[Bugfix] Qwen3MoE aclrtMemcpy failed with NPUGraph (#10013)

.github/workflows/pr-test-npu.yml

@@ -117,7 +117,7 @@ jobs:
           curl -o /tmp/test.jsonl -L https://gh-proxy.test.osinfra.cn/https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl
 
       - name: Run test
-        timeout-minutes: 60
+        timeout-minutes: 120
         env:
           SGLANG_USE_MODELSCOPE: true
           SGLANG_IS_IN_CI: true

python/sglang/srt/layers/quantization/unquant.py

@@ -384,19 +384,83 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         dispatch_output: StandardDispatchOutput,
     ) -> CombineInput:
 
-        from sglang.srt.layers.moe.fused_moe_native import moe_forward_native
+        import torch_npu
+
         from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
 
         x = dispatch_output.hidden_states
-        topk_output = dispatch_output.topk_output
+        topk_weights, topk_ids, _ = dispatch_output.topk_output
 
-        output = moe_forward_native(
+        original_dtype = x.dtype
-            layer,
+        num_tokens = x.shape[0]
-            x,
+        topk_weights = topk_weights.to(x.dtype)
-            topk_output,
+        topk_ids = topk_ids.to(torch.int32)
-            self.moe_runner_config,
+        num_experts = layer.num_experts
+        top_k = layer.top_k
+        row_idx_len = num_tokens * top_k
+        row_idx = (
+            torch.arange(0, row_idx_len, dtype=torch.int32, device=topk_weights.device)
+            .view(top_k, -1)
+            .permute(1, 0)
+            .contiguous()
         )
-        return StandardCombineInput(hidden_states=output)
+
+        hidden_states, expanded_row_idx, expanded_expert_idx = (
+            torch_npu.npu_moe_init_routing(
+                x, row_idx=row_idx, expert_idx=topk_ids, active_num=num_tokens
+            )
+        )
+
+        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
+            expanded_expert_idx, num_experts
+        )
+
+        expert_tokens = expert_tokens.to(torch.int64)
+        if layer.w13_weight.shape[-1] == layer.hidden_size:
+            w13 = layer.w13_weight.transpose(1, 2)
+            w2 = layer.w2_weight.transpose(1, 2)
+
+        # gmm1: gate_up_proj
+        hidden_states = torch_npu.npu_grouped_matmul(
+            x=[hidden_states],
+            weight=[w13],
+            split_item=2,
+            group_list_type=0,
+            group_type=0,
+            group_list=expert_tokens,
+            output_dtype=original_dtype,
+        )[0]
+
+        # act_fn:
+        if self.moe_runner_config.activation == "silu":
+            hidden_states = torch_npu.npu_swiglu(hidden_states)
+        else:
+            from sglang.srt.layers.activation import GeluAndMul
+
+            hidden_states = GeluAndMul()(hidden_states)
+
+        # gmm2: down_proj
+        hidden_states = torch_npu.npu_grouped_matmul(
+            x=[hidden_states],
+            weight=[w2],
+            split_item=2,
+            group_list_type=0,
+            group_type=0,
+            group_list=expert_tokens,
+            output_dtype=original_dtype,
+        )[0]
+
+        final_hidden_states = torch_npu.npu_moe_finalize_routing(
+            hidden_states,
+            skip1=None,
+            skip2=None,
+            bias=None,
+            scales=topk_weights,
+            expanded_src_to_dst_row=expanded_row_idx,
+            export_for_source_row=topk_ids,
+        )
+
+        return StandardCombineInput(hidden_states=final_hidden_states)
 
     def forward_tpu(self, *args, **kwargs) -> CombineInput:
         raise NotImplementedError("The TPU backend currently does not support MoE.")

python/sglang/srt/layers/quantization/w4afp8.py

@@ -17,7 +17,11 @@ from sglang.srt.layers.quantization.base_config import (
 from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
 from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 from sglang.srt.layers.quantization.utils import is_layer_skipped
-from sglang.srt.utils import set_weight_attrs
+from sglang.srt.utils import is_npu, set_weight_attrs
+
+_is_npu = is_npu()
+if not _is_npu:
+    from sglang.srt.layers.moe.cutlass_w4a8_moe import cutlass_w4a8_moe
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe import MoeRunnerConfig

test/srt/ascend/test_ascend_tp4_bf16.py

@@ -0,0 +1,101 @@
+import unittest
+from types import SimpleNamespace
+from urllib.parse import urlparse
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_ci,
+    popen_launch_server,
+    run_bench_offline_throughput,
+)
+
+TEST_MODEL_MATRIX = {
+    "Qwen/Qwen3-30B-A3B-Instruct-2507": {
+        "accuracy": 0.90,
+        "latency": 180,
+        "output_throughput": 20,
+    },
+}
+
+
+class TestAscendTp4Bf16(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.models = TEST_MODEL_MATRIX.keys()
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.url = urlparse(DEFAULT_URL_FOR_TEST)
+        cls.common_args = [
+            "--trust-remote-code",
+            "--mem-fraction-static",
+            0.7,
+            "--max-running-requests",
+            32,
+            "--attention-backend",
+            "ascend",
+            "--cuda-graph-max-bs",
+            32,
+            "--tp-size",
+            4,
+        ]
+
+    def test_a_gsm8k(self):
+        for model in self.models:
+            with self.subTest(model=model):
+                print(f"##=== Testing accuracy: {model} ===##")
+
+                process = popen_launch_server(
+                    model,
+                    self.base_url,
+                    timeout=1800,
+                    other_args=[
+                        *self.common_args,
+                    ],
+                )
+
+                try:
+                    args = SimpleNamespace(
+                        num_shots=5,
+                        data_path=None,
+                        num_questions=1319,
+                        max_new_tokens=512,
+                        parallel=128,
+                        host=f"http://{self.url.hostname}",
+                        port=int(self.url.port),
+                    )
+
+                    metrics = run_eval_few_shot_gsm8k(args)
+                    self.assertGreaterEqual(
+                        metrics["accuracy"],
+                        TEST_MODEL_MATRIX[model]["accuracy"],
+                    )
+                finally:
+                    kill_process_tree(process.pid)
+
+    def test_b_throughput(self):
+        for model in self.models:
+            with self.subTest(model=model):
+                print(f"##=== Testing throughput: {model} ===##")
+
+                output_throughput = run_bench_offline_throughput(
+                    model,
+                    [
+                        *self.common_args,
+                    ],
+                )
+
+                print(f"##=== {model} throughput: {output_throughput} ===##")
+
+                if is_in_ci():
+                    self.assertGreater(
+                        output_throughput,
+                        TEST_MODEL_MATRIX[model]["output_throughput"],
+                    )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/run_suite.py

@@ -294,6 +294,7 @@ suite_ascend = {
     ],
     "per-commit-4-ascend-npu": [
         TestFile("ascend/test_ascend_mla_w8a8int8.py", 400),
+        TestFile("ascend/test_ascend_tp4_bf16.py", 400),
     ],
 }