weight = layer.weight
+
+            if is_fp8_fnuz():
+                input_scale = getattr(layer, "input_scale", None)
+
+                weight, weight_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    weight=weight,
+                    weight_scale=layer.weight_scale,
+                    input_scale=input_scale,
+                )
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale, requires_grad=False)
+            else:
+                weight_scale = layer.weight_scale.data
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+
+        else:
+            raise ValueError(f"Unknown quantization strategy {self.strategy}")
+
+        # INPUT SCALE
+        if self.is_static_input_scheme and hasattr(layer, "input_scale"):
+            layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
+        else:
+            layer.input_scale = None
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        output_partition_sizes: List[int],
+        input_size_per_partition: int,
+        params_dtype: torch.dtype,
+        weight_loader: Callable,
+        **kwargs,
+    ):
+        maybe_create_device_identity()
+
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition,
+                dtype=torch.float8_e4m3fn,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        # TODO: update create_xxx_parameter functions to return
+        # the newly added parameters
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader,
+            )
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            weight_scale = PerTensorScaleParameter(
+                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+                weight_loader=weight_loader,
+            )
+
+        # min requirement for fp8 kernels
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(
+                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+                weight_loader=weight_loader,
+            )
+            input_scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("input_scale", input_scale)
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        return self.fp8_linear.apply(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=layer.input_scale,
+            bias=bias,
+        )

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

+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
+# SPDX-License-Identifier: Apache-2.0
+
+import re
+from types import MappingProxyType
+from typing import Iterable, List, Mapping, Optional
+
+from compressed_tensors import CompressionFormat
+from torch.nn import Module
+
+
+def is_activation_quantization_format(format: str) -> bool:
+    _ACTIVATION_QUANTIZATION_FORMATS = [
+        CompressionFormat.naive_quantized.value,
+        CompressionFormat.int_quantized.value,
+        CompressionFormat.float_quantized.value,
+    ]
+    return format in _ACTIVATION_QUANTIZATION_FORMATS
+
+
+def should_ignore_layer(
+    layer_name: Optional[str],
+    ignore: Iterable[str] = tuple(),
+    fused_mapping: Mapping[str, List[str]] = MappingProxyType({}),
+) -> bool:
+    if layer_name is None:
+        return False
+
+    # layer_name = model.layers.0.self_attn.qkv_proj
+    # proj_name = qkv_proj
+    proj_name = layer_name.split(".")[-1]
+
+    # Fused layers like gate_up_proj or qkv_proj will not be fused
+    # in the safetensors checkpoint. So, we convert the name
+    # from the fused version to unfused + check to make sure that
+    # each shard of the fused layer has the same scheme.
+    if proj_name in fused_mapping and layer_name not in ignore:
+        shard_proj_names = fused_mapping[proj_name]
+
+        # Convert fused_name --> [shard_names]
+        shard_names = [
+            layer_name.replace(proj_name, shard_proj_name)
+            for shard_proj_name in shard_proj_names
+        ]
+
+        # Layer should be ignored if shards are ignored.
+        should_ignore_layer = None
+        for shard_name in shard_names:
+            should_ignore_shard = check_equal_or_regex_match(
+                layer_name=shard_name, targets=ignore
+            )
+
+            # If shard_idx=0, set layer ignore to match shard.
+            if should_ignore_layer is None:
+                should_ignore_layer = should_ignore_shard
+
+            # If shard_idx=1+ confirm scheme matches prior shards.
+            elif should_ignore_shard != should_ignore_layer:
+                raise ValueError(
+                    f"Found a different quantization schemes for "
+                    f"{shard_proj_names} in {layer_name}. vLLM "
+                    "requires all to use the same scheme."
+                )
+
+    # Unfused layers like down_proj and o_proj will match
+    # the safetensors checkpoint already.
+    else:
+        should_ignore_layer = check_equal_or_regex_match(
+            layer_name=layer_name, targets=ignore
+        )
+
+    assert should_ignore_layer is not None
+    return should_ignore_layer
+
+
+def check_equal_or_regex_match(layer_name: str, targets: Iterable[str]) -> bool:
+    """
+    Checks whether a layer_name is exactly equal or a regex match for
+    if target starts with 're:' to any target in list.
+    """
+    for target in targets:
+        if _is_equal_or_regex_match(layer_name, target):
+            return True
+    return False
+
+
+def find_matched_target(
+    layer_name: Optional[str],
+    module: Module,
+    targets: Iterable[str],
+    fused_mapping: Mapping[str, List[str]] = MappingProxyType({}),
+) -> str:
+    """
+    Helper function to look up which "target" in the compressed-tensors
+    config that a layer corresponds to.
+
+    Recall that a compressed-tensors configs has a concept of
+    config_groups, where each layer can be quantized with with a different
+    scheme.
+
+    targets in each config_group will be a list of either layer names
+    (or regexes corresponding to layer names) or names of torch Modules.
+
+    First, we try to match the layer_name with a target
+    Second, we try to match the module's name with a target
+    Third, we try to map the layer_name to a list of fused module names.
+        *All* component module names must match in order for a match to be
+        successful. A successful match returns the first component target
+
+    :param layer_name: layer name
+    :param module: torch.nn.Module
+    :param targets: list of targets to match the layer against
+    :param fused_mapping: map from fused layer names to its components
+    :param fused_strategy: either "all" or "any". If using "all", fused
+        layers match if "all" of its components match
+    """
+
+    if layer_name is None:
+        layer_name = ""
+
+    matched_target = (
+        _find_first_match(layer_name, targets)
+        or _find_first_match(module.__class__.__name__, targets, True)
+        or _match_fused_layer(layer_name, targets, fused_mapping)
+    )
+
+    if matched_target is None:
+        raise ValueError(
+            f"Unable to find matching target for {layer_name} in the "
+            "compressed-tensors config."
+        )
+
+    return matched_target
+
+
+def _find_first_match(
+    value: str, targets: Iterable[str], check_contains: bool = False
+) -> Optional[str]:
+    """
+    Returns first element of target that matches value either
+    exactly or as a regex after 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+
+    :param value: string to compare the list of targets against
+    :param targets: list of targets to match the layer against
+    :param check_contains: whether or not to do a substring match
+    """
+
+    for target in targets:
+        if _is_equal_or_regex_match(value, target, check_contains=check_contains):
+            return target
+    return None
+
+
+def _is_equal_or_regex_match(
+    value: str, target: str, check_contains: bool = False
+) -> bool:
+    """
+    Checks whether a value is exactly equal or a regex match for target
+    if target starts with 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+    """
+
+    if target.startswith("re:"):
+        pattern = target[3:]
+        if re.match(pattern, value):
+            return True
+    elif check_contains:
+        if target.lower() in value.lower():
+            return True
+    elif target == value:
+        return True
+    return False
+
+
+def _match_fused_layer(
+    layer_name: str,
+    target_layers: Iterable[str],
+    fused_mapping: Mapping[str, List[str]],
+) -> Optional[str]:
+    """
+    Match a fused layer name to its corresponding individual layer in
+    target_layers. Returns first value in fused_mapping which matches targets
+
+    Implements an "all" matching strategy where a fused layer matches iff
+    "all" of its components match
+
+    :param layer_name: layer name
+    :param target_layers: list of targets to match the layer against
+    :param fused_mapping: map from fused layer names to its components
+
+    Examples:
+        layer_name = "model.layers.0.self_attn.qkv_proj"
+        target_layers = ["model.layers.0.self_attn.q_proj",
+                        "model.layers.0.self_attn.k_proj",
+                        "model.layers.0.self_attn.v_proj"]
+    """
+    # find layer_name in mapping
+    fused = next((key for key in fused_mapping if layer_name.endswith(key)), None)
+    if fused is None:
+        return None
+
+    # expand path of unfused components
+    unfused_paths = [
+        layer_name.replace(fused, unfused) for unfused in fused_mapping[fused]
+    ]
+
+    # for each unfused component, find a match in targets
+    unfused_matches: List[Optional[str]] = []
+    for unfused in unfused_paths:
+        for target in target_layers:
+            if _is_equal_or_regex_match(unfused, target):
+                unfused_matches.append(target)
+                break
+        else:
+            unfused_matches.append(None)
+
+    return unfused_matches[0] if all(unfused_matches) else None

python/sglang/srt/layers/quantization/fp8_utils.py

+import os
 from typing import List, Optional, Tuple
 
 import torch
@@ -18,6 +19,7 @@ from sglang.srt.utils import (
 
 try:
     import vllm
+    from vllm import _custom_ops as ops
 
     VLLM_AVAILABLE = True
 except ImportError:
@@ -31,19 +33,29 @@ if _is_hip and get_bool_env_var("CK_MOE"):
 
 _is_cuda = is_cuda()
 if _is_cuda:
-    from sgl_kernel import fp8_blockwise_scaled_mm
+    from sgl_kernel import fp8_blockwise_scaled_mm, fp8_scaled_mm
 
+    from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
     from sglang.srt.layers.quantization.fp8_kernel import sglang_per_token_quant_fp8
 
-    if use_vllm_cutlass_w8a8_fp8_kernel and VLLM_AVAILABLE:
-        from vllm import _custom_ops as ops
-    else:
-        from sgl_kernel import fp8_scaled_mm
-
 # Input scaling factors are no longer optional in _scaled_mm starting
 # from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
 TORCH_DEVICE_IDENTITY = torch.ones(1, dtype=torch.float32)
 
+_TORCH_VERSION = torch.__version__.split("+")[0]
+try:
+    _TORCH_VERSION_TUPLE = tuple(map(int, _TORCH_VERSION.split(".")[:3]))
+except ValueError:
+    _TORCH_VERSION_TUPLE = (0, 0, 0)
+
+# The condition to determine if it is on a platform that supports
+# torch._scaled_mm rowwise feature.
+# The condition is determined once as the operations
+# are time consuming.
+USE_ROWWISE_TORCH_SCALED_MM = (
+    _is_hip and get_device_capability() >= (9, 4) and _TORCH_VERSION_TUPLE >= (2, 7, 0)
+)
+
 
 def cutlass_fp8_supported():
     if not _is_cuda:
@@ -330,3 +342,223 @@ def apply_fp8_linear(
             if bias is not None:
                 output = output + bias
             return output.to(dtype=input.dtype).view(*output_shape)
+
+
+def maybe_create_device_identity():
+    # Allocate dummy ones tensor for torch._scaled_mm
+    global TORCH_DEVICE_IDENTITY
+    if TORCH_DEVICE_IDENTITY is None:
+        TORCH_DEVICE_IDENTITY = torch.ones(1, dtype=torch.float32)
+
+
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/w8a8_utils.py
+# TODO(luka): follow similar pattern for marlin and block-fp8-linear
+#  https://github.com/vllm-project/vllm/issues/14397
+class Fp8LinearOp:
+    """
+    This class executes a FP8 linear layer using cutlass if supported and
+    torch.scaled_mm otherwise.
+    It needs to be a class instead of a method so that config can be read
+    in the __init__ method, as reading config is not allowed inside forward.
+    """
+
+    def __init__(
+        self,
+        cutlass_fp8_supported: bool = cutlass_fp8_supported(),
+        use_per_token_if_dynamic: bool = False,
+        pad_output: Optional[bool] = None,
+    ):
+        self.cutlass_fp8_supported = cutlass_fp8_supported
+        self.use_per_token_if_dynamic = use_per_token_if_dynamic
+
+        # Note: we pad the input because torch._scaled_mm is more performant
+        # for matrices with batch dimension > 16.
+        # This could change in the future.
+        # We also don't pad when using torch.compile,
+        # as it breaks with dynamic shapes.
+        if pad_output is None:
+            enable_torch_compile = os.environ.get(
+                "SGLANG_ENABLE_TORCH_COMPILE", "0"
+            ).lower() in ("1", "true", "yes")
+            pad_output = not enable_torch_compile
+        self.output_padding = 17 if pad_output else None
+
+    def apply(
+        self,
+        input: torch.Tensor,
+        weight: torch.Tensor,
+        weight_scale: torch.Tensor,
+        input_scale: Optional[torch.Tensor] = None,
+        input_scale_ub: Optional[torch.Tensor] = None,
+        bias: Optional[torch.Tensor] = None,
+        # TODO(luka) remove this parameter in favor of __init__
+        use_per_token_if_dynamic: Optional[bool] = None,
+    ) -> torch.Tensor:
+        # ops.scaled_fp8_quant supports both dynamic and static quant.
+        #   If dynamic, layer.input_scale is None and x_scale computed from x.
+        #   If static, layer.input_scale is scalar and x_scale is input_scale.
+
+        # View input as 2D matrix for fp8 methods
+        input_2d = input.view(-1, input.shape[-1])
+        output_shape = [*input.shape[:-1], weight.shape[1]]
+
+        # TODO(luka) this is here because currently MLA only decides this
+        #  during the forward method instead of in __init__.
+        if use_per_token_if_dynamic is None:
+            use_per_token_if_dynamic = self.use_per_token_if_dynamic
+
+        # cutlass_scaled_mm supports per tensor/channel W and per tensor/token A
+        # for sgl-kernel fp8_scaled_mm, it support per channel W now
+        if self.cutlass_fp8_supported and weight_scale.numel() == weight.shape[1]:
+            if _is_cuda:
+                qinput, x_scale = sgl_scaled_fp8_quant(
+                    input_2d,
+                    input_scale,
+                    use_per_token_if_dynamic=use_per_token_if_dynamic,
+                )
+            else:
+                qinput, x_scale = ops.scaled_fp8_quant(
+                    input_2d,
+                    input_scale,
+                    scale_ub=input_scale_ub,
+                    use_per_token_if_dynamic=use_per_token_if_dynamic,
+                )
+
+            # Fused GEMM_DQ
+            if VLLM_AVAILABLE and use_vllm_cutlass_w8a8_fp8_kernel:
+                # Fall back to vllm cutlass w8a8 fp8 kernel
+                output = ops.cutlass_scaled_mm(
+                    qinput,
+                    weight,
+                    out_dtype=input.dtype,
+                    scale_a=x_scale,
+                    scale_b=weight_scale,
+                    bias=bias,
+                )
+            else:
+                assert (
+                    weight_scale.numel() == weight.shape[1]
+                ), "cutlass w8a8 fp8 sgl-kernel only supports per-channel scale"
+                output = fp8_scaled_mm(
+                    qinput,
+                    weight,
+                    x_scale,
+                    weight_scale,
+                    out_dtype=input.dtype,
+                    bias=bias,
+                )
+            return output.view(*output_shape)
+
+        # torch.scaled_mm supports per tensor weights + activations only
+        # so fallback to naive if per channel or per token
+        else:
+            # Maybe apply padding to output, see comment in __init__
+            if _is_cuda:
+                qinput, x_scale = sgl_scaled_fp8_quant(
+                    input_2d,
+                    input_scale,
+                    use_per_token_if_dynamic=use_per_token_if_dynamic,
+                )
+                if self.output_padding:
+                    pad_size = max(self.output_padding - qinput.shape[0], 0)
+                    if pad_size > 0:
+                        qinput = torch.nn.functional.pad(qinput, (0, 0, 0, pad_size))
+            else:
+                qinput, x_scale = ops.scaled_fp8_quant(
+                    input_2d,
+                    input_scale,
+                    num_token_padding=self.output_padding,
+                    use_per_token_if_dynamic=use_per_token_if_dynamic,
+                )
+
+            per_tensor_weights = weight_scale.numel() == 1
+            per_tensor_activations = x_scale.numel() == 1
+
+            if per_tensor_weights and per_tensor_activations:
+                # Fused GEMM_DQ
+                output = torch._scaled_mm(
+                    qinput,
+                    weight,
+                    out_dtype=input.dtype,
+                    scale_a=x_scale,
+                    scale_b=weight_scale,
+                    bias=bias,
+                )
+                # A fix for discrepancy in scaled_mm which returns tuple
+                # for torch < 2.5 and a single value in torch >= 2.5
+                if type(output) is tuple and len(output) == 2:
+                    output = output[0]
+
+                return torch.narrow(output, 0, 0, input_2d.shape[0]).view(*output_shape)
+
+            elif (
+                use_per_token_if_dynamic
+                and not per_tensor_weights
+                and not per_tensor_activations
+                and USE_ROWWISE_TORCH_SCALED_MM
+            ):
+                # For now validated on ROCm platform
+                # fp8 rowwise scaling in torch._scaled_mm is introduced in
+                # https://github.com/pytorch/pytorch/pull/144432 using hipBLASLt
+                # and ROCm 6.3, which only exists in torch 2.7 and above.
+                # For CUDA platform please validate if the
+                # torch._scaled_mm support rowwise scaled GEMM
+                # Fused GEMM_DQ Rowwise GEMM
+                output = torch._scaled_mm(
+                    qinput,
+                    weight,
+                    out_dtype=input.dtype,
+                    scale_a=x_scale,
+                    scale_b=weight_scale.t(),
+                    bias=bias,
+                )
+
+                output = torch.narrow(output, 0, 0, input_2d.shape[0])
+                output = output.view(*output_shape)
+                return output
+
+            else:
+                # Fallback for channelwise case, where we use unfused DQ
+                # due to limitations with scaled_mm
+
+                # Symmetric quantized GEMM by definition computes the following:
+                #   C = (s_x * X) (s_w * W) + bias
+                # This is equivalent to dequantizing the weights and activations
+                # before applying a GEMM.
+                #
+                # In order to compute quantized operands, a quantized kernel
+                # will rewrite the above like so:
+                #   C = s_w * s_x * (X * W) + bias
+                #
+                # For the scaled_mm fallback case, we break this down, since it
+                # does not support s_w being a vector.
+
+                # GEMM
+                # This computes C = (X * W).
+                # Output in fp32 to allow subsequent ops to happen in-place
+
+                global TORCH_DEVICE_IDENTITY
+                if TORCH_DEVICE_IDENTITY.device != weight.device:
+                    TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
+
+                output = torch._scaled_mm(
+                    qinput,
+                    weight,
+                    scale_a=TORCH_DEVICE_IDENTITY,
+                    scale_b=TORCH_DEVICE_IDENTITY,
+                    out_dtype=torch.float32,
+                )
+                # A fix for discrepancy in scaled_mm which returns tuple
+                # for torch < 2.5 and a single value in torch >= 2.5
+                if type(output) is tuple and len(output) == 2:
+                    output = output[0]
+                # Unpad (undo num_token_padding)
+                output = torch.narrow(output, 0, 0, input_2d.shape[0])
+                x_scale = torch.narrow(x_scale, 0, 0, input_2d.shape[0])
+
+                # DQ
+                # C = sw * sx * (X * W) + bias
+                output = output * x_scale * weight_scale.t()
+                if bias is not None:
+                    output = output + bias
+                return output.to(dtype=input.dtype).view(*output_shape)

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

@@ -15,6 +15,11 @@ else:
     from vllm import _custom_ops as vllm_ops
 
 
+def is_fp8_fnuz() -> bool:
+    # only device 0 is checked, this assumes MI300 platforms are homogeneous
+    return "gfx94" in torch.cuda.get_device_properties(0).gcnArchName
+
+
 def is_layer_skipped(
     prefix: str,
     ignored_layers: List[str],
@@ -120,3 +125,29 @@ def requantize_with_max_scale(
             start = end
 
     return max_w_scale, weight
+
+
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/layer_utils.py
+# Newly generated tensors need to replace existing tensors that are
+# already registered as parameters by vLLM (and won't be freed)
+def replace_parameter(
+    mod: torch.nn.Module, name: str, new: Union[torch.Tensor, torch.nn.Parameter]
+) -> None:
+
+    old = getattr(mod, name)
+    if (
+        type(old) is type(new)
+        and old.dtype == new.dtype
+        and old.untyped_storage().nbytes() == new.untyped_storage().nbytes()
+    ):
+        # If we can just update in-place to avoid re-registering
+        #   can be faster if the underlying storage is the same
+        update_tensor_inplace(old, new)
+    else:
+        # Fallback re-register parameter, convert to Parameter if necessary
+        # this not only ensures we don't register a tensor as a parameter, but
+        # also ensures that all parameter subclasses get re-registered as
+        # parameters for `torch.compile` compatibility
+        if not isinstance(new, torch.nn.Parameter):
+            new = torch.nn.Parameter(new, requires_grad=False)
+        mod.register_parameter(name, torch.nn.Parameter(new, requires_grad=False))