使用groupgemm完成高吞吐模式适配。

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

@@ -576,7 +576,6 @@ class DeepEPMoE(EPMoE):
 
         if all_tokens <= 0:
             return hidden_states.bfloat16()
-        num_local_tokens = torch.tensor(num_recv_tokens_per_expert, dtype=torch.int32, device="cuda")
         expert_output = self.quant_method.apply_ep(
             x=hidden_states,
             w1=self.w13_weight,
@@ -591,7 +590,6 @@ class DeepEPMoE(EPMoE):
             w1_scale=self.w13_weight_scale,
             w2_scale=self.w2_weight_scale,
             routed_scaling_factor = self.moe_runner_config.routed_scaling_factor,
-            # num_local_tokens=num_local_tokens,
         )
         return expert_output
 

python/sglang/srt/layers/moe/token_dispatcher/deepep.py

@@ -4,7 +4,7 @@ import logging
 from contextlib import nullcontext
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, List, NamedTuple, Optional, Tuple, Union
-
+from sglang.srt.distributed import get_moe_expert_parallel_rank, get_moe_expert_parallel_world_size
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.dp_attention import get_is_extend_in_batch
@@ -357,18 +357,18 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
     ):
         topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
         topk_ids = topk_ids.to(torch.int64)
-        if (
-            deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
-            and not get_moe_runner_backend().is_cutlass()
-        ):
-            # TODO hard code 128 block quant,use fp8 communication
-            hidden_states = sglang_per_token_group_quant_fp8(
-                hidden_states,
-                128,
-                column_major_scales=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
-                scale_tma_aligned=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
-                scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
-            )
+        # if (
+        #     deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
+        #     and not get_moe_runner_backend().is_cutlass()
+        # ):
+        #     # TODO hard code 128 block quant,use fp8 communication
+        #     hidden_states = sglang_per_token_group_quant_fp8(
+        #         hidden_states,
+        #         128,
+        #         column_major_scales=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+        #         scale_tma_aligned=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+        #         scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+        #     )
         previous_event = Buffer.capture() if self.async_finish else None
         return hidden_states, topk_ids, topk_weights, previous_event
 
@@ -380,7 +380,7 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
             num_recv_tokens_per_expert,
             event,
         ) = self._dispatch_core(hidden_states, topk_ids, topk_weights, previous_event)
-        event.current_stream_wait() if self.async_finish else ()
+        # event.current_stream_wait() if self.async_finish else ()
 
         if isinstance(hidden_states, tuple):
             hidden_states, hidden_states_scale = hidden_states
@@ -435,18 +435,23 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
             num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
             is_token_in_rank=is_token_in_rank,
             num_tokens_per_expert=num_tokens_per_expert,
-            previous_event=previous_event,
-            async_finish=self.async_finish,
-            allocate_on_comm_stream=(previous_event is not None) and self.async_finish,
-            expert_alignment=128 if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM else 1,
+            previous_event=None,
+            async_finish=False,
+            allocate_on_comm_stream=False,
+            expert_alignment=1,
             config=DeepEPConfig.get_instance().normal_dispatch_config,
         )
-        get_global_expert_distribution_recorder().on_deepep_dispatch_normal(
-            num_recv_tokens_per_expert,
-            num_tokens_per_rank=num_tokens_per_rank,
-            num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
-            num_tokens_per_expert=num_tokens_per_expert,
-        )
+        # get_global_expert_distribution_recorder().on_deepep_dispatch_normal(
+        #     num_recv_tokens_per_expert,
+        #     num_tokens_per_rank=num_tokens_per_rank,
+        #     num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
+        #     num_tokens_per_expert=num_tokens_per_expert,
+        # )
+        self.rank_expert_offset= get_moe_expert_parallel_rank() * ( self.num_experts // get_moe_expert_parallel_world_size())
+        recv_topk_ids = torch.where(
+            recv_topk_ids == -1,
+            self.num_experts - 1 if self.rank_expert_offset == 0 else 0,
+            recv_topk_ids + self.rank_expert_offset)
 
         return (
             recv_x,
@@ -495,7 +500,7 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
 
     def combine_b(self, output, previous_event):
         hidden_states, event = self._combine_core(output, previous_event)
-        event.current_stream_wait() if self.async_finish else ()
+        # event.current_stream_wait() if self.async_finish else ()
         self.handle = None
         self.src2dst = None
         return hidden_states
@@ -505,9 +510,9 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
         combined_x, _, event = buffer.combine(
             x,
             self.handle,
-            async_finish=self.async_finish,
-            previous_event=previous_event,
-            allocate_on_comm_stream=previous_event is not None,
+            async_finish=False,
+            previous_event=None,
+            allocate_on_comm_stream=False,
             config=DeepEPConfig.get_instance().normal_combine_config,
         )
         return combined_x, event
@@ -536,7 +541,7 @@ class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
         num_max_dispatch_tokens_per_rank: the actual batch size in the decoding engine should be less than 256
         https://github.com/deepseek-ai/DeepEP?tab=readme-ov-file#example-use-in-inference-decoding
         """
-        self.return_recv_hook = return_recv_hook
+        self.return_recv_hook = False
         self.device_module = torch.get_device_module()
         self.quant_config = {}
 

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

@@ -73,7 +73,7 @@ class SlimQuantCompressedTensorsMarlinConfig(CompressedTensorsConfig):
             prefix: str,
     ) -> Optional["QuantizeMethodBase"]:
         from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE  # Avoid circular import
-        # from sglang.srt.layers.radix_attention import RadixAttention
+        from sglang.srt.layers.radix_attention import RadixAttention
         # Check if the layer is skipped for quantization.
         if should_ignore_layer(prefix,
                                ignore=self.ignore,
@@ -85,8 +85,8 @@ class SlimQuantCompressedTensorsMarlinConfig(CompressedTensorsConfig):
                 return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
             layer.scheme = scheme
             return CompressedTensorsLinearMethod(self)
-        # if isinstance(layer, RadixAttention):
-        #     return CompressedTensorsKVCacheMethod(self)
+        if isinstance(layer, RadixAttention):
+            return CompressedTensorsKVCacheMethod(self)
         if isinstance(layer, FusedMoE):
             return CompressedTensorsMarlinMoEMethod.get_moe_method(self, layer)
         return None