[Bugfix][LoRA] Fix Qwen35 LoRA

.buildkite/test_areas/lora.yaml

@@ -8,7 +8,7 @@ steps:
   - vllm/lora
   - tests/lora
   commands:
-    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py
+    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemoel_lora.py 
   parallelism: 4
 
 
@@ -30,4 +30,5 @@ steps:
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
     - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
+    - pytest -v -s -x lora/test_gptoss_tp.py
+    - pytest -v -s -x lora/test_qwen35_densemoel_lora.py

tests/lora/conftest.py

@@ -294,6 +294,11 @@ def whisper_lora_files():
     return snapshot_download(repo_id="chengyili2005/whisper-small-mandarin-lora")
 
 
+@pytest.fixture(scope="session")
+def qwen35_dense_model_lora_files():
+    return snapshot_download(repo_id="jeeejeee/qwen35-4b-text-only-sql-lora")
+
+
 @pytest.fixture
 def reset_default_device():
     """

tests/lora/test_qwen35_densemoel_lora.py

@@ -0,0 +1,132 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers import AutoTokenizer
+
+import vllm
+import vllm.config
+from vllm.lora.request import LoRARequest
+
+from ..utils import create_new_process_for_each_test, multi_gpu_test
+
+MODEL_PATH = "Qwen/Qwen3.5-4B"
+
+PROMPT_TEMPLATE = """Write a SQL query for the given database.\nSchema:\nTables:\n  - stadium(Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average)\n  - singer(Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male)\n  - concert(concert_ID, concert_Name, Theme, Stadium_ID, Year)\n  - singer_in_concert(concert_ID, Singer_ID)\n\nQuestion:\n{query}"""  # noqa: E501
+
+EXPECTED_LORA_OUTPUT = [
+    "SELECT count(*) FROM singer",
+    "SELECT avg(age) ,  min(age) ,  max(age) FROM singer WHERE country  =  'France'",
+    "SELECT name FROM stadium WHERE stadium_id NOT IN (SELECT stadium_id FROM concert)",
+]
+
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
+    prompts = [
+        PROMPT_TEMPLATE.format(query="How many singers do we have?"),
+        PROMPT_TEMPLATE.format(
+            query=(
+                "What is the average, minimum, and maximum "
+                "age of all singers from France?"
+            )
+        ),
+        PROMPT_TEMPLATE.format(
+            query=("What are the names of the stadiums without any concerts?")
+        ),
+    ]
+    input_templates = []
+    for prmpt in prompts:
+        messages = [{"role": "user", "content": prmpt}]
+        prompt = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=False,  # disable thinking
+        )
+        input_templates.append(prompt)
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=512)
+    outputs = llm.generate(
+        input_templates,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
+    )
+
+    generated_texts: list[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+@create_new_process_for_each_test()
+def test_qwen35_dense_model_lora(qwen35_dense_model_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=512,
+        enable_lora=True,
+        max_loras=2,
+        max_num_seqs=16,
+        max_lora_rank=8,
+        trust_remote_code=True,
+    )
+
+    output1 = do_sample(llm, qwen35_dense_model_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, qwen35_dense_model_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]
+
+
+@multi_gpu_test(num_gpus=4)
+def test_qwen35_dense_model_lora_tp4(qwen35_dense_model_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=2,
+        max_lora_rank=8,
+        max_num_seqs=16,
+        tensor_parallel_size=4,
+        trust_remote_code=True,
+        fully_sharded_loras=False,
+        compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
+            cudagraph_specialize_lora=False,
+        ),
+    )
+
+    output1 = do_sample(llm, qwen35_dense_model_lora_files, lora_id=1)
+    print(output1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, qwen35_dense_model_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]
+
+
+@multi_gpu_test(num_gpus=4)
+def test_qwen35_dense_model_lora_tp4_fully_sharded_loras(qwen35_dense_model_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=512,
+        enable_lora=True,
+        max_loras=2,
+        max_lora_rank=8,
+        tensor_parallel_size=4,
+        trust_remote_code=True,
+        fully_sharded_loras=True,
+        gpu_memory_utilization=0.8,
+        compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
+            cudagraph_specialize_lora=False,
+        ),
+    )
+    output1 = do_sample(llm, qwen35_dense_model_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, qwen35_dense_model_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]

vllm/model_executor/models/qwen3_5.py

@@ -32,17 +32,18 @@
 from torch import nn
 
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import (
-    VllmConfig,
-)
+from vllm.config import VllmConfig
 from vllm.distributed import (
     get_pp_group,
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import (
     GemmaRMSNorm as Qwen3_5RMSNorm,
 )
-from vllm.model_executor.layers.linear import MergedColumnParallelLinear
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.mamba.mamba_utils import (
     MambaStateCopyFunc,
@@ -130,6 +131,40 @@ def fix_query_key_value_ordering(
             "Qwen3.5 Series dont need to fix query key value ordering"
         )
 
+    def __init__(
+        self,
+        config: Qwen3_5Config,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        create_in_proj_qkvz = vllm_config.lora_config is None
+        super().__init__(
+            config,
+            vllm_config=vllm_config,
+            prefix=prefix,
+            create_in_proj_qkvz=create_in_proj_qkvz,
+        )
+        if vllm_config.lora_config is not None:
+            # Separate in_proj_qkv (Q,K,V) and in_proj_z for LoRA compatibility.
+            # Use MergedColumnParallelLinear for in_proj_qkv because GDN can have
+            # linear_num_key_heads != linear_num_value_heads (e.g. 16 vs 32), so
+            # output sizes [key_dim, key_dim, value_dim] are not representable
+            # with a single QKVParallelLinear (which ties K and V head counts).
+            self.in_proj_qkv = MergedColumnParallelLinear(
+                input_size=self.hidden_size,
+                output_sizes=[self.key_dim, self.key_dim, self.value_dim],
+                bias=False,
+                quant_config=vllm_config.quant_config,
+                prefix=f"{prefix}.in_proj_qkv",
+            )
+            self.in_proj_z = ColumnParallelLinear(
+                input_size=self.hidden_size,
+                output_size=self.value_dim,
+                bias=False,
+                quant_config=vllm_config.quant_config,
+                prefix=f"{prefix}.in_proj_z",
+            )
+
     def create_qkvz_proj(
         self,
         hidden_size: int,
@@ -180,15 +215,21 @@ def forward(
         # ============================================================
         # Part 1: Input Projection
         # ============================================================
-        mixed_qkvz, ba = torch.ops.vllm.gdn_in_proj(
-            hidden_states,
-            sum(self.in_proj_qkvz.output_sizes) // self.tp_size,
-            sum(self.in_proj_ba.output_sizes) // self.tp_size,
-            self.prefix,
-        )
-        qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
-        z_size = self.value_dim // self.tp_size
-        mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
+        if hasattr(self, "in_proj_qkv"):
+            # LoRA path: separate in_proj_qkv and in_proj_z
+            mixed_qkv, _ = self.in_proj_qkv(hidden_states)
+            ba, _ = self.in_proj_ba(hidden_states)
+            z, _ = self.in_proj_z(hidden_states)
+        else:
+            mixed_qkvz, ba = torch.ops.vllm.gdn_in_proj(
+                hidden_states,
+                sum(self.in_proj_qkvz.output_sizes) // self.tp_size,
+                sum(self.in_proj_ba.output_sizes) // self.tp_size,
+                self.prefix,
+            )
+            qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
+            z_size = self.value_dim // self.tp_size
+            mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
         z = z.reshape(z.size(0), -1, self.head_v_dim)
         b, a = ba.chunk(2, dim=-1)
 
@@ -240,18 +281,14 @@ def __init__(
         model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-        speculative_config = vllm_config.speculative_config
 
         self.layer_type = layer_type
         self.layer_idx = extract_layer_index(prefix)
 
         if self.layer_type == "linear_attention":
             self.linear_attn = Qwen3_5GatedDeltaNet(
-                config,
-                model_config=model_config,
-                cache_config=cache_config,
-                quant_config=quant_config,
-                speculative_config=speculative_config,
+                config=config,
+                vllm_config=vllm_config,
                 prefix=f"{prefix}.linear_attn",
             )
         elif self.layer_type == "full_attention":
@@ -331,6 +368,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.num_redundant_experts = eplb_config.num_redundant_experts
 
         self.config = config
+        self.enable_lora = vllm_config.lora_config is not None
 
         self.vocab_size = config.vocab_size
 
@@ -396,13 +434,25 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             # mlp
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
-            # GDN
-            ("in_proj_qkvz", "in_proj_qkv", (0, 1, 2)),
-            ("in_proj_qkvz", "in_proj_z", 3),
             ("in_proj_ba", "in_proj_b", 0),
             ("in_proj_ba", "in_proj_a", 1),
         ]
 
+        if self.enable_lora:
+            stacked_params_mapping.extend(
+                [
+                    ("in_proj_qkv", "in_proj_qkv", (0, 1, 2)),
+                    ("in_proj_z", "in_proj_z", 0),
+                ]
+            )
+        else:
+            stacked_params_mapping.extend(
+                [
+                    ("in_proj_qkvz", "in_proj_qkv", (0, 1, 2)),
+                    ("in_proj_qkvz", "in_proj_z", 3),
+                ]
+            )
+
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         expert_params_mapping = self.get_expert_mapping()
@@ -450,7 +500,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                     continue
                 param = params_dict[name]
                 weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                if param_name == "in_proj_z" and self.enable_lora:
+                    weight_loader(param, loaded_weight)
+                else:
+                    weight_loader(param, loaded_weight, shard_id)
                 break
             else:
                 is_expert_weight = False
@@ -580,6 +633,15 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
         )
 
+        # When LoRA is enabled, GDN uses separate in_proj_qkv and in_proj_z
+        # instead of merged in_proj_qkvz; pack mapping must match.
+        if vllm_config.lora_config:
+            base = getattr(Qwen3_5ForCausalLMBase, "packed_modules_mapping", {})
+            self.packed_modules_mapping = {k: list(v) for k, v in base.items()}
+            self.packed_modules_mapping.pop("in_proj_qkvz", None)
+            self.packed_modules_mapping["in_proj_qkv"] = ["in_proj_qkv"]
+            self.packed_modules_mapping["in_proj_z"] = ["in_proj_z"]
+
         if get_pp_group().is_last_rank:
             if config.tie_word_embeddings:
                 self.lm_head = self.model.embed_tokens
@@ -672,6 +734,7 @@ class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration, IsHybrid)
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
         # protocols have not __init__ method, so we need to use nn.Module.__init__
         nn.Module.__init__(self)
+        self.update_packed_mapping(enable_lora=vllm_config.lora_config is not None)
         config: Qwen3_5Config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
@@ -699,6 +762,16 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
             self.language_model.make_empty_intermediate_tensors
         )
 
+    def update_packed_mapping(self, enable_lora: bool):
+        # When LoRA is enabled, GDN uses separate in_proj_qkv and in_proj_z
+        if enable_lora:
+            base = getattr(
+                Qwen3_5ForConditionalGeneration, "packed_modules_mapping", {}
+            )
+            self.packed_modules_mapping = {k: list(v) for k, v in base.items()}
+            self.packed_modules_mapping.pop("in_proj_qkvz", None)
+            self.packed_modules_mapping["in_proj_qkv"] = ["in_proj_qkv"]
+
     def embed_input_ids(
         self,
         input_ids: torch.Tensor,
@@ -879,9 +952,13 @@ def set_moe_parameters(self):
 class Qwen3_5MoeForConditionalGeneration(
     Qwen3_5ForConditionalGeneration, Qwen3_5_MoeMixtureOfExperts
 ):
+    # For MoE LoRA weights loading
+    is_3d_moe_weight: bool = True
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
         # protocols have not __init__ method, so we need to use nn.Module.__init__
         nn.Module.__init__(self)
+        self.update_packed_mapping(enable_lora=vllm_config.lora_config is not None)
         config: Qwen3_5MoeConfig = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config