mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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701 lines
27 KiB
Python
701 lines
27 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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# This file is modified from https://github.com/vllm-project/vllm/blob/main/benchmarks/backend_request_func.py
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import io
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import json
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import os
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import sys
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import time
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import traceback
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from dataclasses import dataclass, field
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from typing import Optional
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import aiohttp
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from tqdm.asyncio import tqdm
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AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
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@dataclass
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class RequestFuncInput:
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"""Input for requesting LLMs via API"""
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prompt: str
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history_QA: Optional[dict]
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hyper_parameters: dict
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api_url: str
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prompt_len: int
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output_len: int
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model: str
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model_name: Optional[str] = None
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logprobs: Optional[int] = None
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extra_body: Optional[dict] = None
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multi_modal_content: Optional[dict] = None
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ignore_eos: bool = False
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language: Optional[str] = None
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@dataclass
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class RequestFuncOutput:
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"""Output for requesting LLMs via API"""
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generated_text: str = ""
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reasoning_content: str = ""
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success: bool = False
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latency: float = 0.0
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output_tokens: int = 0
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ttft: float = 0.0 # Time to first token
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arrival_time: list = field(default_factory=list) # arrival_time
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itl: list = field(default_factory=list) # list of inter-token latencies
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tpot: float = 0.0 # avg next-token latencies
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prompt_len: int = 0
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prompt_tokens: int = 0 # 推理侧返回输入token数
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error: str = ""
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async def async_request_eb_openai_chat_completions(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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"""Request an LLM using EB OpenAI"""
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api_url = request_func_input.api_url
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assert api_url.endswith(
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("completions", "profile")
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), "OpenAI Chat Completions API URL must end with 'completions'."
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async with aiohttp.ClientSession(trust_env=True,
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timeout=AIOHTTP_TIMEOUT) as session:
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content = [{"type": "text", "text": request_func_input.prompt}]
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if request_func_input.multi_modal_content:
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content.append(request_func_input.multi_modal_content)
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payload = {
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"model": "default",
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"messages": request_func_input.history_QA,
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"stream": True,
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"stream_options": {
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"include_usage": True,
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"continuous_usage_stats": True
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},
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}
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# 超参由yaml传入
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payload.update(request_func_input.hyper_parameters)
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if request_func_input.ignore_eos:
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payload["ignore_eos"] = request_func_input.ignore_eos
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
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}
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output = RequestFuncOutput()
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output.prompt_len = 0
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ttft = 0.0
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload,
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headers=headers) as response:
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if response.status == 200:
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk = chunk_bytes.decode("utf-8").removeprefix(
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"data: ")
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if chunk != "[DONE]":
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# print("####chunk:", chunk, type(chunk))
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timestamp = time.perf_counter()
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data = json.loads(chunk)
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if choices := data.get("choices"):
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content = choices[0]["delta"].get("content")
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reason_content = choices[0]["delta"].get("reasoning_content")
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# First token
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if ttft == 0.0:
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ttft = timestamp - st
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output.ttft = ttft
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# cached_tokens
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output.prompt_len = data["usage"]["prompt_tokens_details"]["cached_tokens"]
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# Decoding phase
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else:
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output.itl.append(timestamp -
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most_recent_timestamp)
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output.generated_text += content or ""
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output.reasoning_content += reason_content or ""
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output.arrival_time.append(choices[0].get("arrival_time"))
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elif usage := data.get("usage"):
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output.output_tokens = usage.get(
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"completion_tokens")
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output.prompt_tokens = usage.get(
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"prompt_tokens")
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most_recent_timestamp = timestamp
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# output.generated_text = generated_text
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if output.generated_text.strip() == "":
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output.success = False
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output.error = "No generated text found!"
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else:
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output.success = True
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output.latency = most_recent_timestamp - st
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else:
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error_text = await response.text()
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print("####error response:", error_text, "####payload:", payload)
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output.error = error_text or ""
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output.success = False
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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output.error = "".join(traceback.format_exception(*exc_info))
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# 保存失败请求结果
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if not output.success:
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with open("error_output.txt", "a") as f:
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f.write(str(output) + "\n")
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if pbar:
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pbar.update(1)
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return output
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async def async_request_eb_openai_completions(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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"""Request an LLM using EB OpenAI"""
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api_url = request_func_input.api_url
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assert api_url.endswith(
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("completions", "profile")
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), "OpenAI Completions API URL must end with 'completions' or 'profile'."
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async with aiohttp.ClientSession(trust_env=True,
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timeout=AIOHTTP_TIMEOUT) as session:
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payload = {
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"model": "default",
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"prompt": request_func_input.prompt,
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"stream": True,
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"stream_options": {
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"include_usage": True,
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"continuous_usage_stats": True
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},
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}
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# 超参由yaml传入
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payload.update(request_func_input.hyper_parameters)
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if request_func_input.ignore_eos:
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payload["ignore_eos"] = request_func_input.ignore_eos
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headers = {
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"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
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}
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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generated_text = ""
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload,
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headers=headers) as response:
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if response.status == 200:
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first_chunk_received = False
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk = chunk_bytes.decode("utf-8").removeprefix(
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"data: ")
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if chunk != "[DONE]":
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# print("####chunk:", chunk, chunk.usage)
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data = json.loads(chunk)
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# NOTE: Some completion API might have a last
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# usage summary response without a token so we
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# want to check a token was generated
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if choices := data.get("choices"):
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# Note that text could be empty here
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# e.g. for special tokens
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text = choices[0].get("text")
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timestamp = time.perf_counter()
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# First token
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if not first_chunk_received:
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first_chunk_received = True
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ttft = time.perf_counter() - st
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output.ttft = ttft
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# Decoding phase
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else:
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output.itl.append(timestamp -
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most_recent_timestamp)
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most_recent_timestamp = timestamp
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output.arrival_time.append(choices[0].get("arrival_time"))
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generated_text += text or ""
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elif usage := data.get("usage"):
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output.prompt_tokens = usage.get(
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"prompt_tokens")
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output.output_tokens = usage.get(
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"completion_tokens")
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if first_chunk_received:
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output.success = True
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else:
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output.success = False
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output.error = (
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"Never received a valid chunk to calculate TTFT."
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"This response will be marked as failed!")
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output.generated_text = generated_text
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output.latency = most_recent_timestamp - st
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else:
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output.error = response.reason or ""
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output.success = False
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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output.error = "".join(traceback.format_exception(*exc_info))
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if pbar:
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pbar.update(1)
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return output
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async def async_request_tgi(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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"""Request an LLM using the TGI API"""
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api_url = request_func_input.api_url
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assert api_url.endswith("generate_stream")
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async with aiohttp.ClientSession(trust_env=True,
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timeout=AIOHTTP_TIMEOUT) as session:
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params = {
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"max_new_tokens": request_func_input.output_len,
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"do_sample": True,
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"temperature": 0.01, # TGI does not accept 0.0 temperature.
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"top_p": 0.99, # TGI does not accept 1.0 top_p.
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"truncate": request_func_input.prompt_len,
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"ignore_eos_token": request_func_input.ignore_eos,
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}
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payload = {
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"inputs": request_func_input.prompt,
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"parameters": params,
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}
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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if request_func_input.ignore_eos:
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output.output_tokens = request_func_input.output_len
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else:
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output.output_tokens = None
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ttft = 0.0
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload) as response:
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if response.status == 200:
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk_bytes = chunk_bytes.decode("utf-8")
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# NOTE: Sometimes TGI returns a ping response without
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# any data, we should skip it.
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if chunk_bytes.startswith(":"):
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continue
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chunk = chunk_bytes.removeprefix("data:")
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data = json.loads(chunk)
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timestamp = time.perf_counter()
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# First token
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if ttft == 0.0:
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ttft = time.perf_counter() - st
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output.ttft = ttft
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# Decoding phase
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else:
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output.itl.append(timestamp -
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most_recent_timestamp)
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most_recent_timestamp = timestamp
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output.arrival_time.append(data["arrival_time"])
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output.latency = most_recent_timestamp - st
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output.success = True
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output.generated_text = data["generated_text"]
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else:
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output.error = response.reason or ""
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output.success = False
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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output.error = "".join(traceback.format_exception(*exc_info))
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if pbar:
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pbar.update(1)
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return output
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async def async_request_trt_llm(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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"""Request an LLM using TRT's llm_server"""
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api_url = request_func_input.api_url
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assert api_url.endswith("generate_stream")
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async with aiohttp.ClientSession(trust_env=True,
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timeout=AIOHTTP_TIMEOUT) as session:
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payload = {
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"accumulate_tokens": True,
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"text_input": request_func_input.prompt,
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"temperature": 0.0,
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"top_p": 1.0,
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"max_tokens": request_func_input.output_len,
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"stream": True,
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}
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if request_func_input.ignore_eos:
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payload["min_length"] = request_func_input.output_len
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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ttft = 0.0
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload) as response:
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if response.status == 200:
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk = chunk_bytes.decode("utf-8").removeprefix(
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"data:")
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data = json.loads(chunk)
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output.generated_text += data["text_output"]
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timestamp = time.perf_counter()
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# First token
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if ttft == 0.0:
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ttft = timestamp - st
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output.ttft = ttft
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# Decoding phase
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else:
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output.itl.append(timestamp -
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most_recent_timestamp)
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most_recent_timestamp = timestamp
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output.latency = most_recent_timestamp - st
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output.success = True
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else:
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output.error = response.reason or ""
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output.success = False
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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output.error = "".join(traceback.format_exception(*exc_info))
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if pbar:
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pbar.update(1)
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return output
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async def async_request_deepspeed_mii(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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"""Request an LLM using Deepspeed MII"""
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async with aiohttp.ClientSession(trust_env=True,
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timeout=AIOHTTP_TIMEOUT) as session:
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payload = {
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"prompt": request_func_input.prompt,
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"max_tokens": request_func_input.output_len,
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"temperature": 0.01, # deepspeed-mii does not accept 0.0 temp.
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"top_p": 1.0,
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}
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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# NOTE: DeepSpeed-MII doesn't support streaming as of Jan 28 2024,
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# will use 0 as placeholder.
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# See https://github.com/microsoft/DeepSpeed-MII/pull/311
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output.ttft = 0
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st = time.perf_counter()
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try:
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async with session.post(url=request_func_input.api_url,
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json=payload) as response:
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if response.status == 200:
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parsed_resp = await response.json()
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output.latency = time.perf_counter() - st
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if "choices" in parsed_resp:
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output.generated_text = parsed_resp["choices"][0][
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"text"]
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elif "text" in parsed_resp:
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output.generated_text = parsed_resp["text"][0]
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else:
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output.error = ("Unexpected response format: "
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"neither 'choices' nor 'text' found")
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output.success = False
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output.success = True
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else:
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output.error = response.reason or ""
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output.success = False
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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output.error = "".join(traceback.format_exception(*exc_info))
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if pbar:
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pbar.update(1)
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return output
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async def async_request_openai_completions(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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"""Request an LLM using OpenAI"""
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api_url = request_func_input.api_url
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assert api_url.endswith(
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("completions", "profile")
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), "OpenAI Completions API URL must end with 'completions' or 'profile'."
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async with aiohttp.ClientSession(trust_env=True,
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timeout=AIOHTTP_TIMEOUT) as session:
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payload = {
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"model": request_func_input.model_name \
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if request_func_input.model_name else request_func_input.model,
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"prompt": request_func_input.prompt,
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# "temperature": 0.0,
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"max_tokens": request_func_input.output_len,
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"logprobs": request_func_input.logprobs,
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"stream": True,
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#"stream_options": {
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# "include_usage": True,
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#},
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}
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if request_func_input.ignore_eos:
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payload["ignore_eos"] = request_func_input.ignore_eos
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headers = {
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"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
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}
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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generated_text = ""
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload,
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headers=headers) as response:
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if response.status == 200:
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first_chunk_received = False
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk = chunk_bytes.decode("utf-8").removeprefix(
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"data: ")
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if chunk != "[DONE]":
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|
# print("####chunk:", chunk, type(chunk))
|
|
data = json.loads(chunk)
|
|
|
|
# NOTE: Some completion API might have a last
|
|
# usage summary response without a token so we
|
|
# want to check a token was generated
|
|
if choices := data.get("choices"):
|
|
# Note that text could be empty here
|
|
# e.g. for special tokens
|
|
text = choices[0].get("text")
|
|
timestamp = time.perf_counter()
|
|
# First token
|
|
if not first_chunk_received:
|
|
first_chunk_received = True
|
|
ttft = time.perf_counter() - st
|
|
output.ttft = ttft
|
|
|
|
# Decoding phase
|
|
else:
|
|
output.itl.append(timestamp -
|
|
most_recent_timestamp)
|
|
|
|
most_recent_timestamp = timestamp
|
|
generated_text += text or ""
|
|
elif usage := data.get("usage"):
|
|
output.output_tokens = usage.get(
|
|
"completion_tokens")
|
|
if first_chunk_received:
|
|
output.success = True
|
|
else:
|
|
output.success = False
|
|
output.error = (
|
|
"Never received a valid chunk to calculate TTFT."
|
|
"This response will be marked as failed!")
|
|
output.generated_text = generated_text
|
|
output.latency = most_recent_timestamp - st
|
|
else:
|
|
output.error = response.reason or ""
|
|
output.success = False
|
|
except Exception:
|
|
output.success = False
|
|
exc_info = sys.exc_info()
|
|
output.error = "".join(traceback.format_exception(*exc_info))
|
|
|
|
if pbar:
|
|
pbar.update(1)
|
|
return output
|
|
|
|
|
|
async def async_request_openai_audio(
|
|
request_func_input: RequestFuncInput,
|
|
pbar: Optional[tqdm] = None,
|
|
) -> RequestFuncOutput:
|
|
"""Request an LLM using OpenAI"""
|
|
# Lazy import without PlaceholderModule to avoid vllm dep.
|
|
import soundfile
|
|
api_url = request_func_input.api_url
|
|
assert api_url.endswith(
|
|
("transcriptions", "translations"
|
|
)), "OpenAI Chat Completions API URL must end with 'transcriptions' "
|
|
"or `translations`."
|
|
|
|
async with aiohttp.ClientSession(trust_env=True,
|
|
timeout=AIOHTTP_TIMEOUT) as session:
|
|
content = [{"type": "text", "text": request_func_input.prompt}]
|
|
payload = {
|
|
"model": request_func_input.model_name \
|
|
if request_func_input.model_name else request_func_input.model,
|
|
"temperature": 0.0,
|
|
"max_completion_tokens": request_func_input.output_len,
|
|
"stream": True,
|
|
"language": "en",
|
|
# Flattened due to multipart/form-data
|
|
"stream_include_usage": True,
|
|
"stream_continuous_usage_stats": True
|
|
}
|
|
if request_func_input.extra_body:
|
|
payload.update(request_func_input.extra_body)
|
|
headers = {
|
|
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
|
|
}
|
|
|
|
# Send audio file
|
|
def to_bytes(y, sr):
|
|
buffer = io.BytesIO()
|
|
soundfile.write(buffer, y, sr, format="WAV")
|
|
buffer.seek(0)
|
|
return buffer
|
|
|
|
with to_bytes(*request_func_input.multi_modal_content['audio']) as f:
|
|
form = aiohttp.FormData()
|
|
form.add_field('file', f, content_type='audio/wav')
|
|
for key, value in payload.items():
|
|
form.add_field(key, str(value))
|
|
|
|
output = RequestFuncOutput()
|
|
output.prompt_len = request_func_input.prompt_len
|
|
|
|
generated_text = ""
|
|
ttft = 0.0
|
|
st = time.perf_counter()
|
|
most_recent_timestamp = st
|
|
try:
|
|
async with session.post(url=api_url,
|
|
data=form,
|
|
headers=headers) as response:
|
|
if response.status == 200:
|
|
async for chunk_bytes in response.content:
|
|
chunk_bytes = chunk_bytes.strip()
|
|
if not chunk_bytes:
|
|
continue
|
|
|
|
chunk = chunk_bytes.decode("utf-8").removeprefix(
|
|
"data: ")
|
|
if chunk != "[DONE]":
|
|
timestamp = time.perf_counter()
|
|
data = json.loads(chunk)
|
|
|
|
if choices := data.get("choices"):
|
|
content = choices[0]["delta"].get(
|
|
"content")
|
|
# First token
|
|
if ttft == 0.0:
|
|
ttft = timestamp - st
|
|
output.ttft = ttft
|
|
|
|
# Decoding phase
|
|
else:
|
|
output.itl.append(
|
|
timestamp - most_recent_timestamp)
|
|
|
|
generated_text += content or ""
|
|
elif usage := data.get("usage"):
|
|
output.output_tokens = usage.get(
|
|
"completion_tokens")
|
|
|
|
most_recent_timestamp = timestamp
|
|
|
|
output.generated_text = generated_text
|
|
output.success = True
|
|
output.latency = most_recent_timestamp - st
|
|
else:
|
|
output.error = response.reason or ""
|
|
output.success = False
|
|
except Exception:
|
|
output.success = False
|
|
exc_info = sys.exc_info()
|
|
output.error = "".join(traceback.format_exception(*exc_info))
|
|
|
|
if pbar:
|
|
pbar.update(1)
|
|
return output
|
|
|
|
|
|
ASYNC_REQUEST_FUNCS = {
|
|
"tgi": async_request_tgi,
|
|
"vllm": async_request_openai_completions,
|
|
"lmdeploy": async_request_openai_completions,
|
|
"deepspeed-mii": async_request_deepspeed_mii,
|
|
"openai": async_request_eb_openai_completions,
|
|
"openai-chat": async_request_eb_openai_chat_completions,
|
|
"openai-audio": async_request_openai_audio,
|
|
"tensorrt-llm": async_request_trt_llm,
|
|
"scalellm": async_request_openai_completions,
|
|
"sglang": async_request_openai_completions,
|
|
}
|
|
|
|
OPENAI_COMPATIBLE_BACKENDS = [
|
|
k for k, v in ASYNC_REQUEST_FUNCS.items()
|
|
if v in (async_request_openai_completions,
|
|
async_request_eb_openai_chat_completions)
|
|
]
|
|
|