[Perf] Eliminate padding and slicing op for GPT-OSS with Flashinfer MXFP4 MXFP8 MoE (#30647)

Signed-off-by: elvischenv <219235043+elvischenv@users.noreply.github.com>

vllm/model_executor/layers/fused_moe/fused_moe_method_base.py

@@ -101,6 +101,11 @@ class FusedMoEMethodBase(QuantizeMethodBase):
             return self.moe_kernel.prepare_finalize.topk_indices_dtype()
         return None
 
+    @property
+    def skip_forward_padding(self) -> bool:
+        """Whether to skip the padding in the forward before applying the moe method."""
+        return False
+
     @property
     def supports_eplb(self) -> bool:
         return False

vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py

@@ -415,7 +415,10 @@ class DefaultMoERunner(MoERunner):
 
         # This is the dimension after transform (for routed expert output slicing)
         transformed_hidden_dim = hidden_states.shape[-1]
-        if self.moe_config.hidden_dim != transformed_hidden_dim:
+        if (
+            not self.quant_method.skip_forward_padding
+            and self.moe_config.hidden_dim != transformed_hidden_dim
+        ):
             hidden_states = F.pad(
                 hidden_states,
                 (0, self.moe_config.hidden_dim - transformed_hidden_dim),

vllm/model_executor/layers/quantization/mxfp4.py

@@ -294,6 +294,12 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         # Initialized in process_weights_after_loading for CUTLASS/SM90 backends
         self.moe_kernel: mk.FusedMoEKernel | None = None
 
+    @property
+    def skip_forward_padding(self) -> bool:
+        # SM100_FI_MXFP4_MXFP8_TRTLLM supports padding with mxfp8 quant
+        # so can skip the padding in the forward before applying the moe method
+        return self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -1130,9 +1136,17 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             elif self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM:
                 from flashinfer import mxfp8_quantize
 
-                x_quant, x_scale = mxfp8_quantize(x, False)  # to mxfp8
+                # x_quant is padded in hidden dimension with alignment=256
+                x_quant, x_scale = mxfp8_quantize(
+                    x,
+                    is_sf_swizzled_layout=False,
+                    alignment=256,
+                )
                 x_scale = x_scale.view(torch.float8_e4m3fn).reshape(*x.shape[:-1], -1)
 
+            # output with original unpadded hidden size
+            output = torch.empty_like(x)
+
             trtllm_gen_output = trtllm_fp4_block_scale_moe(
                 routing_logits=router_logits.to(torch.bfloat16),
                 routing_bias=None,
@@ -1161,6 +1175,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 routing_method_type=1 if layer.renormalize else 0,
                 do_finalize=True,
                 tune_max_num_tokens=max(self.max_capture_size, 1),
+                output=output,
             )[0]
             return trtllm_gen_output
         elif self.mxfp4_backend == Mxfp4Backend.CK: