Small refactor DeepEPDispatcher into subclasses (#4994)

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

@@ -23,7 +23,7 @@ _buffer_normal = None
 _buffer_low_latency = None
 
 
-def get_buffer_normal(group: dist.ProcessGroup, hidden_bytes: int):
+def _get_buffer_normal(group: dist.ProcessGroup, hidden_bytes: int):
     """
     Copy from DeepEP example usage in model inference prefilling.
     https://github.com/deepseek-ai/DeepEP?tab=readme-ov-file#example-use-in-model-training-or-inference-prefilling
@@ -53,7 +53,7 @@ def get_buffer_normal(group: dist.ProcessGroup, hidden_bytes: int):
     return _buffer_normal
 
 
-def get_buffer_low_latency(
+def _get_buffer_low_latency(
     group: dist.ProcessGroup,
     num_max_dispatch_tokens_per_rank: int,
     hidden: int,
@@ -85,24 +85,16 @@ def get_buffer_low_latency(
     return _buffer_low_latency
 
 
-class DeepEPDispatcher:
-    """
-    Copy from Megatron-Core token_dispatcher MoEFlexTokenDispatcher
-    https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/transformer/moe/token_dispatcher.py
-    """
-
+class _DeepEPDispatcherImplBase:
     def __init__(
         self,
         group: torch.distributed.ProcessGroup,
         router_topk: int,
-        permute_fusion: bool = False,
-        num_experts: int = None,
-        num_local_experts: int = None,
-        hidden_size: int = None,
-        params_dtype: torch.dtype = None,
-        deepep_mode: DeepEPMode = DeepEPMode.auto,
-        async_finish: bool = False,
-        return_recv_hook: bool = False,
+        permute_fusion: bool,
+        num_experts: int,
+        num_local_experts: int,
+        hidden_size: int,
+        params_dtype: torch.dtype,
     ):
         if not use_deepep:
             raise ImportError(
@@ -119,63 +111,36 @@ class DeepEPDispatcher:
         self.params_dtype = params_dtype
         self.params_bytes = 2
 
-        self.deepep_mode = deepep_mode
         self.handle = None
 
-        if self.deepep_mode.enable_normal():
-            self.buffer_normal = get_buffer_normal(
-                self.group, self.hidden_size * self.params_bytes
-            )
-            self.async_finish = async_finish
-            self.src2dst = None
-        if self.deepep_mode.enable_low_latency():
-            """
-            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
-            """
-            # TODO(ch-wan): allow users to set this value
-            self.num_max_dispatch_tokens_per_rank = 128
-            self.buffer_low_latency = get_buffer_low_latency(
-                self.group,
-                self.num_max_dispatch_tokens_per_rank,
-                self.hidden_size,
-                self.num_experts,
-            )
-            self.return_recv_hook = return_recv_hook
-
-    def deepep_permute(
+    def dispatch(
         self,
         hidden_states: torch.Tensor,
         topk_idx: torch.Tensor,
-        fp8_dtype: Optional[torch.dtype] = None,
-        use_fp8_w8a8: bool = False,
-        use_block_quant: bool = False,
+        topk_weights: torch.Tensor,
+        num_experts: int,
+        num_max_dispatch_tokens_per_rank: int,
     ):
-        reorder_topk_ids, self.src2dst, seg_indptr = deepep_run_moe_deep_preprocess(
-            topk_idx, self.num_experts
-        )
-        num_total_tokens = reorder_topk_ids.numel()
-        gateup_input = torch.empty(
-            (int(num_total_tokens), hidden_states.shape[1]),
-            device=hidden_states.device,
-            dtype=(
-                fp8_dtype
-                if (use_fp8_w8a8 and not use_block_quant)
-                else hidden_states.dtype
-            ),
-        )
-        # PreReorder
-        deepep_permute_triton_kernel[(hidden_states.shape[0],)](
-            hidden_states,
-            gateup_input,
-            self.src2dst,
-            topk_idx,
-            None,
-            self.router_topk,
-            hidden_states.shape[1],
-            BLOCK_SIZE=512,
+        raise NotImplementedError
+
+    def combine(
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        topk_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
+    def __init__(self, async_finish: bool, **kwargs):
+        super().__init__(**kwargs)
+
+        self.buffer_normal = _get_buffer_normal(
+            self.group, self.hidden_size * self.params_bytes
         )
-        return reorder_topk_ids, seg_indptr, gateup_input
+        self.async_finish = async_finish
+        self.src2dst = None
 
     def dispatch(
         self,
@@ -183,51 +148,34 @@ class DeepEPDispatcher:
         topk_idx: torch.Tensor,
         topk_weights: torch.Tensor,
         num_experts: int,
-        num_max_dispatch_tokens_per_rank: int = 128,
-        forward_mode: ForwardMode = None,
-    ) -> Tuple:
+        num_max_dispatch_tokens_per_rank: int,
+    ):
         topk_idx = topk_idx.to(torch.int64)
-        reorder_topk_ids = torch.empty(
-            (0,), device=hidden_states.device, dtype=torch.int64
-        )
-        seg_indptr = torch.zeros(
-            (num_experts + 1,), device=hidden_states.device, dtype=torch.int64
-        )
-        masked_m = torch.empty(
-            (self.num_local_experts,), device=hidden_states.device, dtype=torch.int64
-        )
-        expected_m = 0
-
-        resolved_deepep_mode = self.deepep_mode.resolve(forward_mode)
-        if resolved_deepep_mode == DeepEPMode.normal:
-            (
-                hidden_states,
-                topk_idx,
-                topk_weights,
-                event,
-            ) = self.dispatch_normal(hidden_states, topk_idx, topk_weights, num_experts)
-            event.current_stream_wait() if self.async_finish else ()
-            if hidden_states.shape[0] > 0:
-                reorder_topk_ids, seg_indptr, hidden_states = self.deepep_permute(
-                    hidden_states, topk_idx, fp8_dtype=hidden_states.dtype
-                )
-        elif resolved_deepep_mode == DeepEPMode.low_latency:
-            expected_m = (
-                hidden_states.shape[0]
-                * self.buffer_low_latency.group_size
-                * topk_idx.shape[1]
-                + num_experts
-            ) // num_experts
-            hidden_states, masked_m, event, hook = self.dispatch_low_latency(
-                hidden_states,
-                topk_idx,
-                num_max_dispatch_tokens_per_rank,
-                num_experts,
-                use_fp8=True,
+        (
+            hidden_states,
+            topk_idx,
+            topk_weights,
+            event,
+        ) = self._dispatch_normal(hidden_states, topk_idx, topk_weights, num_experts)
+        event.current_stream_wait() if self.async_finish else ()
+        if hidden_states.shape[0] > 0:
+            reorder_topk_ids, seg_indptr, hidden_states = self._deepep_permute(
+                hidden_states, topk_idx, fp8_dtype=hidden_states.dtype
             )
-            hook() if self.return_recv_hook else event.current_stream_wait()
         else:
-            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
+            reorder_topk_ids = torch.empty(
+                (0,), device=hidden_states.device, dtype=torch.int64
+            )
+            seg_indptr = torch.zeros(
+                (num_experts + 1,), device=hidden_states.device, dtype=torch.int64
+            )
+
+        # TODO
+        # masked_m = torch.empty(
+        #     (self.num_local_experts,), device=hidden_states.device, dtype=torch.int64
+        # )
+        # expected_m = 0
+        masked_m = expected_m = None
 
         return (
             hidden_states,
@@ -239,7 +187,7 @@ class DeepEPDispatcher:
             expected_m,
         )
 
-    def dispatch_normal(
+    def _dispatch_normal(
         self,
         x: torch.Tensor,
         topk_idx: torch.Tensor,
@@ -292,7 +240,156 @@ class DeepEPDispatcher:
             event,
         )
 
-    def dispatch_low_latency(
+    def _deepep_permute(
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        fp8_dtype: Optional[torch.dtype] = None,
+        use_fp8_w8a8: bool = False,
+        use_block_quant: bool = False,
+    ):
+        """
+        Copy from Megatron-Core token_dispatcher MoEFlexTokenDispatcher
+        https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/transformer/moe/token_dispatcher.py
+        """
+
+        reorder_topk_ids, self.src2dst, seg_indptr = deepep_run_moe_deep_preprocess(
+            topk_idx, self.num_experts
+        )
+        num_total_tokens = reorder_topk_ids.numel()
+        gateup_input = torch.empty(
+            (int(num_total_tokens), hidden_states.shape[1]),
+            device=hidden_states.device,
+            dtype=(
+                fp8_dtype
+                if (use_fp8_w8a8 and not use_block_quant)
+                else hidden_states.dtype
+            ),
+        )
+        # PreReorder
+        deepep_permute_triton_kernel[(hidden_states.shape[0],)](
+            hidden_states,
+            gateup_input,
+            self.src2dst,
+            topk_idx,
+            None,
+            self.router_topk,
+            hidden_states.shape[1],
+            BLOCK_SIZE=512,
+        )
+        return reorder_topk_ids, seg_indptr, gateup_input
+
+    def combine(
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        topk_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        if hidden_states.shape[0] > 0:
+            num_tokens = self.src2dst.shape[0] // self.router_topk
+            output = torch.empty(
+                (num_tokens, hidden_states.shape[1]),
+                device=hidden_states.device,
+                dtype=hidden_states.dtype,
+            )
+            deepep_post_reorder_triton_kernel[(num_tokens,)](
+                hidden_states,
+                output,
+                self.src2dst,
+                topk_idx,
+                topk_weights,
+                self.router_topk,
+                hidden_states.shape[1],
+                BLOCK_SIZE=512,
+            )
+        else:
+            output = torch.zeros(
+                (0, hidden_states.shape[1]),
+                device=hidden_states.device,
+                dtype=hidden_states.dtype,
+            )
+        hidden_states, event = self._combine_normal(
+            output,
+        )
+        event.current_stream_wait() if self.async_finish else ()
+
+        return hidden_states
+
+    def _combine_normal(self, x: torch.Tensor):
+        previous_event = Buffer.capture() if self.async_finish else None
+
+        combined_x, _, event = self.buffer_normal.combine(
+            x,
+            self.handle,
+            async_finish=self.async_finish,
+            previous_event=previous_event,
+            allocate_on_comm_stream=previous_event is not None,
+        )
+        return combined_x, event
+
+
+class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
+    def __init__(self, return_recv_hook: bool, **kwargs):
+        super().__init__(**kwargs)
+
+        """
+        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
+        """
+        # TODO(ch-wan): allow users to set this value
+        self.num_max_dispatch_tokens_per_rank = 128
+        self.buffer_low_latency = _get_buffer_low_latency(
+            self.group,
+            self.num_max_dispatch_tokens_per_rank,
+            self.hidden_size,
+            self.num_experts,
+        )
+        self.return_recv_hook = return_recv_hook
+
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        topk_weights: torch.Tensor,
+        num_experts: int,
+        num_max_dispatch_tokens_per_rank: int,
+    ):
+        topk_idx = topk_idx.to(torch.int64)
+        expected_m = (
+            hidden_states.shape[0]
+            * self.buffer_low_latency.group_size
+            * topk_idx.shape[1]
+            + num_experts
+        ) // num_experts
+        hidden_states, masked_m, event, hook = self._dispatch_low_latency(
+            hidden_states,
+            topk_idx,
+            num_max_dispatch_tokens_per_rank,
+            num_experts,
+            use_fp8=True,
+        )
+        hook() if self.return_recv_hook else event.current_stream_wait()
+
+        # TODO
+        # reorder_topk_ids = torch.empty(
+        #     (0,), device=hidden_states.device, dtype=torch.int64
+        # )
+        # seg_indptr = torch.zeros(
+        #     (num_experts + 1,), device=hidden_states.device, dtype=torch.int64
+        # )
+        reorder_topk_ids = seg_indptr = None
+
+        return (
+            hidden_states,
+            topk_idx,
+            topk_weights,
+            reorder_topk_ids,
+            seg_indptr,
+            masked_m,
+            expected_m,
+        )
+
+    def _dispatch_low_latency(
         self,
         hidden_states: torch.Tensor,
         topk_idx: torch.Tensor,
@@ -351,62 +448,17 @@ class DeepEPDispatcher:
         hidden_states: torch.Tensor,
         topk_idx: torch.Tensor,
         topk_weights: torch.Tensor,
-        forward_mode: ForwardMode,
     ) -> torch.Tensor:
-        resolved_deepep_mode = self.deepep_mode.resolve(forward_mode)
-        if resolved_deepep_mode == DeepEPMode.normal:
-            if hidden_states.shape[0] > 0:
-                num_tokens = self.src2dst.shape[0] // self.router_topk
-                output = torch.empty(
-                    (num_tokens, hidden_states.shape[1]),
-                    device=hidden_states.device,
-                    dtype=hidden_states.dtype,
-                )
-                deepep_post_reorder_triton_kernel[(num_tokens,)](
-                    hidden_states,
-                    output,
-                    self.src2dst,
-                    topk_idx,
-                    topk_weights,
-                    self.router_topk,
-                    hidden_states.shape[1],
-                    BLOCK_SIZE=512,
-                )
-            else:
-                output = torch.zeros(
-                    (0, hidden_states.shape[1]),
-                    device=hidden_states.device,
-                    dtype=hidden_states.dtype,
-                )
-            hidden_states, event = self.combine_normal(
-                output,
-            )
-            event.current_stream_wait() if self.async_finish else ()
-        elif resolved_deepep_mode == DeepEPMode.low_latency:
-            hidden_states, event, hook = self.combine_low_latency(
-                hidden_states,
-                topk_idx,
-                topk_weights,
-            )
-            hook() if self.return_recv_hook else event.current_stream_wait()
-        else:
-            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")
+        hidden_states, event, hook = self._combine_low_latency(
+            hidden_states,
+            topk_idx,
+            topk_weights,
+        )
+        hook() if self.return_recv_hook else event.current_stream_wait()
 
         return hidden_states
 
-    def combine_normal(self, x: torch.Tensor):
-        previous_event = Buffer.capture() if self.async_finish else None
-
-        combined_x, _, event = self.buffer_normal.combine(
-            x,
-            self.handle,
-            async_finish=self.async_finish,
-            previous_event=previous_event,
-            allocate_on_comm_stream=previous_event is not None,
-        )
-        return combined_x, event
-
-    def combine_low_latency(
+    def _combine_low_latency(
         self,
         hidden_states: torch.Tensor,
         topk_idx: torch.Tensor,
@@ -423,3 +475,80 @@ class DeepEPDispatcher:
             )
         )
         return combined_hidden_states, event, hook
+
+
+class DeepEPDispatcher:
+    def __init__(
+        self,
+        group: torch.distributed.ProcessGroup,
+        router_topk: int,
+        permute_fusion: bool = False,
+        num_experts: int = None,
+        num_local_experts: int = None,
+        hidden_size: int = None,
+        params_dtype: torch.dtype = None,
+        deepep_mode: DeepEPMode = DeepEPMode.auto,
+        async_finish: bool = False,
+        return_recv_hook: bool = False,
+    ):
+        self.deepep_mode = deepep_mode
+
+        common_kwargs = dict(
+            group=group,
+            router_topk=router_topk,
+            permute_fusion=permute_fusion,
+            num_experts=num_experts,
+            num_local_experts=num_local_experts,
+            hidden_size=hidden_size,
+            params_dtype=params_dtype,
+        )
+
+        if self.deepep_mode.enable_normal():
+            self._normal_dispatcher = _DeepEPDispatcherImplNormal(
+                async_finish=async_finish,
+                **common_kwargs,
+            )
+        if self.deepep_mode.enable_low_latency():
+            self._low_latency_dispatcher = _DeepEPDispatcherImplLowLatency(
+                return_recv_hook=return_recv_hook,
+                **common_kwargs,
+            )
+
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        topk_weights: torch.Tensor,
+        num_experts: int,
+        num_max_dispatch_tokens_per_rank: int = 128,
+        forward_mode: ForwardMode = None,
+    ) -> Tuple:
+        return self._get_dispatcher(forward_mode).dispatch(
+            hidden_states=hidden_states,
+            topk_idx=topk_idx,
+            topk_weights=topk_weights,
+            num_experts=num_experts,
+            num_max_dispatch_tokens_per_rank=num_max_dispatch_tokens_per_rank,
+        )
+
+    def combine(
+        self,
+        hidden_states: torch.Tensor,
+        topk_idx: torch.Tensor,
+        topk_weights: torch.Tensor,
+        forward_mode: ForwardMode,
+    ) -> torch.Tensor:
+        return self._get_dispatcher(forward_mode).combine(
+            hidden_states=hidden_states,
+            topk_idx=topk_idx,
+            topk_weights=topk_weights,
+        )
+
+    def _get_dispatcher(self, forward_mode: ForwardMode) -> _DeepEPDispatcherImplBase:
+        resolved_deepep_mode = self.deepep_mode.resolve(forward_mode)
+        if resolved_deepep_mode == DeepEPMode.normal:
+            return self._normal_dispatcher
+        elif resolved_deepep_mode == DeepEPMode.low_latency:
+            return self._low_latency_dispatcher
+        else:
+            raise ValueError(f"Invalid deepep_mode: {self.deepep_mode}")

python/sglang/srt/models/deepseek_v2.py

@@ -188,35 +188,24 @@ class DeepseekV2MoE(nn.Module):
             if global_server_args_dict["enable_deepep_moe"]
             else (EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE)
         )
-        if not global_server_args_dict["enable_deepep_moe"]:
-            self.experts = MoEImpl(
-                num_experts=config.n_routed_experts,
-                top_k=config.num_experts_per_tok,
-                hidden_size=config.hidden_size,
-                intermediate_size=config.moe_intermediate_size,
-                renormalize=config.norm_topk_prob,
-                quant_config=quant_config,
-                use_grouped_topk=True,
-                num_expert_group=config.n_group,
-                topk_group=config.topk_group,
-                correction_bias=self.gate.e_score_correction_bias,
-                prefix=add_prefix("experts", prefix),
-            )
-        else:
-            self.experts = MoEImpl(
-                num_experts=config.n_routed_experts,
-                top_k=config.num_experts_per_tok,
-                hidden_size=config.hidden_size,
-                intermediate_size=config.moe_intermediate_size,
-                renormalize=config.norm_topk_prob,
-                quant_config=quant_config,
-                use_grouped_topk=True,
-                num_expert_group=config.n_group,
-                topk_group=config.topk_group,
-                correction_bias=self.gate.e_score_correction_bias,
-                prefix=add_prefix("experts", prefix),
-                deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]],
-            )
+        self.experts = MoEImpl(
+            num_experts=config.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            topk_group=config.topk_group,
+            correction_bias=self.gate.e_score_correction_bias,
+            prefix=add_prefix("experts", prefix),
+            **(
+                dict(deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]])
+                if global_server_args_dict["enable_deepep_moe"]
+                else {}
+            ),
+        )
 
         if config.n_shared_experts is not None:
             intermediate_size = config.moe_intermediate_size * config.n_shared_experts