add qwen3

vllm-v0.6.2/tests/lora/test_lora_bias_e2e.py

@@ -0,0 +1,52 @@
+from typing import List
+
+import pytest
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "ibm-granite/granite-3b-code-base"
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
+    prompts = [
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]"  # noqa: E501
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0,
+                                          max_tokens=256,
+                                          stop=["[/assistant]"])
+    outputs = llm.generate(
+        prompts,
+        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:
+        generated_text = output.outputs[0].text
+        generated_texts.append(generated_text)
+    return generated_texts
+
+
+@pytest.mark.parametrize("lora_bias", [True])
+@pytest.mark.parametrize("fully_sharded", [True, False])
+def test_lora_bias(lora_bias_files: str, lora_bias: bool, fully_sharded: bool):
+    llm = vllm.LLM(MODEL_PATH,
+                   enable_lora=True,
+                   max_num_seqs=16,
+                   max_lora_rank=8,
+                   max_loras=1,
+                   enable_lora_bias=lora_bias,
+                   tensor_parallel_size=1,
+                   fully_sharded_loras=fully_sharded)
+
+    print("lora adapter created")
+    output1 = do_sample(llm, lora_bias_files, lora_id=0)
+
+    print("lora")
+    output2 = do_sample(llm, lora_bias_files, lora_id=1)
+
+    if lora_bias:
+        assert output1 != output2
+    else:
+        assert output1 == output2