"""Compare the outputs of HF and vLLM when using greedy sampling. It tests chunked prefill. Chunked prefill can be enabled by enable_chunked_prefill=True. If prefill size exceeds max_num_batched_tokens, prefill requests are chunked. Run `pytest tests/models/test_chunked_prefill.py`. """ import pytest MODELS = [ "facebook/opt-125m", "meta-llama/Llama-2-7b-hf", ] @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [32]) @pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16]) @pytest.mark.parametrize("enforce_eager", [False, True]) # NOTE: Increasing this in this suite will fail CI because we currently cannot # reset distributed env properly. Use a value > 1 just when you test. @pytest.mark.parametrize("tensor_parallel_size", [1]) def test_models( hf_runner, vllm_runner, example_prompts, model: str, dtype: str, max_tokens: int, chunked_prefill_token_size: int, enforce_eager: bool, tensor_parallel_size: int, ) -> None: max_num_seqs = min(chunked_prefill_token_size, 256) enable_chunked_prefill = False max_num_batched_tokens = None if chunked_prefill_token_size != -1: enable_chunked_prefill = True max_num_batched_tokens = chunked_prefill_token_size hf_model = hf_runner(model, dtype=dtype) hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) del hf_model vllm_model = vllm_runner( model, dtype=dtype, max_num_batched_tokens=max_num_batched_tokens, enable_chunked_prefill=enable_chunked_prefill, tensor_parallel_size=tensor_parallel_size, enforce_eager=enforce_eager, max_num_seqs=max_num_seqs, ) vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) del vllm_model for i in range(len(example_prompts)): hf_output_ids, hf_output_str = hf_outputs[i] vllm_output_ids, vllm_output_str = vllm_outputs[i] assert hf_output_str == vllm_output_str, ( f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}") assert hf_output_ids == vllm_output_ids, ( f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")