feat: w4a8 marlin合入主分支, 通过-q slimquant_w4a8_marlin 可以启用

vllm/config.py

@@ -893,7 +893,8 @@ class ModelConfig:
         optimized_quantization_methods = [
             "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
             "awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
-            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas"
+            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas",
+            "slimquant_w4a8","slimquant_w4a8_marlin"
         ]
         if self.quantization is not None:
             self.quantization = cast(me_quant.QuantizationMethods,
@@ -920,6 +921,7 @@ class ModelConfig:
                 "awq_marlin",
                 "ipex",
                 "moe_wna16",
+                "slimquant_w4a8_marlin"
             ]
             quantization_methods = [
                 q for q in supported_quantization if q not in overrides

vllm/model_executor/layers/fused_moe/layer.py

@@ -819,9 +819,9 @@ class FusedMoE(torch.nn.Module):
                     "CompressedTensorsWNA16MoEMethod")):
             moe_quant_params["intermediate_size_full"] = intermediate_size
 
-        if (self.quant_method.__class__.__name__ in ("BlockInt8MoEMethod")):
-            moe_quant_params["intermediate_size"] = self.intermediate_size_per_partition
-        if (self.quant_method.__class__.__name__ in ("SlimQuantW4A8Int8MoEMethod")):
+        if (self.quant_method.__class__.__name__ in ("BlockInt8MoEMethod",
+                                                     "SlimQuantW4A8Int8MoEMethod",
+                                                     "SlimQuantW4A8Int8MarlinMoEMethod")):
             moe_quant_params["intermediate_size"] = self.intermediate_size_per_partition
 
 

vllm/model_executor/layers/quantization/__init__.py

@@ -37,7 +37,8 @@ QuantizationMethods = Literal[
     "auto-round",
     "rtn",
     "blockwise_int8",
-    "slimquant_w4a8"
+    "slimquant_w4a8",
+    "slimquant_w4a8_marlin"
 ]
 QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
 
@@ -118,6 +119,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     from .tpu_int8 import Int8TpuConfig
     from .blockwise_int8 import BlockInt8Config
     from .slimquant_w4a8 import SlimQuantW4A8Int8Config
+    from .slimquant_w4a8_marlin import SlimQuantW4A8Int8MarlinConfig
 
     method_to_config: dict[str, type[QuantizationConfig]] = {
         "aqlm": AQLMConfig,
@@ -151,6 +153,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "rtn": RTNConfig,
         "blockwise_int8": BlockInt8Config,
         "slimquant_w4a8":SlimQuantW4A8Int8Config,
+        "slimquant_w4a8_marlin":SlimQuantW4A8Int8MarlinConfig,
     }
     # Update the `method_to_config` with customized quantization methods.
     method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)

vllm/model_executor/layers/quantization/slimquant_w4a8.py

@@ -7,7 +7,6 @@ from torch.nn.parameter import Parameter
 from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
-from vllm.model_executor.layers.quantization.utils.w4a8_utils import w4a8_2_marlin_weight
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
 from vllm.model_executor.parameter import (BasevLLMParameter,
@@ -19,29 +18,10 @@ from lmslim.layers.gemm.int8_utils import (
     per_token_quant_int8)
 from vllm import _custom_ops as ops
 from vllm.utils import W8a8GetCacheJSON
-import vllm.envs as envs
+
 import os
 from vllm import _custom_ops as ops
 
-try:
-    from lmslim.layers.fused_moe.fuse_moe_w4a8_marlin import fused_experts_impl_w4a8_marlin
-except Exception:
-    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
-
-workspace = None
-global_reduce_buffer = None
-
-def get_marlin_moe_workspace(device):
-    global workspace
-    global global_reduce_buffer
-    if workspace is None:
-        sms = torch.cuda.get_device_properties(device).multi_processor_count
-        workspace = torch.zeros(500, dtype=torch.int, device=device, requires_grad=False)
-    if global_reduce_buffer is None:
-        sms = torch.cuda.get_device_properties(device).multi_processor_count
-        global_reduce_buffer = torch.zeros(sms * 6 * 128 * 512, dtype=torch.int, device=device, requires_grad=False)
-    return workspace, global_reduce_buffer
-
 W8A8_TRITONJSON=W8a8GetCacheJSON()
 
 def baseline_scaled_mm(a: torch.Tensor,
@@ -339,27 +319,14 @@ class SlimQuantW4A8Int8MoEMethod:
         if configs_dict:
             self.tritonsingleton.triton_moejson_dict.update(configs_dict)
 
-        # layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
-        # layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
+        layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
+        layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
         layer.w13_weight_scale = Parameter(
             layer.w13_weight_scale.data, requires_grad=False
         )
         layer.w2_weight_scale = Parameter(
             layer.w2_weight_scale.data, requires_grad=False
         )
-        w1_marlin_list = []
-        for e in range(layer.w13_weight.shape[0]):
-            w1_marlin_in = w4a8_2_marlin_weight(layer.w13_weight[e])
-            w1_marlin_list.append(w1_marlin_in)
-        layer.w13_weight = Parameter(torch.stack(w1_marlin_list, dim=0), requires_grad=False)
-        w2_marlin_list = []
-        for e in range(layer.w2_weight.shape[0]):
-            w2_marlin_in = w4a8_2_marlin_weight(layer.w2_weight[e])
-            w2_marlin_list.append(w2_marlin_in)
-        layer.w2_weight = Parameter(torch.stack(w2_marlin_list, dim=0), requires_grad=False)
-
-
-
 
     def apply(
         self,
@@ -403,15 +370,13 @@ class SlimQuantW4A8Int8MoEMethod:
             routed_scaling_factor=routed_scaling_factor,
             use_fused_gate=use_fused_gate
         )
-        workspace, global_reduce_buffer=get_marlin_moe_workspace(device=x.device)
-        return fused_experts_impl_w4a8_marlin(
+
+        return fused_experts(
             x,
             layer.w13_weight,
             layer.w2_weight,
             topk_weights=topk_weights,
             topk_ids=topk_ids,
-            workspace=workspace,
-            global_reduce_buffer=global_reduce_buffer,
             inplace=True,
             use_int4_w4a8=True,
             per_channel_quant=True,

vllm/model_executor/layers/quantization/slimquant_w4a8_marlin.py

+from typing import Any, Callable, Dict, List, Optional
+import os
+import torch
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.distributed import get_tensor_model_parallel_world_size
+from torch.nn.parameter import Parameter
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
+from vllm.model_executor.layers.quantization.base_config import (QuantizationConfig,
+                                                                 QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.utils.w4a8_utils import w4a8_2_marlin_weight
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter)
+from vllm.model_executor.layers.quantization.slimquant_w4a8 import SlimQuantW4A8Int8LinearMethod
+
+try:
+    from lmslim.layers.fused_moe.fuse_moe_w4a8_marlin import fused_experts_impl_w4a8_marlin
+except Exception:
+    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
+
+class MarlinMoeWorkspace:
+    """
+    Singleton manager for device-specific workspace buffers used by w4a8 Marlin-MoE.
+    global_reduce_buffer will take 1.5MB * cus (about 120MB for BW200) memoery in each device
+    """
+    _instances = {}
+    def __new__(cls, device):
+        if device not in cls._instances:
+            instance = super().__new__(cls)
+            instance._initialized = False
+            cls._instances[device] = instance
+        return cls._instances[device]
+
+    def __init__(self, device):
+        if self._initialized:
+            return
+        sms = torch.cuda.get_device_properties(device).multi_processor_count
+        self.workspace = torch.zeros(
+            500, dtype=torch.int, device=device, requires_grad=False
+        )
+        self.global_reduce_buffer = torch.zeros(
+            sms * 6 * 128 * 512, dtype=torch.int, device=device, requires_grad=False
+        )
+        self._initialized = True
+
+    def get_buffers(self):
+        return self.workspace, self.global_reduce_buffer
+
+def baseline_scaled_mm(a: torch.Tensor,
+                      b: torch.Tensor,
+                      scale_a: torch.Tensor,
+                      scale_b: torch.Tensor,
+                      out_dtype: torch.dtype,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+    scales= scale_a* scale_b.T
+    gemmout= torch.mm(
+        a.to(dtype=torch.float32), b.to(dtype=torch.float32))
+    output = (scales *gemmout).to(out_dtype)
+    if bias is not None:
+        output = output + bias
+    return output.to(out_dtype)
+
+
+class SlimQuantW4A8Int8MarlinConfig(QuantizationConfig):
+    """Config class for W4A8 Int8 Quantization.
+    - Weight: static, per-channel, symmetric
+    - Activation: dynamic, per-token, symmetric
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 75
+
+    @classmethod
+    def get_name(self) -> str:
+        return "slimquant_w4a8_marlin"
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "SlimQuantW4A8Int8MarlinConfig":
+        return cls()
+    @classmethod
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
+        if hf_quant_cfg.get("quant_method") == "slimquant_w4a8" \
+                and user_quant == "slimquant_w4a8_marlin":
+            return cls.get_name()
+        return None
+    def get_quant_method(
+        self,
+        layer: torch.nn.Module,
+        prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+
+        if isinstance(layer, LinearBase):
+            return SlimQuantW4A8Int8LinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return SlimQuantW4A8Int8MarlinMoEMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class SlimQuantW4A8Int8MarlinMoEMethod:
+    """MoE method for W4A8INT8 Marlin.
+    Supports loading INT8 checkpoints with static weight scale and
+    dynamic/static activation scale.
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __new__(cls, *args, **kwargs):
+
+        if not hasattr(cls, "_initialized"):
+            original_init = cls.__init__
+            new_cls = type(
+                cls.__name__,
+                (FusedMoEMethodBase,),
+                {
+                    "__init__": original_init,
+                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
+                },
+            )
+            obj = super(new_cls, new_cls).__new__(new_cls)
+            obj.__init__(*args, **kwargs)
+            return obj
+        return super().__new__(cls)
+
+    def __init__(self, quant_config):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        tp_size = get_tensor_model_parallel_world_size()
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts, 2 * intermediate_size, hidden_size//2, dtype=torch.int8
+            ),
+            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//2, dtype=torch.int8),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
+            requires_grad=False,
+        )
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+        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)
+
+        w13_input_scale = None
+        layer.register_parameter("w13_input_scale", w13_input_scale)
+
+        w2_input_scale = None
+        layer.register_parameter("w2_input_scale", w2_input_scale)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.w13_weight_scale = Parameter(
+            layer.w13_weight_scale.data, requires_grad=False
+        )
+        layer.w2_weight_scale = Parameter(
+            layer.w2_weight_scale.data, requires_grad=False
+        )
+
+        w1_marlin_list = []
+        for e in range(layer.w13_weight.shape[0]):
+            w1_marlin_in = w4a8_2_marlin_weight(layer.w13_weight[e])
+            w1_marlin_list.append(w1_marlin_in)
+        layer.w13_weight = Parameter(torch.stack(w1_marlin_list, dim=0), requires_grad=False)
+        w2_marlin_list = []
+        for e in range(layer.w2_weight.shape[0]):
+            w2_marlin_in = w4a8_2_marlin_weight(layer.w2_weight[e])
+            w2_marlin_list.append(w2_marlin_in)
+        layer.w2_weight = Parameter(torch.stack(w2_marlin_list, dim=0), 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",
+        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,
+        **_  
+    ) -> torch.Tensor:
+        from vllm.model_executor.layers.fused_moe import fused_experts
+        if enable_eplb:
+            raise NotImplementedError(
+                "EPLB not supported for `SlimQuantW4A8Int8MarlinMoEMethod` yet.")
+        # Expert selection
+        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,
+            e_score_correction_bias=e_score_correction_bias,
+            routed_scaling_factor=routed_scaling_factor,
+            use_fused_gate=use_fused_gate
+        )
+        workspace, global_reduce_buffer = MarlinMoeWorkspace(x.device).get_buffers()
+        return fused_experts_impl_w4a8_marlin(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            workspace=workspace,
+            global_reduce_buffer=global_reduce_buffer,
+            inplace=True,
+            use_int4_w4a8=True,
+            per_channel_quant=True,
+            activation=activation,
+            expert_map=expert_map,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            global_num_experts=global_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=use_nn_moe,
+        )

vllm/model_executor/layers/quantization/utils/w4a8_utils.py

@@ -18,8 +18,8 @@ def unpack_int8_to_int4(tensor_int8: torch.Tensor) -> torch.Tensor:
 
     N, K_half = tensor_int8.shape
     tensor_uint8 = tensor_int8.to(torch.uint8)
-    low4 = tensor_uint8 & 0x0F
-    high4 = (tensor_uint8 >> 4) & 0x0F
+    high4 = tensor_uint8 & 0x0F
+    low4 = (tensor_uint8 >> 4) & 0x0F
     unpacked = torch.empty((N, K_half * 2), dtype=torch.int32, device=tensor_int8.device)
     unpacked[:, 0::2] = low4.to(torch.int32)
     unpacked[:, 1::2] = high4.to(torch.int32)

vllm/platforms/rocm.py

@@ -180,7 +180,7 @@ class RocmPlatform(Platform):
 
     supported_quantization: list[str] = [
         "awq", "gptq", "fp8", "compressed-tensors", "fbgemm_fp8", "gguf",
-        "quark", "ptpc_fp8", "moe_wna16", "blockwise_int8","slimquant_w4a8","awq_marlin"
+        "quark", "ptpc_fp8", "moe_wna16", "blockwise_int8","slimquant_w4a8","awq_marlin","slimquant_w4a8_marlin"
     ]
 
     @classmethod