47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
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import vllm
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from vllm.lora.request import LoRARequest
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MODEL_PATH = "google/gemma-7b"
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def do_sample(llm, lora_path: str, lora_id: int) -> str:
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prompts = [
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"Quote: Imagination is",
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"Quote: Be yourself;",
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"Quote: So many books,",
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]
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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 = []
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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_gemma_lora(gemma_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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expected_lora_output = [
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"more important than knowledge.\nAuthor: Albert Einstein\n",
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"everyone else is already taken.\nAuthor: Oscar Wilde\n",
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"so little time\nAuthor: Frank Zappa\n",
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]
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output1 = do_sample(llm, gemma_lora_files, lora_id=1)
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for i in range(len(expected_lora_output)):
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assert output1[i].startswith(expected_lora_output[i])
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output2 = do_sample(llm, gemma_lora_files, lora_id=2)
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for i in range(len(expected_lora_output)):
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assert output2[i].startswith(expected_lora_output[i])
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