[RL]moe bf16 ep support paddle batch_gemm (#7337)

* moe bf16 ep support paddle batch_gemm

fastdeploy/model_executor/layers/moe/fused_moe_cutlass_backend.py

@@ -43,6 +43,7 @@ if current_platform.is_cuda():
         logger.warning("import w4afp8_gemm_scale_permute Failed!")
 
 from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
+from fastdeploy.model_executor.layers.quantization.fp8_utils import paddlefleet_ops
 from fastdeploy.model_executor.utils import (
     TensorTracker,
     free_tensor,
@@ -166,18 +167,19 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
                     override_buffer_size=token_all_num,
                 )
 
-                token_nums_per_expert_cumsum = count_tokens_per_expert_func(
-                    recv_topk_idx, layer.num_local_experts, True
-                )[2].cast(paddle.int64)
-                ffn_out = self.compute_ffn(
-                    layer,
+                out = paddle.incubate.nn.functional.batched_gemm(
                     permute_input,
-                    token_nums_per_expert_cumsum,
-                    None,
-                    False,
-                    -1,
-                    None,
-                    None,
+                    getattr(layer, self.added_weight_attrs[0]),
+                    recv_num_tokens_per_expert_list,
+                )
+                if fastdeploy.envs.FD_MOE_PROB_IN_ADVANCE:
+                    out = paddlefleet_ops.fused_swiglu_scale(out, dst_weights)
+                else:
+                    out = paddle.incubate.nn.functional.swiglu(out)
+                ffn_out = paddle.incubate.nn.functional.batched_gemm(
+                    out,
+                    getattr(layer, self.added_weight_attrs[1]),
+                    recv_num_tokens_per_expert_list,
                 )
 
                 tmp_ffn_out, _out_probs = paddle.nn.functional.moe_unpermute(
@@ -187,7 +189,7 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
                     token_prob_unzipped=dst_weights,
                     total_zipped_tokens=recv_x.shape[0],
                     num_experts=layer.num_local_experts,
-                    using_weighted_combine=True,
+                    using_weighted_combine=not fastdeploy.envs.FD_MOE_PROB_IN_ADVANCE,
                 )
             else:
                 # --- original ep_moe_expert_dispatch / combine path ---