【vLLM 学习】Profiling

vLLM 是一款专为大语言模型推理加速而设计的框架，实现了 KV 缓存内存几乎零浪费，解决了内存管理瓶颈问题。

更多 vLLM 中文文档及教程可访问 →vllm.hyper.ai/

*在线运行 vLLM 入门教程：零基础分步指南

源码 examples/offline_inference/profiling.py

# SPDX-License-Identifier: Apache-2.0 import inspect import json import os import sys from argparse import RawTextHelpFormatter from collections.abc import Generator from dataclasses import asdict, dataclass from typing import Any, Optional, TypeAlias import torch import tqdm from vllm import LLM, SamplingParams from vllm.engine.arg_utils import EngineArgs from vllm.profiler import layerwise_profile from vllm.utils import FlexibleArgumentParser BATCH_SIZE_DEFAULT = 1 PROMPT_LEN_DEFAULT = 256 @dataclass class ProfileContext: engine_args: EngineArgs prompt_len: int batch_size: int # The profiler can run in 2 modes, # 1. Run profiler for user specified num_steps # 分析器能以 2 个模式运行， # 1.为用户指定的 num_steps 运行 profiler num_steps: Optional[int] = None # 2. Run profiler until all requests complete # 2.运行 profiler 直到所有请求完成 complete_num_requests_per_step: Optional[int] = None save_chrome_traces_folder: Optional[str] = None def get_dtype(dtype: str): if dtype == "torch.float": return torch.float else: return dtype OutputLen_NumReqs_Map: TypeAlias = dict[int, int] def compute_request_output_lengths(batch_size: int, step_requests: list[int]) \ -> OutputLen_NumReqs_Map: """ 根据请求数量、batch_size 以及每个引擎步骤应处理的请求数 step_requests， 确定各请求的输出长度，以确保满足 step_requests 的要求。 示例： 若 batch_size = 128 且 step_requests = [128, 128, 96, 64, 32, 1] 则返回 {2: 32, 3: 32, 4: 32, 5: 31, 6: 1}，表示： 应有 32 个请求的输出长度为 2， 32 个请求的输出长度为 3， 32 个请求的输出长度为 4， 31 个请求的输出长度为 5， 1 个请求的输出长度为 6。 Args: batch_size (int): 提交分析的请求数量，对应 args.batch_size step_requests (list[int]): step_requests[i] 表示第 i 个引擎步骤应处理的请求数 Returns: OutputLen_NumReqs_Map: 字典类型，键为输出长度，值为对应该输出长度的请求数量 """ ol_nr: OutputLen_NumReqs_Map = {} # 分配了输出长度的请求数 num_reqs_assigned: int = 0 num_steps: int = len(step_requests) # 理智检查。第一步 (预填充步骤) 必须处理所有请求。 assert step_requests[0] == batch_size # 从最后一步开始分配。 output_length: int = num_steps for num_requests_at_step in reversed(step_requests): if num_reqs_assigned == batch_size: break assert num_reqs_assigned < batch_size # 删除已确定的请求数量 # 参加此步骤及以后。 num_reqs_unassigned_at_step = num_requests_at_step - num_reqs_assigned assert num_reqs_unassigned_at_step >= 0 if num_reqs_unassigned_at_step > 0: ol_nr[output_length] = num_reqs_unassigned_at_step num_reqs_assigned += num_reqs_unassigned_at_step output_length -= 1 # 理智检查。 assert sum(ol_nr.values()) == batch_size, \ ("Number of requests in output-length assignment does not match " f"batch-size.\n batch size {batch_size} - " f"step requests {step_requests} - assignments {ol_nr}") # 检查输出长度是否在[1，numSteps]中。输出长度必须是 # 至少1个请求必须参与预填充步骤。 assert all(ol >= 1 and ol <= num_steps for ol in ol_nr), \ ("Output lengths of requests should be in range " f"[1, num-engine-steps].\n batch size {batch_size} - " f"step requests {step_requests} - assignments {ol_nr}") return ol_nr def determine_requests_per_step(context: ProfileContext) -> list[int]: """ 确定每个引擎步骤应处理的请求数量。 若设置了 context.num_steps，则所有引擎步骤处理相同数量的请求， 且输出列表的长度为 context.num_steps。 若设置了 context.complete_num_requests_per_step，则每个解码步骤 处理的请求数量逐次递减，直至没有待处理请求。 此时，输出列表的大小等于处理所有请求所需的步骤数。 Args: context: ProfileContext 对象。 Returns: list[int]: 所有引擎步骤应处理的请求数量列表。 output[i] 表示第 i 个步骤应处理的请求数量。 """ if context.num_steps: # 所有请求必须运行，直到 num_engine_steps 为止。这意味着 # 他们的输出长度必须等于 num_engine_steps。 return [context.batch_size] * context.num_steps assert context.complete_num_requests_per_step and \ context.complete_num_requests_per_step > 0, \ (f"Expected a positive complete_num_requests_per_step argument." f"Instead got {context.complete_num_requests_per_step}") # 我们在第一个解码步骤之后开始掉落。 step_requests = [ context.batch_size, # prefill # 预填充 context.batch_size, # decode # 解码 ] num_running_requests = context.batch_size num_running_requests -= context.complete_num_requests_per_step while num_running_requests > 0: step_requests.append(num_running_requests) num_running_requests -= context.complete_num_requests_per_step if step_requests[-1] != 1: # 在最后一步有1个请求。这通常很有用 step_requests.append(1) return step_requests def run_profile(context: ProfileContext, csv_output: Optional[str], json_output: Optional[str]): print("Run profile with:") for key, value in asdict(context).items(): print(f" {key} = {value}") requests_per_step: list[int] = determine_requests_per_step(context) ol_nr: OutputLen_NumReqs_Map = compute_request_output_lengths( context.batch_size, requests_per_step) num_steps_to_profile: int = len(requests_per_step) max_output_len: int = max(ol_nr.keys()) assert max_output_len >= 1 # 创建采样参数 sampling_params = SamplingParams( temperature=0.8, top_p=0.95, # max_tokens is set on a per-request basis. # MAX_TOKENS 以每次要求设置。 max_tokens=None, ignore_eos=True) # 创建 LLM llm = LLM(**asdict(context.engine_args)) batch_size = context.batch_size prompt_len = context.prompt_len scheduler_config = llm.llm_engine.scheduler_config max_model_len = llm.llm_engine.model_config.max_model_len max_num_batched_tokens = scheduler_config.max_num_batched_tokens max_num_seqs = scheduler_config.max_num_seqs if batch_size * prompt_len > max_num_batched_tokens: print(f"ERROR: chosen batch_size * prompt_len " f"({batch_size} * {prompt_len} = {batch_size * prompt_len}) is " f"larger than max_num_batched_tokens ({max_num_batched_tokens}) " f"and therefore cannot be run in a single profile step, please " f"choose a smaller batch size or prompt length, or increase " f"--max-num-batched-tokens") sys.exit(-1) if batch_size > max_num_seqs: print( f"ERROR: chosen batch_size ({batch_size}) is larger than " f"max_num_seqs ({max_num_seqs}) and therefore cannot be run in a " f"single profile step, please choose a smaller batch size") sys.exit(-1) print("llm.llm_engine.model_config.max_model_len: ", llm.llm_engine.model_config.max_model_len) if prompt_len + max_output_len > llm.llm_engine.model_config.max_model_len: print(f"ERROR: chosen prompt_len + max_output_len ({prompt_len} + " f"{max_output_len} = {prompt_len + max_output_len}) is larger " f"than the model's max_model_len ({max_model_len}), please " f"choose a smaller prompt_len or max_output_len, or increase " f"--max-model-len") sys.exit(-1) def add_requests(): def get_output_len_generator() -> Generator[int, Any, Any]: for output_len, num_reqs in ol_nr.items(): for _ in range(num_reqs): yield output_len output_len_generator = get_output_len_generator() for i in range(batch_size): sampling_params.max_tokens = next(output_len_generator) assert isinstance(sampling_params.max_tokens, int) prompt_token_ids = torch.randint( llm.llm_engine.model_config.get_vocab_size(), size=(prompt_len, )).tolist() llm.llm_engine.add_request( request_id=f"seq{i}", prompt={'prompt_token_ids': prompt_token_ids}, params=sampling_params) def abort_requests(): for i in range(batch_size): llm.llm_engine.abort_request(f"seq{i}") # 预热跑步 print("Warm up run ...") add_requests() llm.llm_engine.step() # Prefill llm.llm_engine.step() # Decode abort_requests() print("Profile run ...") add_requests() with layerwise_profile() as prefill_prof: llm.llm_engine.step() # First step is prefill decode_profs = [] for _ in tqdm.tqdm(range(num_steps_to_profile - 1)): num_running_seqs = llm.llm_engine.scheduler[ 0].get_num_unfinished_seq_groups() with layerwise_profile( num_running_seqs=num_running_seqs) as decode_prof: llm.llm_engine.step() decode_profs.append(decode_prof) decode_results_list = [prof.results for prof in decode_profs] prefill_results = prefill_prof.results has_decode = len(decode_results_list) > 0 LINE_WIDTH = 80 print("=" * LINE_WIDTH) print(f"= Prefill Model Table " f"(prompt_len={prompt_len}, batch_size={batch_size})") print("=" * LINE_WIDTH) print() prefill_results.print_model_table() if has_decode: print() print("=" * LINE_WIDTH) print(f"= First Decode Step Model Table " f"(prompt_len={prompt_len}, batch_size={batch_size})") print("=" * LINE_WIDTH) print() decode_results_list[0].print_model_table() print() print("=" * LINE_WIDTH) print(f"= Prefill Summary Table " f"(prompt_len={prompt_len}, batch_size={batch_size})") print("=" * LINE_WIDTH) print() prefill_results.print_summary_table() if has_decode: print() print("=" * LINE_WIDTH) print(f"= First Decode Step Summary Table " f"(prompt_len={prompt_len}, batch_size={batch_size})") print("=" * LINE_WIDTH) print() decode_results_list[0].print_summary_table() if csv_output: csv_filename_base = csv_output[:-4] \ if csv_output.endswith('.csv') else csv_output prefill_results.export_model_stats_table_csv( csv_filename_base + "_prefill_model_table.csv") prefill_results.export_summary_stats_table_csv( csv_filename_base + "_prefill_summary_table.csv") if has_decode: decode_results_list[0].export_model_stats_table_csv(\ csv_filename_base + "_decode_model_table.csv") decode_results_list[0].export_summary_stats_table_csv( csv_filename_base + "_decode_summary_table.csv") if json_output: cuda_devices = [ torch.cuda.get_device_properties(dev_idx) for dev_idx in range(torch.cuda.device_count()) ] json_dict = { "context": { "python_version": f"{sys.version}", "torch_version": f"{torch.__version__}", "torch_cuda_version": f"{torch.version.cuda}", "cuda_devices": f"{cuda_devices}", **asdict(context) }, "prefill": prefill_results.convert_stats_to_dict(), } if has_decode: for idx, dr in enumerate(decode_results_list): json_dict[f"decode_{idx + 1}"] = dr.convert_stats_to_dict() # 如果尚不存在，则将.json 添加到 JSON_OUTPUT 文件名。 json_output_file = json_output if json_output.endswith( '.json') else json_output + '.json' with open(json_output_file, "w+") as f: json.dump(json_dict, f, indent=2) pass if context.save_chrome_traces_folder is not None: os.makedirs(context.save_chrome_traces_folder, exist_ok=True) prefill_prof.profiler.export_chrome_trace( context.save_chrome_traces_folder + "/prefill.json") for idx, decode_prof in enumerate(decode_profs): decode_prof.profiler.export_chrome_trace( context.save_chrome_traces_folder + f"/decode_{idx + 1}.json") print("Traces saved as prefill.json and decode_1.json, etc." f" in folder {context.save_chrome_traces_folder}") if __name__ == "__main__": parser = FlexibleArgumentParser(description=""" Profile a model example: ``` python examples/offline_inference/profiling.py \ --model neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8 --batch-size 4 \ --prompt-len 512 --max-num-batched-tokens 8196 --json Llama31-8b-FP8 \ --enforce-eager run_num_steps -n 2 ``` then you can use various tools to analyze the json output terminal ascii tables: ``` python tools/profiler/print_layerwise_table.py \ --json-trace Llama31-8b-FP8.json --phase prefill --table summary ``` or create matplotlib stacked bar charts: ``` python tools/profiler/visualize_layerwise_profile.py \ --json-trace Llama31-8b-FP8.json \ --output-directory profile_breakdown --plot-metric pct_cuda_time ``` """, formatter_class=RawTextHelpFormatter) parser.add_argument( "--csv", type=str, default=None, help="Export the results as multiple csv file. This should be the root " "filename, will create <filename>_prefill_model_table.csv, " "<filename>_prefill_summary_table.csv, " "<filename>_decode_model_table.csv, and " "<filename>_decode_summary_table.csv") parser.add_argument( "--json", type=str, default=None, help="Export the results as a json file. This should be the filename") parser.add_argument("--save-chrome-traces-folder", type=str, help="Save chrome traces for the prefill and decode " "will save traces as prefill.json and decode_1.json, " "etc. inside this folder") parser.add_argument( "--prompt-len", type=int, default=PROMPT_LEN_DEFAULT, help=f"Length of the random prompt to use when profiling, all batched " f"requests use the same prompt_len, default={PROMPT_LEN_DEFAULT}") parser.add_argument("--batch-size", type=int, default=BATCH_SIZE_DEFAULT, help=f"Number of requests to run as a single batch, " f"default={BATCH_SIZE_DEFAULT}") subparsers = parser.add_subparsers(dest="cmd") run_num_steps_parser = subparsers.add_parser( "run_num_steps", help="This variation profiles n engine.step() invocations.") run_num_steps_parser.add_argument( '-n', '--num-steps', type=int, help="Number of engine steps to profile.\n" "Setting it to 1, profiles only the prefill step.\n" "Setting it to 2, profiles the prefill and first decode step\n" "Setting it to 3, profiles the prefill, 1st and 2nd decode steps\n" "and so on ...") run_to_completion_parser = subparsers.add_parser( "run_to_completion", help="This variation profiles all the engine.step() invocations" "until the engine exhausts all submitted requests.") run_to_completion_parser.add_argument( '-n', '--complete-num-requests-per-step', type=int, help= "Complete complete_num_requests_per_step requests every decode step." "For e.g., with batch_size 128 and complete_num_requests_per_step 32," "the profiler is run for 6 engine steps, with the steps processing, " "128, 128, 96, 64, 32, 1 requests respectively.\n" "Note that we tack-on a one-request step at the end as it is often " "useful.") EngineArgs.add_cli_args(parser) args = parser.parse_args() context = ProfileContext( engine_args=EngineArgs.from_cli_args(args), **{ k: v for k, v in vars(args).items() if k in inspect.signature(ProfileContext).parameters }) run_profile(context, csv_output=args.csv, json_output=args.json)