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116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# NOTE To avoid overloading the CI pipeline, this test script will not
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# be triggered on CI and is primarily intended for local testing and verification.
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import vllm
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from vllm.lora.request import LoRARequest
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from ..utils import multi_gpu_test
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MODEL_PATH = "Qwen/Qwen3-30B-A3B"
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PROMPT_TEMPLATE = """<|im_start|>user
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I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.
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"
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##Instruction:
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candidate_poll contains tables such as candidate, people. Table candidate has columns such as Candidate_ID, People_ID, Poll_Source, Date, Support_rate, Consider_rate, Oppose_rate, Unsure_rate. Candidate_ID is the primary key.
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Table people has columns such as People_ID, Sex, Name, Date_of_Birth, Height, Weight. People_ID is the primary key.
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The People_ID of candidate is the foreign key of People_ID of people.
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###Input:
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{context}
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###Response:<|im_end|>
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<|im_start|>assistant""" # noqa: E501
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EXPECTED_LORA_OUTPUT = [
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"<think>\n\n</think>\n\nSELECT count(*) FROM candidate",
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"<think>\n\n</think>\n\nSELECT count(*) FROM candidate",
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"<think>\n\n</think>\n\nSELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1", # noqa: E501
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"<think>\n\n</think>\n\nSELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1", # noqa: E501
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]
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def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int) -> None:
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prompts = [
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PROMPT_TEMPLATE.format(context="How many candidates are there?"),
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PROMPT_TEMPLATE.format(context="Count the number of candidates."),
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PROMPT_TEMPLATE.format(
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context="Which poll resource provided the most number of candidate information?" # noqa: E501
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),
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PROMPT_TEMPLATE.format(
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context="Return the poll resource associated with the most candidates."
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),
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]
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sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
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outputs = llm.generate(
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prompts,
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sampling_params,
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lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
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)
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# Print the outputs.
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generated_texts: list[str] = []
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text.strip()
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generated_texts.append(generated_text)
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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for i in range(len(EXPECTED_LORA_OUTPUT)):
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assert generated_texts[i].startswith(EXPECTED_LORA_OUTPUT[i])
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def test_qwen3moe_lora(qwen3moe_lora_files):
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# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
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# Otherwise, the lora-test will fail due to CUDA OOM.
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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enable_chunked_prefill=True,
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)
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generate_and_test(llm, qwen3moe_lora_files, lora_id=1)
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generate_and_test(llm, qwen3moe_lora_files, lora_id=2)
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@multi_gpu_test(num_gpus=2)
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def test_qwen3moe_lora_tp2(qwen3moe_lora_files):
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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enable_chunked_prefill=True,
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tensor_parallel_size=2,
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)
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generate_and_test(llm, qwen3moe_lora_files, lora_id=1)
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generate_and_test(llm, qwen3moe_lora_files, lora_id=2)
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@multi_gpu_test(num_gpus=4)
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def test_qwen3moe_lora_tp4(qwen3moe_lora_files):
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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enable_chunked_prefill=True,
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tensor_parallel_size=4,
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)
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generate_and_test(llm, qwen3moe_lora_files, lora_id=1)
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generate_and_test(llm, qwen3moe_lora_files, lora_id=2)
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