[Bugfix][LoRA] Fix Qwen35 LoRA (#36976)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

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]