"""Compare the outputs of HF and vLLM when using greedy sampling. Run `pytest tests/models/test_models.py`. """ import pytest import os from ...utils import check_logprobs_close from ....utils import models_path_prefix @pytest.mark.parametrize( "model", [ pytest.param( os.path.join(models_path_prefix, "bigscience/bloom-560m"), # bloom - testing alibi slopes marks=[pytest.mark.core_model, pytest.mark.cpu_model], ), pytest.param( os.path.join(models_path_prefix, "openai-community/gpt2"), # gpt2 marks=[pytest.mark.core_model, pytest.mark.cpu_model], ), pytest.param(os.path.join(models_path_prefix, "Milos/slovak-gpt-j-405M")), # gptj pytest.param(os.path.join(models_path_prefix, "bigcode/tiny_starcoder_py")), # gpt_bigcode pytest.param(os.path.join(models_path_prefix, "EleutherAI/pythia-70m")), # gpt_neox pytest.param( os.path.join(models_path_prefix, "google/gemma-1.1-2b-it"), # gemma marks=[pytest.mark.core_model, pytest.mark.cpu_model], ), pytest.param( os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct"), # llama marks=[pytest.mark.core_model, pytest.mark.cpu_model], ), pytest.param( os.path.join(models_path_prefix, "openbmb/MiniCPM3-4B"), # fused_moe not supported on CPU marks=[pytest.mark.core_model], ), pytest.param( os.path.join(models_path_prefix, "facebook/opt-125m"), # opt marks=[pytest.mark.core_model, pytest.mark.cpu_model], ), pytest.param( os.path.join(models_path_prefix, "microsoft/phi-2"), # phi marks=[pytest.mark.core_model], ), pytest.param( os.path.join(models_path_prefix, "Qwen/Qwen2.5-0.5B-Instruct"), # qwen2 marks=[pytest.mark.core_model], ), pytest.param(os.path.join(models_path_prefix, "stabilityai/stablelm-3b-4e1t")), # stablelm pytest.param(os.path.join(models_path_prefix, "bigcode/starcoder2-3b")), # starcoder2 ]) @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [32]) @pytest.mark.parametrize("num_logprobs", [5]) def test_models( hf_runner, vllm_runner, example_prompts, model: str, dtype: str, max_tokens: int, num_logprobs: int, ) -> None: with hf_runner(model, dtype=dtype) as hf_model: hf_outputs = hf_model.generate_greedy_logprobs_limit( example_prompts, max_tokens, num_logprobs) with vllm_runner(model, dtype=dtype) as vllm_model: vllm_outputs = vllm_model.generate_greedy_logprobs( example_prompts, max_tokens, num_logprobs) # This test is for verifying whether the model's extra_repr # can be printed correctly. print(vllm_model.model.llm_engine.model_executor.driver_worker. model_runner.model) check_logprobs_close( outputs_0_lst=hf_outputs, outputs_1_lst=vllm_outputs, name_0="hf", name_1="vllm", )