[CI/Build][LoRA] Update Qwen35 LoRA testing (#37816)
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
This commit is contained in:
Jee Jee Li
@@ -8,7 +8,7 @@ steps:
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- vllm/lora
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- tests/lora
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commands:
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- 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
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- 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_densemodel_lora.py
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parallelism: 4
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@@ -31,4 +31,4 @@ steps:
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- pytest -v -s -x lora/test_llm_with_multi_loras.py
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- pytest -v -s -x lora/test_olmoe_tp.py
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- pytest -v -s -x lora/test_gptoss_tp.py
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- pytest -v -s -x lora/test_qwen35_densemoel_lora.py
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- pytest -v -s -x lora/test_qwen35_densemodel_lora.py
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@@ -295,10 +295,15 @@ def whisper_lora_files():
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@pytest.fixture(scope="session")
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def qwen35_dense_model_lora_files():
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def qwen35_text_lora_files():
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return snapshot_download(repo_id="jeeejeee/qwen35-4b-text-only-sql-lora")
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@pytest.fixture(scope="session")
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def qwen35_vl_lora_files():
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return snapshot_download(repo_id="jeeejeee/qwen35-4b-all-linear-pokemon-lora")
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@pytest.fixture
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def reset_default_device():
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"""
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361
tests/lora/test_qwen35_densemodel_lora.py
Normal file
361
tests/lora/test_qwen35_densemodel_lora.py
Normal file
@@ -0,0 +1,361 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers import AutoTokenizer
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import vllm
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import vllm.config
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from vllm.assets.image import ImageAsset
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from vllm.lora.request import LoRARequest
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from ..utils import create_new_process_for_each_test, multi_gpu_test
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MODEL_PATH = "Qwen/Qwen3.5-4B"
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TEXT_LORA_ID = 1
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VL_LORA_ID = 2
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# text-only task
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TEXT_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
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TEXT_EXPECTED_LORA_OUTPUT = [
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"SELECT count(*) FROM singer",
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"SELECT avg(age) , min(age) , max(age) FROM singer WHERE country = 'France'",
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"SELECT name FROM stadium WHERE stadium_id NOT IN (SELECT stadium_id FROM concert)",
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]
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# visual caption
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VL_QUESTION = "What is in the image?"
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VL_TEST_IMAGES = [
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ImageAsset("stop_sign"),
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ImageAsset("cherry_blossom"),
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]
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VL_EXPECTED_LORA_OUTPUT = [
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'A red STOP sign stands prominently in the foreground, with a traditional Chinese gate adorned with red lanterns and the Chinese characters "中華門" in the background, signaling the entrance to a Chinatown. A black car passes by on the street, and stone lion statues guard the entrance to the culturally rich area.', # noqa: E501
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"A vibrant blue sky serves as a backdrop for the iconic Tokyo Skytree, partially obscured by the delicate pink blossoms of cherry trees in full bloom.", # noqa: E501
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]
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TOKENIZER = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
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def _assert_exact_outputs(
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generated_texts: list[str], expected_outputs: list[str]
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) -> None:
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assert generated_texts == expected_outputs
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def _assert_prefix_outputs(
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generated_texts: list[str],
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expected_outputs: list[str],
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) -> None:
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assert len(generated_texts) == len(expected_outputs)
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for generated_text, expected_text in zip(generated_texts, expected_outputs):
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assert expected_text.startswith(generated_text), (
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f"Generated {generated_text!r} is not a prefix of expected "
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f"{expected_text!r}"
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)
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def _run_text_lora_sample(
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llm: vllm.LLM,
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lora_path: str,
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lora_id: int,
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) -> list[str]:
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prompts = [
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TEXT_PROMPT_TEMPLATE.format(query="How many singers do we have?"),
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TEXT_PROMPT_TEMPLATE.format(
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query=(
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"What is the average, minimum, and maximum "
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"age of all singers from France?"
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)
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),
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TEXT_PROMPT_TEMPLATE.format(
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query="What are the names of the stadiums without any concerts?"
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),
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]
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input_templates = []
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for prompt_text in prompts:
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messages = [{"role": "user", "content": prompt_text}]
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prompt = TOKENIZER.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False, # disable thinking
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)
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input_templates.append(prompt)
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outputs = llm.generate(
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input_templates,
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vllm.SamplingParams(temperature=0, max_tokens=512),
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lora_request=LoRARequest(str(lora_id), lora_id, lora_path),
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)
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generated_texts: list[str] = []
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for output in outputs:
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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: {output.prompt!r}, Generated text: {generated_text!r}")
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return generated_texts
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def _run_vl_lora_sample(
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llm: vllm.LLM,
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lora_path: str | None = None,
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lora_id: int = 0,
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) -> list[str]:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": VL_QUESTION},
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],
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}
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]
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prompt = TOKENIZER.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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prompts = [
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{
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"prompt": prompt,
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"multi_modal_data": {"image": asset.pil_image},
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}
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for asset in VL_TEST_IMAGES
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]
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outputs = llm.generate(
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prompts,
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vllm.SamplingParams(temperature=0, max_tokens=128),
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lora_request=(
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LoRARequest(str(lora_id), lora_id, lora_path)
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if lora_path is not None
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else None
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),
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)
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generated_texts: list[str] = []
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for output in outputs:
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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: {output.prompt!r}, Generated text: {generated_text!r}")
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return generated_texts
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def _build_text_prompts() -> list[str]:
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prompts = [
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TEXT_PROMPT_TEMPLATE.format(query="How many singers do we have?"),
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TEXT_PROMPT_TEMPLATE.format(
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query=(
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"What is the average, minimum, and maximum "
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"age of all singers from France?"
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)
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),
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TEXT_PROMPT_TEMPLATE.format(
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query="What are the names of the stadiums without any concerts?"
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),
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]
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input_templates = []
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for prompt_text in prompts:
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messages = [{"role": "user", "content": prompt_text}]
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prompt = TOKENIZER.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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input_templates.append(prompt)
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return input_templates
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def _build_vl_prompts() -> list[dict]:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": VL_QUESTION},
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],
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}
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]
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prompt = TOKENIZER.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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return [
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{
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"prompt": prompt,
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"multi_modal_data": {"image": asset.pil_image},
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}
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for asset in VL_TEST_IMAGES
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]
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def _run_mixed_lora_sample(
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llm: vllm.LLM,
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text_lora_path: str,
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vl_lora_path: str,
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text_lora_id: int,
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vl_lora_id: int,
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) -> list[str]:
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text_prompts = _build_text_prompts()[:2]
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vl_prompts = _build_vl_prompts()
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prompts = [
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text_prompts[0],
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vl_prompts[0],
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text_prompts[1],
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vl_prompts[1],
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]
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lora_requests = [
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LoRARequest("qwen35-text", text_lora_id, text_lora_path),
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LoRARequest("qwen35-vl", vl_lora_id, vl_lora_path),
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LoRARequest("qwen35-text", text_lora_id, text_lora_path),
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LoRARequest("qwen35-vl", vl_lora_id, vl_lora_path),
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]
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outputs = llm.generate(
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prompts,
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vllm.SamplingParams(temperature=0, max_tokens=256),
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lora_request=lora_requests,
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)
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generated_texts: list[str] = []
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for output in outputs:
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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: {output.prompt!r}, Generated text: {generated_text!r}")
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return generated_texts
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def _run_mixed_lora_and_base_sample(
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llm: vllm.LLM,
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text_lora_path: str,
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vl_lora_path: str,
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text_lora_id: int,
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vl_lora_id: int,
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) -> list[str]:
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text_prompt = _build_text_prompts()[0]
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vl_prompt = _build_vl_prompts()[0]
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prompts = [
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text_prompt,
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vl_prompt,
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text_prompt,
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vl_prompt,
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]
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lora_requests = [
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LoRARequest("qwen35-text", text_lora_id, text_lora_path),
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LoRARequest("qwen35-vl", vl_lora_id, vl_lora_path),
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None,
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None,
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]
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outputs = llm.generate(
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prompts,
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vllm.SamplingParams(temperature=0, max_tokens=256),
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lora_request=lora_requests,
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)
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generated_texts: list[str] = []
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for output in outputs:
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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: {output.prompt!r}, Generated text: {generated_text!r}")
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return generated_texts
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def _assert_qwen35_text_vl_and_mixed_lora(
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llm: vllm.LLM,
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qwen35_text_lora_files: str,
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qwen35_vl_lora_files: str,
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) -> None:
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generated_texts = _run_text_lora_sample(
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llm,
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qwen35_text_lora_files,
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TEXT_LORA_ID,
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)
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_assert_exact_outputs(generated_texts, TEXT_EXPECTED_LORA_OUTPUT)
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generated_texts = _run_vl_lora_sample(
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llm,
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qwen35_vl_lora_files,
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VL_LORA_ID,
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)
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_assert_prefix_outputs(generated_texts, VL_EXPECTED_LORA_OUTPUT)
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generated_texts = _run_mixed_lora_sample(
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llm,
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qwen35_text_lora_files,
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qwen35_vl_lora_files,
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text_lora_id=TEXT_LORA_ID,
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vl_lora_id=VL_LORA_ID,
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)
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assert generated_texts[0] == TEXT_EXPECTED_LORA_OUTPUT[0]
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assert generated_texts[2] == TEXT_EXPECTED_LORA_OUTPUT[1]
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_assert_prefix_outputs([generated_texts[1]], [VL_EXPECTED_LORA_OUTPUT[0]])
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_assert_prefix_outputs([generated_texts[3]], [VL_EXPECTED_LORA_OUTPUT[1]])
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generated_texts = _run_mixed_lora_and_base_sample(
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llm,
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qwen35_text_lora_files,
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qwen35_vl_lora_files,
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text_lora_id=TEXT_LORA_ID,
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vl_lora_id=VL_LORA_ID,
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)
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assert generated_texts[0] == TEXT_EXPECTED_LORA_OUTPUT[0]
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_assert_prefix_outputs([generated_texts[1]], [VL_EXPECTED_LORA_OUTPUT[0]])
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assert generated_texts[2] != TEXT_EXPECTED_LORA_OUTPUT[0]
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assert not VL_EXPECTED_LORA_OUTPUT[0].startswith(generated_texts[3]), (
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"Non-LoRA vision output unexpectedly matches the LoRA expectation."
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)
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@create_new_process_for_each_test()
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def test_qwen35_text_lora(qwen35_text_lora_files, qwen35_vl_lora_files):
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llm = vllm.LLM(
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model=MODEL_PATH,
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max_model_len=4096,
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enable_lora=True,
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max_loras=2,
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max_num_seqs=4,
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max_lora_rank=8,
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enforce_eager=True,
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trust_remote_code=True,
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enable_tower_connector_lora=True,
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mm_processor_cache_gb=0,
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limit_mm_per_prompt={"image": 1},
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)
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_assert_qwen35_text_vl_and_mixed_lora(
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llm,
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qwen35_text_lora_files,
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qwen35_vl_lora_files,
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)
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@multi_gpu_test(num_gpus=4)
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def test_qwen35_text_lora_tp4(qwen35_text_lora_files, qwen35_vl_lora_files):
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llm = vllm.LLM(
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model=MODEL_PATH,
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max_model_len=4096,
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enable_lora=True,
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max_loras=2,
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max_lora_rank=8,
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max_num_seqs=4,
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enforce_eager=True,
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tensor_parallel_size=4,
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trust_remote_code=True,
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enable_tower_connector_lora=True,
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mm_processor_cache_gb=0,
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limit_mm_per_prompt={"image": 1},
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compilation_config=vllm.config.CompilationConfig(
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cudagraph_specialize_lora=False,
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),
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)
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|
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_assert_qwen35_text_vl_and_mixed_lora(
|
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llm,
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qwen35_text_lora_files,
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qwen35_vl_lora_files,
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)
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@@ -1,132 +0,0 @@
|
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# SPDX-License-Identifier: Apache-2.0
|
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from transformers import AutoTokenizer
|
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|
||||
import vllm
|
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import vllm.config
|
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from vllm.lora.request import LoRARequest
|
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|
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from ..utils import create_new_process_for_each_test, multi_gpu_test
|
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MODEL_PATH = "Qwen/Qwen3.5-4B"
|
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|
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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
|
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|
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EXPECTED_LORA_OUTPUT = [
|
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"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)",
|
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]
|
||||
|
||||
|
||||
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]
|
||||
Reference in New Issue
Block a user