70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
"""Compare the outputs of HF and vLLM when using greedy sampling.
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This test only tests small models. Big models such as 7B should be tested from
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test_big_models.py because it could use a larger instance to run tests.
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Run `pytest tests/models/test_models.py`.
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"""
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import pytest
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from vllm.platforms import current_platform
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from ...utils import check_logprobs_close
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MODELS = [
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"facebook/opt-125m", # opt
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"openai-community/gpt2", # gpt2
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# "Milos/slovak-gpt-j-405M", # gptj
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# "bigcode/tiny_starcoder_py", # gpt_bigcode
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# "EleutherAI/pythia-70m", # gpt_neox
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"bigscience/bloom-560m", # bloom - testing alibi slopes
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"microsoft/phi-2", # phi
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# "stabilityai/stablelm-3b-4e1t", # stablelm
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# "bigcode/starcoder2-3b", # starcoder2
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"google/gemma-1.1-2b-it", # gemma
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"Qwen/Qwen2.5-0.5B-Instruct", # qwen2
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"meta-llama/Llama-3.2-1B-Instruct", # llama
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]
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if not current_platform.is_cpu():
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MODELS += [
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# fused_moe which not supported on CPU
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"openbmb/MiniCPM3-4B",
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]
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target_dtype = "half"
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", [target_dtype])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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with hf_runner(model, dtype=dtype) as hf_model:
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hf_outputs = hf_model.generate_greedy_logprobs_limit(
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example_prompts, max_tokens, num_logprobs)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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# This test is for verifying whether the model's extra_repr
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# can be printed correctly.
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print(vllm_model.model.llm_engine.model_executor.driver_worker.
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model_runner.model)
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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