d4201e06d5
Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com> Co-authored-by: Nick Hill <nickhill@us.ibm.com>
45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
import pytest
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from .conftest import run_equality_correctness_test
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@pytest.mark.parametrize(
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"common_llm_kwargs",
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[{
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"model": "JackFram/llama-68m",
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# Skip cuda graph recording for fast test.
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"enforce_eager": True,
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# Required for spec decode.
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"use_v2_block_manager": True,
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# speculative model
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"speculative_model": "JackFram/llama-160m",
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# num speculative tokens
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"num_speculative_tokens": 3,
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}])
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@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
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@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
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@pytest.mark.parametrize("batch_size", [1, 8, 32])
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@pytest.mark.parametrize("temperature", [0.1, 1.0])
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@pytest.mark.parametrize(
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"output_len",
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[
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# Use smaller output len for fast test.
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10,
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])
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@pytest.mark.parametrize("seed", [1])
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def test_seeded_consistency(baseline_llm_generator, batch_size: int,
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temperature: float, output_len: int):
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"""Verify outputs are consistent across multiple runs with same seed
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"""
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run_equality_correctness_test(baseline_llm_generator,
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baseline_llm_generator,
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batch_size,
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max_output_len=output_len,
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temperature=temperature,
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seeded=True,
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force_output_len=True)
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