[main][Bugfix] Fix unable to load qwen3_moe quantized weights (#2219)

### What this PR does / why we need it?

Fixes unable to load `qwen3_moe` quantized weights issue due to #1994

### Does this PR introduce _any_ user-facing change?

None

### How was this patch tested?

Add a `qwen3_moe` W8A8 quantized model in
`tests/e2e/multicard/test_qwen3_moe.py`

- vLLM version: v0.10.0
- vLLM main:
c494f96fbc

---------

Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>

tests/e2e/multicard/test_qwen3_moe.py

@@ -18,9 +18,11 @@
 #
 """Compare the short outputs of HF and vLLM when using greedy sampling.
 
-Run `pytest tests/test_offline_inference.py`.
+Run `pytest tests/e2e/multicard/test_qwen3_moe.py`.
 """
 
+from modelscope import snapshot_download  # type: ignore
+
 from tests.e2e.conftest import VllmRunner
 
 
@@ -53,3 +55,20 @@ def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
             distributed_executor_backend="mp",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_Qwen3_MOE_W8A8():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    dtype = "auto"
+    max_tokens = 5
+    with VllmRunner(
+            snapshot_download("vllm-ascend/Qwen3-30B-A3B-W8A8"),
+            max_model_len=8192,
+            dtype=dtype,
+            tensor_parallel_size=2,
+            quantization="ascend",
+            enforce_eager=False,
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)

vllm_ascend/models/qwen3_moe.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2024 The Qwen team.
 # Copyright 2023 The vLLM team.
-#
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -26,20 +27,23 @@ from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
                                              get_tp_group)
 from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe.layer import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.models.interfaces import (MixtureOfExperts,
+                                                   SupportsLoRA, SupportsPP)
 from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention,
                                                   Qwen3MoeDecoderLayer,
                                                   Qwen3MoeForCausalLM,
                                                   Qwen3MoeMLP, Qwen3MoeModel,
                                                   Qwen3MoeSparseMoeBlock)
 from vllm.model_executor.models.utils import (
-    extract_layer_index, make_empty_intermediate_tensors_factory, make_layers,
-    maybe_prefix)
+    PPMissingLayer, extract_layer_index,
+    make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
 
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
 
@@ -230,6 +234,9 @@ class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         nn.Module.__init__(self)
+        SupportsPP.__init__(self)
+        SupportsLoRA.__init__(self)
+        MixtureOfExperts.__init__(self)
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         self.config = config
@@ -238,9 +245,31 @@ class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
                                          prefix=maybe_prefix(prefix, "model"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
-                                      quant_config=quant_config)
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
+
+        # Set MoE hyperparameters
+        self.expert_weights: list[torch.Tensor] = []
+
+        self.moe_layers: list[FusedMoE] = []
+        example_layer = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, Qwen3MoeDecoderLayer)
+            if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
+                example_layer = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_layer is None:
+            raise RuntimeError("No Qwen3MoE layer found in the model.layers.")
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0