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https://github.com/vllm-project/vllm.git
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52c2a8d4ad
Signed-off-by: Woosuk Kwon <woosuk@thinkingmachines.ai> Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import weakref
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import pytest
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from vllm import LLM, SamplingParams
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from vllm.distributed import cleanup_dist_env_and_memory
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MODEL_NAME = "distilbert/distilgpt2"
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PROMPTS = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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TOKEN_IDS = [
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[0],
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[0, 1],
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[0, 2, 1],
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[0, 3, 1, 2],
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]
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@pytest.fixture(scope="module")
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def llm():
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# pytest caches the fixture so we use weakref.proxy to
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# enable garbage collection
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llm = LLM(model=MODEL_NAME,
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max_num_batched_tokens=4096,
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tensor_parallel_size=1,
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gpu_memory_utilization=0.10,
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enforce_eager=True)
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yield weakref.proxy(llm)
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del llm
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cleanup_dist_env_and_memory()
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@pytest.mark.skip_global_cleanup
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def test_multiple_sampling_params(llm: LLM):
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sampling_params = [
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SamplingParams(temperature=0.01, top_p=0.95),
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SamplingParams(temperature=0.3, top_p=0.95),
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SamplingParams(temperature=0.7, top_p=0.95),
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SamplingParams(temperature=0.99, top_p=0.95),
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]
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# Multiple SamplingParams should be matched with each prompt
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outputs = llm.generate(PROMPTS, sampling_params=sampling_params)
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assert len(PROMPTS) == len(outputs)
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# Exception raised, if the size of params does not match the size of prompts
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with pytest.raises(ValueError):
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outputs = llm.generate(PROMPTS, sampling_params=sampling_params[:3])
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# Single SamplingParams should be applied to every prompt
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single_sampling_params = SamplingParams(temperature=0.3, top_p=0.95)
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outputs = llm.generate(PROMPTS, sampling_params=single_sampling_params)
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assert len(PROMPTS) == len(outputs)
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# sampling_params is None, default params should be applied
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outputs = llm.generate(PROMPTS, sampling_params=None)
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assert len(PROMPTS) == len(outputs)
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def test_max_model_len():
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max_model_len = 20
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llm = LLM(
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model=MODEL_NAME,
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max_model_len=max_model_len,
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gpu_memory_utilization=0.10,
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enforce_eager=True, # reduce test time
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)
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sampling_params = SamplingParams(max_tokens=max_model_len + 10)
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outputs = llm.generate(PROMPTS, sampling_params)
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for output in outputs:
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num_total_tokens = len(output.prompt_token_ids) + len(
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output.outputs[0].token_ids)
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# Total tokens must not exceed max_model_len.
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# It can be less if generation finishes due to other reasons (e.g., EOS)
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# before reaching the absolute model length limit.
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assert num_total_tokens <= max_model_len
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