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) 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)