# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import subprocess import sys import pytest import vllm import vllm.config from vllm import LLM from vllm.lora.request import LoRARequest from vllm.model_executor.model_loader.tensorizer import TensorizerConfig from ..utils import VLLM_PATH, create_new_process_for_each_test, multi_gpu_test MODEL_PATH = "meta-llama/Llama-2-7b-hf" EXPECTED_LORA_OUTPUT = [ " SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ", # noqa: E501 " SELECT nationality FROM table_name_11 WHERE elector = 'anchero pantaleone' ", " SELECT one_mora FROM table_name_95 WHERE gloss = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] AND accented_mora = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] ", # noqa: E501 " SELECT sex FROM people WHERE people_id IN (SELECT people_id FROM candidate GROUP BY sex ORDER BY COUNT(people_id) DESC LIMIT 1) ", # noqa: E501 " SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ", " SELECT womens_doubles FROM table_28138035_4 WHERE mens_singles = 'Werner Schlager' ", # noqa: E501 ] def do_sample( llm: vllm.LLM, lora_path: str, lora_id: int, tensorizer_config_dict: dict | None = None, ) -> list[str]: prompts = [ "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_74 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]", # noqa: E501 "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? [/user] [assistant]", # noqa: E501 "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_95 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a low tone mora with a gloss of /˩okiru/ [òkìɽɯ́]? [/user] [assistant]", # noqa: E501 "[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_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? [/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, skip_special_tokens=False, stop=["[/assistant]"] ) if tensorizer_config_dict is not None: outputs = llm.generate( prompts, sampling_params, lora_request=LoRARequest( str(lora_id), lora_id, lora_path, tensorizer_config_dict=tensorizer_config_dict, ) if lora_id else None, ) else: outputs = llm.generate( prompts, sampling_params, lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None, ) # Print the outputs. generated_texts: list[str] = [] for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text generated_texts.append(generated_text) print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") return generated_texts def generate_and_test(llm, sql_lora_files, tensorizer_config_dict: dict | None = None): print("lora adapter created") print("lora 1") assert ( do_sample( llm, sql_lora_files, tensorizer_config_dict=tensorizer_config_dict, lora_id=1, ) == EXPECTED_LORA_OUTPUT ) print("lora 2") assert ( do_sample( llm, sql_lora_files, tensorizer_config_dict=tensorizer_config_dict, lora_id=2, ) == EXPECTED_LORA_OUTPUT ) print("removing lora") @create_new_process_for_each_test() @pytest.mark.parametrize("cudagraph_specialize_lora", [True, False]) def test_llama_lora(sql_lora_files, cudagraph_specialize_lora: bool): llm = vllm.LLM( MODEL_PATH, tokenizer=sql_lora_files, enable_lora=True, # also test odd max_num_seqs max_num_seqs=13, max_loras=4, compilation_config=vllm.config.CompilationConfig( cudagraph_specialize_lora=cudagraph_specialize_lora, ), ) generate_and_test(llm, sql_lora_files) @multi_gpu_test(num_gpus=4) def test_llama_lora_tp4(sql_lora_files): llm = vllm.LLM( MODEL_PATH, tokenizer=sql_lora_files, enable_lora=True, max_num_seqs=16, max_loras=4, tensor_parallel_size=4, ) generate_and_test(llm, sql_lora_files) @multi_gpu_test(num_gpus=4) def test_llama_lora_tp4_fully_sharded_loras(sql_lora_files): llm = vllm.LLM( MODEL_PATH, tokenizer=sql_lora_files, enable_lora=True, max_num_seqs=16, max_loras=4, tensor_parallel_size=4, fully_sharded_loras=True, ) generate_and_test(llm, sql_lora_files) @multi_gpu_test(num_gpus=2) def test_tp2_serialize_and_deserialize_lora( tmp_path, sql_lora_files, sql_lora_huggingface_id ): # Run the tensorizing of the LoRA adapter and the model in a subprocess # to guarantee cleanup tp_size = 2 model_name = "model-rank-%03d.tensors" model_ref = MODEL_PATH lora_path = sql_lora_huggingface_id suffix = "test" try: result = subprocess.run( [ sys.executable, f"{VLLM_PATH}/examples/others/tensorize_vllm_model.py", "--model", MODEL_PATH, "--lora-path", lora_path, "--tensor-parallel-size", str(tp_size), "serialize", "--serialized-directory", str(tmp_path), "--suffix", suffix, "--serialization-kwargs", '{"limit_cpu_concurrency": 4}', ], check=True, capture_output=True, text=True, ) except subprocess.CalledProcessError as e: print("Tensorizing failed.") print("STDOUT:\n", e.stdout) print("STDERR:\n", e.stderr) raise print("STDOUT:\n", result.stdout) model_uri = tmp_path / "vllm" / model_ref / suffix / model_name tensorizer_config = TensorizerConfig(tensorizer_uri=str(model_uri)) loaded_llm = LLM( model=model_ref, tokenizer=sql_lora_files, load_format="tensorizer", enable_lora=True, enforce_eager=True, model_loader_extra_config=tensorizer_config, max_num_seqs=13, tensor_parallel_size=2, max_loras=2, ) tc_as_dict = tensorizer_config.to_serializable() print("lora adapter created") print("lora 1") assert ( do_sample( loaded_llm, sql_lora_files, tensorizer_config_dict=tc_as_dict, lora_id=1 ) == EXPECTED_LORA_OUTPUT )