import asyncio import os from itertools import cycle from typing import Dict, List, Optional, Sequence, Tuple, Union import pytest import ray import torch from vllm import LLM from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.async_llm_engine import AsyncLLMEngine from vllm.lora.request import LoRARequest from vllm.model_executor.utils import set_random_seed from vllm.multimodal import MultiModalDataDict from vllm.outputs import RequestOutput from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sampling_params import SamplingParams from vllm.sequence import Logprob from vllm.usage.usage_lib import UsageContext from vllm.utils import Counter, random_uuid from ...conftest import cleanup from ...utils import wait_for_gpu_memory_to_clear class AsyncLLM: """AsyncLLM Note: Current LLM class in vllm don't support async mode, for test purpose, we implement async one in here. Maybe we could move to vllm/entrypoints/llm.py in future. Below AsyncLLM is directly borrow from vllm/entrypoints/llm.py with changes to make to work in async mode. """ def __init__( self, model: str, tokenizer: Optional[str] = None, tokenizer_mode: str = "auto", skip_tokenizer_init: bool = False, trust_remote_code: bool = False, tensor_parallel_size: int = 1, dtype: str = "auto", quantization: Optional[str] = None, revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, seed: int = 0, gpu_memory_utilization: float = 0.9, swap_space: int = 4, enforce_eager: bool = False, max_seq_len_to_capture: int = 8192, disable_custom_all_reduce: bool = False, **kwargs, ) -> None: if "disable_log_stats" not in kwargs: kwargs["disable_log_stats"] = True # Needed to engine_use_ray works as a deprecated feature, # otherwise the following constructor will raise an exception os.environ["VLLM_ALLOW_ENGINE_USE_RAY"] = "1" engine_args = AsyncEngineArgs( model=model, tokenizer=tokenizer, tokenizer_mode=tokenizer_mode, skip_tokenizer_init=skip_tokenizer_init, trust_remote_code=trust_remote_code, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, revision=revision, tokenizer_revision=tokenizer_revision, seed=seed, gpu_memory_utilization=gpu_memory_utilization, swap_space=swap_space, enforce_eager=enforce_eager, max_seq_len_to_capture=max_seq_len_to_capture, # For now use ray for the distributed back-end, since # we rely on the use of engine_use_ray=True to avoid # reinitializing CUDA in the same process (driver worker) engine_use_ray=True, distributed_executor_backend="ray", disable_custom_all_reduce=disable_custom_all_reduce, **kwargs, ) self.request_counter = Counter() self.llm_engine = AsyncLLMEngine.from_engine_args( engine_args, usage_context=UsageContext.LLM_CLASS) def generate( self, prompts: Optional[Union[str, List[str]]] = None, sampling_params: Optional[Union[SamplingParams, List[SamplingParams]]] = None, prompt_token_ids: Optional[List[List[int]]] = None, use_tqdm: bool = True, lora_request: Optional[LoRARequest] = None, multi_modal_data: Optional[MultiModalDataDict] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None ) -> List[RequestOutput]: if prompts is None: raise ValueError("prompts must be provided.") if isinstance(prompts, str): # Convert a single prompt to a list. prompts = [prompts] if prompts is not None: num_requests = len(prompts) if sampling_params is None: # Use default sampling params. sampling_params = SamplingParams() elif isinstance(sampling_params, list) and len(sampling_params) != num_requests: raise ValueError("The lengths of prompts and " "sampling_params must be the same.") async def get_output(prompt, sampling_param) -> RequestOutput: request_id = random_uuid() results_generator = self.llm_engine.generate( prompt, sampling_param, request_id) final_output = None async for request_output in results_generator: final_output = request_output assert final_output is not None return final_output outputs: List[RequestOutput] = [] try: for i in range(num_requests): prompt = prompts[i] if prompts is not None else None params = sampling_params[i] if isinstance( sampling_params, Sequence) else sampling_params res = asyncio.run(get_output(prompt, params)) outputs.append(res) finally: ray.shutdown() return outputs @pytest.fixture def baseline_llm_generator(request, common_llm_kwargs, per_test_common_llm_kwargs, baseline_llm_kwargs, seed): return create_llm_generator("baseline", request, common_llm_kwargs, per_test_common_llm_kwargs, baseline_llm_kwargs, seed) @pytest.fixture def test_llm_generator(request, common_llm_kwargs, per_test_common_llm_kwargs, test_llm_kwargs, seed): return create_llm_generator("test", request, common_llm_kwargs, per_test_common_llm_kwargs, test_llm_kwargs, seed) def create_llm_generator(baseline_or_test, request, common_llm_kwargs, per_test_common_llm_kwargs, distinct_llm_kwargs, seed): kwargs = { **common_llm_kwargs, **per_test_common_llm_kwargs, **distinct_llm_kwargs, } test_name = request.node.name model = kwargs["model"] draft_model = kwargs.get("speculative_model", None) same_draft_target_model = (draft_model is not None and draft_model == model) def generator_inner(): wait_for_gpu_memory_to_clear( devices=list(range(torch.cuda.device_count())), threshold_bytes=2 * 2**30, timeout_s=60, ) use_async = False if "use_async" in kwargs: use_async = kwargs.pop("use_async") print(f'{use_async=}') print(f'Creating {baseline_or_test=} LLM for {test_name=}. {kwargs=}') llm = AsyncLLM(**kwargs) if use_async else LLM(**kwargs) # Override logging interval to 0 for spec decode test run to # log all metrics in time. if (baseline_or_test == "test" and not use_async and llm.llm_engine.log_stats): for sate_logger in llm.llm_engine.stat_loggers.values(): sate_logger.local_interval = 0 if seed is not None: set_random_seed(seed) yield llm del llm cleanup() def generator_outer(): for llm in generator_inner(): yield llm del llm # Set an attribute to the generator_outer function to allow us to # determine whether to further check the acceptance rate in tests. generator_outer.same_draft_target_model = same_draft_target_model # type: ignore return generator_outer def maybe_assert_ngram_worker(llm): # Verify the proposer worker is ngram if ngram is specified. if (not isinstance(llm, AsyncLLM) and llm.llm_engine.speculative_config is not None and llm.llm_engine.speculative_config.ngram_prompt_lookup_max > 0): from vllm.spec_decode.ngram_worker import NGramWorker assert isinstance( llm.llm_engine.model_executor.driver_worker.proposer_worker, NGramWorker) def get_output_from_llm_generator( llm_generator, prompts, sampling_params) -> Tuple[List[str], List[List[int]], float]: tokens: List[str] = [] token_ids: List[List[int]] = [] acceptance_rate: float = -1.0 for llm in llm_generator(): maybe_assert_ngram_worker(llm) outputs = llm.generate(prompts, sampling_params, use_tqdm=True) token_ids = [output.outputs[0].token_ids for output in outputs] tokens = [output.outputs[0].text for output in outputs] # Fetch acceptance rate if logging is enabled. if stat_loggers := getattr(llm.llm_engine, "stat_loggers", None): stat_logger = stat_loggers["prometheus"] acceptance_rate = (stat_logger.metrics. gauge_spec_decode_draft_acceptance_rate.labels( **stat_logger.labels)._value.get()) del llm return tokens, token_ids, acceptance_rate def get_logprobs_from_llm_generator( llm_generator, prompts, sampling_params) -> List[List[Dict[int, Logprob]]]: """Returns a dict of (token_id: Logprob) for each generated position, for each sequence in the batch. """ for llm in llm_generator(): outputs = llm.generate(prompts, sampling_params, use_tqdm=True) logprobs = [output.outputs[0].logprobs[:] for output in outputs] del llm return logprobs def run_greedy_equality_correctness_test(baseline_llm_generator, test_llm_generator, batch_size, max_output_len, force_output_len: bool, print_tokens: bool = False, ensure_all_accepted: bool = False): """Helper method that compares the outputs of both the baseline LLM and the test LLM. It asserts greedy equality, e.g. that the outputs are exactly the same when temperature is zero. """ run_equality_correctness_test(baseline_llm_generator, test_llm_generator, batch_size, max_output_len, force_output_len, temperature=0.0, seeded=False, print_tokens=print_tokens, ensure_all_accepted=ensure_all_accepted) def run_equality_correctness_test( baseline_llm_generator, test_llm_generator, batch_size, max_output_len, force_output_len: bool, temperature: float, seeded: bool, print_tokens: bool = False, ensure_all_accepted: bool = False, expected_acceptance_rate: Optional[float] = None): """Helper method that compares the outputs of both the baseline LLM and the test LLM. It asserts greedy equality, e.g. that the outputs are exactly the same when temperature is zero (or when temperature is > 0 and seeded). """ prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", "San Francisco is know for its", "Facebook was created in 2004 by", "Curious George is a", "Python 3.11 brings improvements to its", ] prompts = [prompt for prompt, _ in zip(cycle(prompts), range(batch_size))] # If the test requires that we generated max_output_len tokens, then set the # sampling params to ignore eos token. ignore_eos = force_output_len if seeded: sampling_params = [ SamplingParams( max_tokens=max_output_len, ignore_eos=ignore_eos, temperature=temperature, seed=i, ) for i in range(len(prompts)) ] else: sampling_params = SamplingParams( max_tokens=max_output_len, ignore_eos=ignore_eos, temperature=temperature, ) (spec_batch_tokens, spec_batch_token_ids, acceptance_rate) = get_output_from_llm_generator(test_llm_generator, prompts, sampling_params) (baseline_batch_tokens, baseline_batch_token_ids, _) = get_output_from_llm_generator(baseline_llm_generator, prompts, sampling_params) assert len(baseline_batch_token_ids) == len(prompts) assert len(spec_batch_token_ids) == len(prompts) for i, (baseline_token_ids, baseline_tokens, spec_token_ids, spec_tokens) in enumerate( zip(baseline_batch_token_ids, baseline_batch_tokens, spec_batch_token_ids, spec_batch_tokens)): if print_tokens: print(f'{i=} {baseline_tokens=}') print(f'{i=} {spec_tokens=}') print(f'{i=} {baseline_token_ids=}') print(f'{i=} {spec_token_ids=}') assert baseline_token_ids == spec_token_ids print(f'{acceptance_rate=}') if ensure_all_accepted: assert acceptance_rate == 1.0 if expected_acceptance_rate is not None: assert acceptance_rate >= expected_acceptance_rate - 1e-2