vllm/tests/samplers/test_logprobs.py

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import pytest
import torch
from vllm import SamplingParams
MODELS = ["facebook/opt-125m"]
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
def test_get_prompt_logprobs(
hf_runner,
vllm_runner,
model,
dtype,
example_prompts,
):
max_tokens = 5
hf_model = hf_runner(model, dtype=dtype)
hf_logprobs = hf_model.generate_greedy_logprobs(
example_prompts,
max_tokens=max_tokens,
)
del hf_model
vllm_model = vllm_runner(model, dtype=dtype)
vllm_sampling_params = SamplingParams(max_tokens=max_tokens,
logprobs=5,
prompt_logprobs=5,
temperature=0.0)
vllm_results = vllm_model.model.generate(
example_prompts, sampling_params=vllm_sampling_params)
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del vllm_model
# Test whether logprobs are included in the results.
for result in vllm_results:
assert result.prompt_logprobs is not None
assert result.outputs[0].logprobs is not None
# Test whether prompt logprobs are consistent with HF
for vllm_result, hf_logprob in zip(vllm_results, hf_logprobs):
# Check prompt logprobs
vllm_prompt_logprobs = vllm_result.prompt_logprobs[1:]
for i, vllm_prompt_logprob_dict in enumerate(vllm_prompt_logprobs):
for token_id, logprob in vllm_prompt_logprob_dict.items():
torch.testing.assert_close(logprob,
hf_logprob[0][i][token_id].item(),
atol=1e-2,
rtol=1e-2)
vllm_sample_logprobs = vllm_result.outputs[0].logprobs
for i, vllm_sample_logprob_dict in enumerate(vllm_sample_logprobs):
for token_id, logprob in vllm_sample_logprob_dict.items():
torch.testing.assert_close(logprob,
hf_logprob[i][-1][token_id].item(),
atol=1e-2,
rtol=1e-2)