[Feat][BugFix]Support the Qwen3-Next-80B-A3B-Instruct quantization model&Fix the NZ issue (#4245)

### What this PR does / why we need it?
Support the Qwen3-Next-80B-A3B-Instruct quantization model and Fix the
NZ issue. Triton kernel doesn't support data format nz, thus we skip
converting weight to nz on layer `conv1d`

- vLLM version: v0.11.0
- vLLM main:
vllm-project/vllm@2918c1b

---------

Signed-off-by: IncSec <1790766300@qq.com>

Author: InSec

tests/e2e/multicard/test_qwen3_next.py

@@ -20,6 +20,12 @@
 
 Run `pytest tests/e2e/multicard/test_qwen3_next.py`.
 """
+
+import os
+from unittest.mock import patch
+
+from modelscope import snapshot_download  # type: ignore
+
 from tests.e2e.conftest import VllmRunner
 
 
@@ -106,3 +112,23 @@ def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
             print(f"spec_output: {spec_output[1]}")
 
     assert matches > int(0.66 * len(ref_outputs))
+
+
+# TODO: will conduct accuracy verification after the subsequent version becomes stable
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
+def test_models_distributed_Qwen3_NEXT_W8A8DYNAMIC_WITH_EP():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            snapshot_download(
+                "vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8-Pruning"),
+            max_model_len=4096,
+            tensor_parallel_size=2,
+            gpu_memory_utilization=0.4,
+            max_num_seqs=1,
+            enable_expert_parallel=True,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)

tests/ut/attention/test_mla_v1.py

@@ -797,7 +797,6 @@ def test_q_proj_and_k_up_proj(self):
         self.assertEqual(q_pe.shape[1], self.impl.num_heads)
         self.assertEqual(q_pe.shape[2], self.impl.qk_rope_head_dim)
 
-    @patch('vllm_ascend.utils._ENABLE_NZ', True)
     @patch('torch_npu.npu_format_cast')
     def test_process_weights_after_loading(self, mock_format_cast):
         layer = MagicMock(spec=LinearBase)

tests/ut/models/test_qwen2_5_vl.py

@@ -1,5 +1,3 @@
-from unittest.mock import patch
-
 import pytest
 import torch
 import torch.nn.functional as F
@@ -367,7 +365,6 @@ def test_pad_qkv_bias(self, mocker: MockerFixture):
         res = attention.pad_qkv_bias(torch.rand((300)))
         assert res.shape[0] == 384
 
-    @patch('vllm_ascend.utils._ENABLE_NZ', True)
     def test_pad_qkv_weight(self, mocker: MockerFixture):
         attention = self.init_vision_transformer(mocker)
         mocker.patch("torch.nn.Module.__setattr__")
@@ -380,7 +377,6 @@ def test_pad_qkv_weight(self, mocker: MockerFixture):
         res = attention.pad_qkv_weight(torch.rand((300, 300)))
         assert res.shape == (384, 300)
 
-    @patch('vllm_ascend.utils._ENABLE_NZ', True)
     def test_pad_proj_weight(self, mocker: MockerFixture):
         attention = self.init_vision_transformer(mocker)
         mocker.patch("torch.nn.Module.__setattr__")

tests/ut/quantization/test_w4a8_dynamic.py

@@ -260,7 +260,6 @@ def build_layer(self,
                 requires_grad=False)
         return layer
 
-    @patch('vllm_ascend.utils._ENABLE_NZ', False)
     @patch('torch_npu.npu_format_cast')
     @patch('torch_npu.npu_quantize')
     @patch('torch.Tensor.npu')

tests/ut/test_utils.py

@@ -46,18 +46,12 @@ def test_is_310p(self):
             self.assertFalse(utils.is_310p())
 
     def test_is_enable_nz(self):
-        # Case when _ENABLE_NZ is already set
-        utils._ENABLE_NZ = True
-        self.assertTrue(utils.is_enable_nz())
-
-        utils._ENABLE_NZ = False
-        self.assertFalse(utils.is_enable_nz())
-
-        # Case when _ENABLE_NZ is None and vllm_config is not provided
-        utils._ENABLE_NZ = None
-        with self.assertRaises(ValueError) as context:
-            utils.is_enable_nz()
-        self.assertIn("vllm_config must be provided", str(context.exception))
+        with mock.patch("vllm_ascend.utils.envs_ascend.VLLM_ASCEND_ENABLE_NZ",
+                        1):
+            self.assertTrue(utils.is_enable_nz())
+        with mock.patch("vllm_ascend.utils.envs_ascend.VLLM_ASCEND_ENABLE_NZ",
+                        0):
+            self.assertFalse(utils.is_enable_nz())
 
     def test_sleep_mode_enabled(self):
         utils._SLEEP_MODE_ENABLED = None

tests/ut/worker/test_worker_v1.py

@@ -281,9 +281,9 @@ def test_sleep_mode_disabled_raises_error(self, mock_sleep_mode_enabled):
 
             self.assertIn("Sleep mode is not enabled", str(cm.exception))
 
-    @patch('vllm_ascend.utils._ENABLE_NZ', False)
     @patch("vllm_ascend.worker.worker_v1.sleep_mode_enabled")
     @patch("vllm_ascend.worker.worker_v1.CaMemAllocator")
+    @patch.dict("os.environ", {"VLLM_ASCEND_ENABLE_NZ": "0"})
     def test_wake_up_mode_enabled(self, mock_allocator_class,
                                   mock_sleep_mode_enabled):
         """Test wake_up method when sleep mode is enabled"""

vllm_ascend/ops/linear.py

@@ -45,7 +45,8 @@ class AscendUnquantizedLinearMethod(UnquantizedLinearMethod):
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         super().process_weights_after_loading(layer)
-        if (is_enable_nz() and layer.weight.data.dtype
+        if "conv1d" not in layer.prefix and (
+                is_enable_nz() and layer.weight.data.dtype
                 in [torch.float16, torch.bfloat16]):
             layer.weight.data = torch_npu.npu_format_cast(
                 layer.weight.data, ACL_FORMAT_FRACTAL_NZ)

vllm_ascend/quantization/quant_config.py

@@ -222,6 +222,8 @@ def get_scaled_act_names(self) -> List[str]:
         ],
         "gate_up_proj": ["gate_proj", "up_proj"],
         "in_proj": ["in_proj_qkvz", "in_proj_ba"],
+        "experts":
+        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
     },
     "qwen2_5_vl": {
         "qkv_proj": [

vllm_ascend/utils.py

@@ -59,7 +59,6 @@
 _IS_MOE_MODEL = None
 _ENABLE_SP = None
 _HAS_LAYER_IDX = None
-_ENABLE_NZ = None
 _SUBSCRIBED_COMPUTE_STREAMS = set()
 _GRAPH_PRINT_STREAM = None
 _GRAPH_PRINT_STREAM_LOCK = Lock()
@@ -129,14 +128,8 @@ def is_310p():
     return _IS_310P
 
 
-def is_enable_nz(vllm_config: Optional[VllmConfig] = None) -> bool:
-    global _ENABLE_NZ
-    if _ENABLE_NZ is None:
-        if not vllm_config:
-            raise ValueError(
-                "vllm_config must be provided when _ENABLE_NZ is None")
-        _ENABLE_NZ = envs_ascend.VLLM_ASCEND_ENABLE_NZ and vllm_config.model_config.hf_config.model_type != "qwen3_next"
-    return _ENABLE_NZ
+def is_enable_nz():
+    return envs_ascend.VLLM_ASCEND_ENABLE_NZ
 
 
 def sleep_mode_enabled():

vllm_ascend/worker/worker_v1.py

@@ -87,7 +87,6 @@ def __init__(
         # register patch for vllm
         from vllm_ascend.utils import adapt_patch
         adapt_patch()
-        is_enable_nz(vllm_config)
         # Register ops when worker init.
         from vllm_ascend import ops
         ops.register_dummy_fusion_op()