68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import List
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import pytest
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import vllm
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from vllm.lora.request import LoRARequest
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from vllm.platforms import current_platform
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MODEL_PATH = "google/gemma-7b"
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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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"Quote: Imagination is",
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"Quote: Be yourself;",
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"Quote: Painting is poetry that is seen rather than felt,",
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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: 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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@pytest.fixture(autouse=True)
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def v1(run_with_both_engines_lora):
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# Simple autouse wrapper to run both engines for each test
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# This can be promoted up to conftest.py to run for every
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# test in a package
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pass
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# The V1 lora test for this model requires more than 24GB.
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@pytest.mark.skip_v1
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@pytest.mark.xfail(current_platform.is_rocm(),
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reason="There can be output mismatch on ROCm")
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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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enable_chunked_prefill=True)
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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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"and poetry is painting that is felt rather than seen.\n"
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"Author: Leonardo da Vinci\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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