[feat]W8A8适配deepseek以及mtp

vllm/config/model.py

@@ -913,6 +913,7 @@ class ModelConfig:
                 "mxfp4",
                 "cpu_awq",
                 "slimquant_w4a8_marlin",
+                "slimquant_marlin",
                 "slimquant_compressed_tensors_marlin",
             ]
             quantization_methods = [

vllm/envs.py

@@ -280,6 +280,7 @@ if TYPE_CHECKING:
     VLLM_USE_LIGHTOP_MOE_SUM: bool = False
     VLLM_USE_LIGHTOP_MOE_ALIGN: bool = False
     VLLM_USE_MERGE_ATTN_STATES_OPT: bool = False
+    USE_FUSED_RMS_QUANT: bool = False
     VLLM_USE_PD_SPLIT: bool = False
     VLLM_USE_PP_SYNC: bool = False
     VLLM_USE_PIECEWISE: bool = False
@@ -1788,6 +1789,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_MERGE_ATTN_STATES_OPT":
         lambda: (os.environ.get("VLLM_USE_MERGE_ATTN_STATES_OPT", "True").lower() in
                  ("true", "1")),  
+    # vllm will use rmsquant fused op 
+    "USE_FUSED_RMS_QUANT": 
+        lambda: bool(int(os.getenv("USE_FUSED_RMS_QUANT", "0"))),
     # vLLM will split prefill and decode, not mix up
     "VLLM_USE_PD_SPLIT":
         lambda: (os.environ.get("VLLM_USE_PD_SPLIT", "False").lower() in

vllm/model_executor/layers/fused_moe/config.py

@@ -872,7 +872,6 @@ class FusedMoEParallelConfig:
 
     use_ep: bool  # whether to use EP or not
     all2all_backend: str  # all2all backend for MoE communication
-    is_sequence_parallel: bool  # whether sequence parallelism is used
     enable_eplb: bool  # whether to enable expert load balancing
 
     @property

vllm/model_executor/layers/fused_moe/layer.py

@@ -670,10 +670,6 @@ class FusedMoE(CustomOp):
     # This is called after all weight loading and post-processing, so it
     # should be safe to swap out the quant_method.
     def maybe_init_modular_kernel(self) -> None:
-        # NOTE(rob): WIP refactor. For quant methods that own the MK
-        # we create the MK during process_weights_after_loading.
-        if self.quant_method.supports_internal_mk or self.quant_method.is_monolithic:
-            return None
 
         self.ensure_moe_quant_config_init()
         # routing_tables only needed for round-robin expert placement with
@@ -1930,7 +1926,7 @@ class FusedMoE(CustomOp):
                     topk_weights=topk_weights,
                     topk_ids=topk_ids,
                     use_nn_moe=self.use_nn_moe,
-                    use_fused_gate=self.use_fused_gate,
+                    # use_fused_gate=self.use_fused_gate,
                 )
 
             if has_separate_shared_experts:

vllm/model_executor/layers/quantization/__init__.py

@@ -38,6 +38,7 @@ QuantizationMethods = Literal[
     "blockwise_int8",
     "slimquant_w4a8",
     "slimquant_w4a8_marlin",
+    "slimquant_marlin",
     "slimquant_compressed_tensors_marlin",
 ]
 QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
@@ -177,6 +178,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "blockwise_int8": BlockInt8Config,
         "slimquant_w4a8":SlimQuantW4A8Int8Config,
         "slimquant_w4a8_marlin":SlimQuantW4A8Int8MarlinConfig,
+        "slimquant_marlin":SlimQuantCompressedTensorsMarlinConfig,
         "slimquant_compressed_tensors_marlin":SlimQuantCompressedTensorsMarlinConfig,
     }
     # Update the `method_to_config` with customized quantization methods.

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_marlin.py

@@ -50,6 +50,8 @@ class SlimQuantCompressedTensorsMarlinConfig(CompressedTensorsConfig):
         kv_cache_scheme: Optional[dict[str, Any]] = None,
         config: Optional[dict[str, Any]] = None,
         transform_config: Optional[dict[str, Any]] = None,
+        total_num_heads: int | None = None,
+        total_num_kv_heads: int | None = None,
     ):
         super().__init__(
             target_scheme_map,
@@ -61,6 +63,9 @@ class SlimQuantCompressedTensorsMarlinConfig(CompressedTensorsConfig):
             config,
             transform_config
         )
+        self.total_num_heads = total_num_heads
+        self.total_num_kv_heads = total_num_kv_heads
+        
     @classmethod
     def override_quantization_method(
             cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py

@@ -147,52 +147,15 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
         layer.w13_weight = Parameter(w1_marlin, requires_grad=False)
         layer.w2_weight = Parameter(w2_marlin, requires_grad=False)
 
-
     def apply(
         self,
-        layer: torch.nn.Module,
+        layer: FusedMoE,
         x: torch.Tensor,
-        router_logits: torch.Tensor,
-        top_k: int,
-        renormalize: bool,
-        use_grouped_topk: bool = False,
-        topk_group: Optional[int] = None,
-        num_expert_group: Optional[int] = None,
-        global_num_experts: int = -1,
-        expert_map: Optional[torch.Tensor] = None,
-        custom_routing_function: Optional[Callable] = None,
-        scoring_func: str = "softmax",
-        e_score_correction_bias: Optional[torch.Tensor] = None,
-        apply_router_weight_on_input: bool = False,
-        activation: str = "silu",
-        enable_eplb: bool = False,
-        use_nn_moe: Optional[bool] = False,
-        routed_scaling_factor: Optional[float] = None,
-        use_fused_gate: Optional[bool] = False,
-        expert_load_view: Optional[torch.Tensor] = None,
-        logical_to_physical_map: Optional[torch.Tensor] = None,
-        logical_replica_count: Optional[torch.Tensor] = None,
-        shared_output: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if enable_eplb:
-            raise NotImplementedError(
-                "EPLB not supported for "
-                "`CompressedTensorsW8A8Int8MoEMethod` yet.")
-
-
-        topk_weights, topk_ids, _ = FusedMoE.select_experts(
-            hidden_states=x,
-            router_logits=router_logits,
-            use_grouped_topk=use_grouped_topk,
-            top_k=top_k,
-            renormalize=renormalize,
-            topk_group=topk_group,
-            num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function,
-            scoring_func=scoring_func,
-            routed_scaling_factor=routed_scaling_factor,
-            use_fused_gate=use_fused_gate,
-            e_score_correction_bias=e_score_correction_bias)
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        use_nn_moe: bool | None = False,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        from vllm.model_executor.layers.fused_moe import fused_experts
 
         return fused_experts_impl_int8_marlin(
             hidden_states=x,
@@ -201,16 +164,16 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
             topk_weights=topk_weights,
             topk_ids=topk_ids,
             inplace=True,
-            activation=activation,
-            apply_router_weight_on_input=apply_router_weight_on_input,
+            activation=layer.activation,
+            apply_router_weight_on_input=layer.apply_router_weight_on_input,
             use_int8_w8a8=True,
             per_channel_quant=True,
-            global_num_experts=global_num_experts,
-            expert_map=expert_map,
+            global_num_experts=layer.global_num_experts,
+            expert_map=layer.expert_map,
+            quant_config=self.moe_quant_config,
             w1_scale=layer.w13_weight_scale,
             w2_scale=layer.w2_weight_scale,
             a1_scale=layer.w13_input_scale,
             a2_scale=layer.w2_input_scale,
             use_nn_moe=False,
-            shared_output=shared_output,
-            routed_scaling_factor=routed_scaling_factor)
+        )
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