d4d93db2c5
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Nicolò Lucchesi <nlucches@redhat.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Michael Goin <michael@neuralmagic.com>
36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import pytest
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from vllm.entrypoints.llm import LLM
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from vllm.sampling_params import SamplingParams
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@pytest.mark.skip_v1
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@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
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def test_computed_prefix_blocks(model: str):
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# This test checks if the engine generates completions both with and
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# without optional detokenization, that detokenization includes text
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# and no-detokenization doesn't, and that both completions have the same
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# token_ids.
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prompt = (
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"You are a helpful assistant. How do I build a car from cardboard and "
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"paper clips? Is there an easy to follow video tutorial available "
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"online for free?")
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llm = LLM(model=model)
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sampling_params = SamplingParams(max_tokens=10,
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temperature=0.0,
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detokenize=False)
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outputs_no_detokenization = llm.generate(prompt,
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sampling_params)[0].outputs[0]
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sampling_params.detokenize = True
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outputs_with_detokenization = llm.generate(prompt,
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sampling_params)[0].outputs[0]
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assert outputs_no_detokenization.text == ''
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assert outputs_with_detokenization.text != ''
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assert outputs_no_detokenization.token_ids == \
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outputs_with_detokenization.token_ids
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