[NPU]Support model Trinity-mini for Npu, accuracy 90% (#18172)

Co-authored-by: sglang-npu-bot <sglangnpu@163.com>

python/sglang/srt/hardware_backend/npu/moe/topk.py

@@ -41,42 +41,33 @@ def fused_topk_npu(
             )
         topk_weights = topk_weights.to(torch.float32)
 
-    # Grouped top-k with correction bias
-    elif use_grouped_topk and correction_bias is not None:
-        topk_weights, topk_ids, _ = torch.ops.npu.npu_moe_gating_top_k(
-            router_logits.to(torch.float32),
-            k=topk_config.top_k,
-            bias=correction_bias.to(torch.float32),
-            k_group=topk_config.topk_group,
-            group_count=topk_config.num_expert_group,
-            group_select_mode=1,
-            renorm=0,
-            norm_type=1,
-            routed_scaling_factor=(
-                1 if renormalize else topk_config.routed_scaling_factor
-            ),
-            eps=float(1e-20),
-        )
-
-    # npu_moe_gating_top_k is not yet supported custom_routing_function
-    # torch native is not yet supported num_token_non_padded
+    # Support grouped top-k or correction bias or sigmoid or routed_scaling_factor
     elif (
-        topk_config.custom_routing_function is None
-        and num_token_non_padded is not None
-        and correction_bias is not None
+        correction_bias is not None
+        or topk_config.scoring_func == "sigmoid"
+        or num_token_non_padded is not None
     ):
         topk_weights, topk_ids, _ = torch.ops.npu.npu_moe_gating_top_k(
             router_logits.to(torch.float32),
             k=topk_config.top_k,
-            bias=correction_bias.to(torch.float32),
+            bias=(
+                correction_bias.to(torch.float32)
+                if correction_bias is not None
+                else None
+            ),
+            # num_expert_group and topk_group in some topk_config without group is None, (not supported by this ops)
+            k_group=topk_config.topk_group if use_grouped_topk else 1,
+            group_count=topk_config.num_expert_group if use_grouped_topk else 1,
+            group_select_mode=(1 if use_grouped_topk else 0),
             renorm=0,
-            norm_type=1,
+            norm_type=1,  # 1 for sigmoid, 0 for softmax
             routed_scaling_factor=(
                 1 if renormalize else topk_config.routed_scaling_factor
             ),
             eps=float(1e-20),
         )
 
+    # torch native is not yet supported num_token_non_padded
     # Fallback to torch native implementation
     else:
         topk_config.torch_native = True

python/sglang/srt/models/afmoe.py

@@ -58,7 +58,16 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.utils import add_prefix
+from sglang.srt.utils import add_prefix, is_npu
+
+_is_npu = is_npu()
+
+if not _is_npu:
+    from sglang.srt.layers.moe.fused_moe_triton import fused_moe
+else:
+    from sglang.srt.hardware_backend.npu.quantization.fused_moe_method_npu import (
+        fused_moe_npu as fused_moe,
+    )
 
 
 def get_attention_sliding_window_size(config: PretrainedConfig) -> Optional[int]:
@@ -200,6 +209,7 @@ class AfmoeMoE(nn.Module):
                 for idx in range(self.n_routed_experts)
             ]
         )
+
         self.pack_params()
 
         if self.num_shared_experts:
@@ -216,7 +226,7 @@ class AfmoeMoE(nn.Module):
             self.shared_experts = None
 
         custom_routing_fn = None
-        correction_bias = None
+        correction_bias = None if not _is_npu else self.expert_bias
         if self.use_grouped_topk:
             correction_bias = self.expert_bias
         elif self.score_func == "sigmoid":
@@ -226,7 +236,9 @@ class AfmoeMoE(nn.Module):
                 expert_bias=self.expert_bias,
             )
 
-        renormalize = self.route_norm if self.score_func == "sigmoid" else False
+        renormalize = (
+            self.route_norm if self.score_func == "sigmoid" and not _is_npu else False
+        )
         self.topk = TopK(
             top_k=self.top_k,
             renormalize=renormalize,
@@ -236,6 +248,7 @@ class AfmoeMoE(nn.Module):
             custom_routing_function=custom_routing_fn,
             correction_bias=correction_bias,
             routed_scaling_factor=self.route_scale,
+            **({"scoring_func": self.score_func} if _is_npu else {}),
         )
 
     def pack_params(self) -> None:
@@ -266,7 +279,7 @@ class AfmoeMoE(nn.Module):
 
         router_logits, _ = self.gate(hidden_states)
         topk_output = self.topk(hidden_states, router_logits)
-        final_hidden_states = fused_moe.fused_moe(
+        final_hidden_states = fused_moe(
             hidden_states,
             w1=self.w1,
             w2=self.w2,

python/sglang/test/ascend/test_ascend_utils.py

@@ -159,6 +159,7 @@ STABLELM_2_1_6B_WEIGHTS_PATH = os.path.join(
     MODEL_WEIGHTS_DIR, "stabilityai/stablelm-2-1_6b"
 )
 XVERSE_MOE_A36B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "xverse/XVERSE-MoE-A36B")
+TRINITY_MINI_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "arcee-ai/Trinity-Mini")
 MINIMAX_M2_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "cyankiwi/MiniMax-M2-BF16")
 
 # VLM model weights path