def test_multiple_lora_requests():
    llm = LLM(
        model=MODEL_PATH,
        enable_lora=True,
        max_loras=4,
        max_lora_rank=LORA_RANK,
        max_model_len=512,
        gpu_memory_utilization=0.5,
        enforce_eager=True,
    )
    # Use a prompt that triggers the self-cognition of the LoRAs.
    PROMPTS = ["Hi, tell me about you"] * 2

    # Use two different LoRAs to ensure they are applied correctly.
    lora_request = [
        LoRARequest("Alice", 1, LORA_NAME_PATH_MAP["Alice"]),
        LoRARequest("Bob", 2, LORA_NAME_PATH_MAP["Bob"]),
    ]

    sampling_params = SamplingParams(temperature=0, max_tokens=64)

    # Multiple LoRARequests should be matched with each prompt.
    outputs = llm.generate(PROMPTS, sampling_params, lora_request=lora_request)
    assert len(PROMPTS) == len(outputs)

    # Check that the correct LoRA was applied to each prompt.
    output_text_1 = outputs[0].outputs[0].text
    output_text_2 = outputs[1].outputs[0].text
    assert "Alice" in output_text_1
    assert "Bob" in output_text_2

    # Exception raised, if the size of lora_request does not match the size of prompts.
    with pytest.raises(ValueError):
        llm.generate(PROMPTS, sampling_params, lora_request=lora_request[:1])

    # Single LoRARequest should be applied to every prompt.
    single_lora_request = lora_request[0]
    outputs = llm.generate(PROMPTS,
                           sampling_params,
                           lora_request=single_lora_request)
    assert len(PROMPTS) == len(outputs)
    assert "Alice" in outputs[0].outputs[0].text
    assert "Alice" in outputs[1].outputs[0].text