[V1] [ROCm] Enable EP with AITER Fused MoE (#20270)

Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>

vllm/model_executor/layers/fused_moe/layer.py

@@ -646,13 +646,13 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             indices_type=self.topk_indices_dtype)
 
         if self.rocm_aiter_moe_enabled:
-            assert expert_map is None
             return self.rocm_aiter_fused_experts(
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
+                expert_map=expert_map,
                 activation=activation,
                 apply_router_weight_on_input=apply_router_weight_on_input)
         else:

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -315,7 +315,8 @@ def rocm_aiter_fused_experts(
         w2_scale: Optional[torch.Tensor] = None,
         a1_scale: Optional[torch.Tensor] = None,
         a2_scale: Optional[torch.Tensor] = None,
-        block_shape: Optional[list[int]] = None) -> torch.Tensor:
+        block_shape: Optional[list[int]] = None,
+        expert_map: Optional[torch.Tensor] = None) -> torch.Tensor:
 
     activation_method = (ActivationMethod.SILU
                          if activation == "silu" else ActivationMethod.GELU)
@@ -323,6 +324,11 @@ def rocm_aiter_fused_experts(
     topk_weights = topk_weights.to(torch.float32)
     topk_ids = topk_ids.to(torch.int32)
 
+    if expert_map is not None:
+        expert_mask = (expert_map > -1).to(torch.int32)
+    else:
+        expert_mask = None
+
     # w8a8 per-channel quantization
     if per_channel_quant and apply_router_weight_on_input and use_fp8_w8a8:
         # AITER tkw1 kernel for FP8 models with `apply_router_weight_on_input`
@@ -346,7 +352,7 @@ def rocm_aiter_fused_experts(
             fc2_smooth_scale=None,
             a16=False,
             per_tensor_quant_scale=None,
-            expert_mask=None,
+            expert_mask=expert_mask,
             activation_method=activation_method)
 
     else:
@@ -378,6 +384,7 @@ def rocm_aiter_fused_experts(
             w2,
             topk_weights,
             topk_ids,
+            expert_mask=expert_mask,
             quant_method=quant_method,
             activation_method=activation_method,
             w1_scale=w1_scale,

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

@@ -633,7 +633,8 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                 w1_scale=layer.w13_weight_scale,
                 w2_scale=layer.w2_weight_scale,
                 a1_scale=layer.w13_input_scale,
-                a2_scale=layer.w2_input_scale)
+                a2_scale=layer.w2_input_scale,
+                expert_map=expert_map)
         if self.use_marlin:
             assert activation == "silu", (
                 f"{activation} not supported for Marlin MoE.")

vllm/model_executor/layers/quantization/fp8.py

@@ -442,6 +442,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
     """
 
     def __init__(self, quant_config: Fp8Config):
+
         from vllm.model_executor.layers.fused_moe import fused_experts
         self.quant_config = quant_config
         self.block_quant = self.quant_config.weight_block_size is not None
@@ -879,7 +880,8 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                           if self.block_quant else layer.w2_weight_scale),
                 a1_scale=layer.w13_input_scale,
                 a2_scale=layer.w2_input_scale,
-                block_shape=self.quant_config.weight_block_size)
+                block_shape=self.quant_config.weight_block_size,
+                expert_map=expert_map)
         elif self.use_marlin:
             assert activation == "silu", (
                 f"{activation} not supported for Marlin MoE.")