60 lines
3.1 KiB
Python
60 lines
3.1 KiB
Python
from typing import List
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import vllm
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from vllm.lora.request import LoRARequest
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MODEL_PATH = "THUDM/chatglm3-6b"
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PROMPT_TEMPLATE = """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.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:""" # noqa: E501
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def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
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prompts = [
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PROMPT_TEMPLATE.format(query="How many singers do we have?"),
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PROMPT_TEMPLATE.format(
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query=
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"What is the average, minimum, and maximum age of all singers from France?" # noqa: E501
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),
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PROMPT_TEMPLATE.format(
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query=
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"Show name, country, age for all singers ordered by age from the oldest to the youngest." # noqa: E501
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),
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]
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print(prompts)
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sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)
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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)
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if lora_id else None)
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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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return generated_texts
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def test_chatglm3_lora(chatglm3_lora_files):
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llm = vllm.LLM(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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max_lora_rank=64,
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trust_remote_code=True)
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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'", # noqa: E501
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"SELECT name , country , age FROM singer ORDER BY age",
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]
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output1 = do_sample(llm, chatglm3_lora_files, lora_id=1)
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for i in range(len(expected_lora_output)):
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assert output1[i] == expected_lora_output[i]
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output2 = do_sample(llm, chatglm3_lora_files, lora_id=2)
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for i in range(len(expected_lora_output)):
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assert output2[i] == expected_lora_output[i]
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