Support piecewise cuda graph for dsv3 fp4 (#15531)

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

@@ -41,7 +41,13 @@ from sglang.srt.layers.moe.token_dispatcher.standard import (
     StandardDispatcher,
     StandardDispatchOutput,
 )
-from sglang.srt.layers.moe.topk import StandardTopKOutput, TopKOutput, TopKOutputChecker
+from sglang.srt.layers.moe.topk import (
+    BypassedTopKOutput,
+    StandardTopKOutput,
+    TopKConfig,
+    TopKOutput,
+    TopKOutputChecker,
+)
 from sglang.srt.layers.moe.utils import RoutingMethodType
 from sglang.srt.layers.quantization.base_config import (
     FusedMoEMethodBase,
@@ -1210,16 +1216,21 @@ class FlashInferFP4MoE(FusedMoE):
         return hs_fp4, hs_sf
 
     def forward(self, hidden_states: torch.Tensor, topk_output: TopKOutput):
+        assert TopKOutputChecker.format_is_bypassed(
+            topk_output
+        ), "Only bypassed topk output is supported for flashinfer fp4 moe"
+
         if is_in_piecewise_cuda_graph():
-            assert TopKOutputChecker.format_is_standard(
-                topk_output
-            ), "Only standard topk output is supported for piecewise cuda graph"
-            return torch.ops.sglang.moe_forward_piecewise_cuda_graph_impl(
-                hidden_states,
-                topk_output.topk_weights,
-                topk_output.topk_ids,
-                topk_output.router_logits,
-                self.layer_id,
+            return (
+                torch.ops.sglang.flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl(
+                    hidden_states,
+                    topk_output.router_logits,
+                    topk_output.topk_config.top_k,
+                    topk_output.topk_config.topk_group,
+                    topk_output.topk_config.num_expert_group,
+                    topk_output.topk_config.correction_bias,
+                    self.layer_id,
+                )
             )
         else:
             return self.forward_impl(hidden_states, topk_output)
@@ -1343,9 +1354,52 @@ def moe_forward_piecewise_cuda_graph_impl_fake(
     return torch.empty_like(hidden_states)
 
 
+def flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    top_k: int,
+    topk_group: Optional[int],
+    num_expert_group: Optional[int],
+    correction_bias: Optional[torch.Tensor],
+    layer_id: int,
+) -> torch.Tensor:
+    topk_output = BypassedTopKOutput(
+        hidden_states=hidden_states,
+        router_logits=router_logits,
+        topk_config=TopKConfig(
+            top_k=top_k,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            correction_bias=correction_bias,
+        ),
+    )
+    forward_context = get_forward_context()
+    moe_layer = forward_context.moe_layers[layer_id]
+    return moe_layer.forward_impl(hidden_states, topk_output)
+
+
+def flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl_fake(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    top_k: int,
+    topk_group: Optional[int],
+    num_expert_group: Optional[int],
+    correction_bias: Optional[torch.Tensor],
+    layer_id: int,
+) -> torch.Tensor:
+    return torch.empty_like(hidden_states)
+
+
 direct_register_custom_op(
     op_name="moe_forward_piecewise_cuda_graph_impl",
     op_func=moe_forward_piecewise_cuda_graph_impl,
     mutates_args=[],
     fake_impl=moe_forward_piecewise_cuda_graph_impl_fake,
 )
+
+direct_register_custom_op(
+    op_name="flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl",
+    op_func=flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl,
+    mutates_args=[],
+    fake_impl=flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl_fake,
+)