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fbe3547c95
* [Feature] Support include_stop_str_in_output in chat/completion * Add ci test for include_stop_str_in_output * Update version of openai * Fix ci test --------- Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com>
480 lines
21 KiB
Python
480 lines
21 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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import asyncio
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import json
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import time
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import traceback
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import uuid
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from typing import List, Optional
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import msgpack
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import aiozmq
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from aiozmq import zmq
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from fastdeploy.entrypoints.openai.protocol import (
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ChatCompletionRequest, ChatCompletionResponse,
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ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
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LogProbEntry, LogProbs, PromptTokenUsageInfo, UsageInfo)
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from fastdeploy.metrics.work_metrics import work_process_metrics
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from fastdeploy.utils import api_server_logger, get_host_ip
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from fastdeploy.worker.output import LogprobsLists
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class OpenAIServingChat:
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"""
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OpenAI-style chat completions serving
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"""
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def __init__(self, engine_client, pid, dist_init_ip):
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self.engine_client = engine_client
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self.pid = pid
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self.master_ip = dist_init_ip
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self.host_ip = get_host_ip()
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def _check_master(self):
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if self.master_ip is None:
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return True
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if self.host_ip == self.master_ip:
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return True
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return False
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async def create_chat_completion(
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self,
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request: ChatCompletionRequest
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):
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"""
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Create a new chat completion using the specified parameters.
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"""
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if not self._check_master():
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err_msg = f"Only master node can accept completion request, please send request to master node: {self.pod_ips[0]}"
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api_server_logger.error(err_msg)
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return ErrorResponse(message=err_msg, code=400)
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if request.user is not None:
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request_id = f"chatcmpl-{request.user}-{uuid.uuid4()}"
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else:
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request_id = f"chatcmpl-{uuid.uuid4()}"
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api_server_logger.info(f"create chat completion request: {request_id}")
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try:
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current_req_dict = request.to_dict_for_infer(request_id)
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current_req_dict["arrival_time"] = time.time()
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prompt_token_ids = self.engine_client.format_and_add_data(current_req_dict)
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except Exception as e:
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return ErrorResponse(code=400, message=str(e))
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del current_req_dict
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if request.stream:
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return self.chat_completion_stream_generator(
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request, request_id,
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request.model,
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prompt_token_ids)
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else:
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try:
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return await self.chat_completion_full_generator(
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request, request_id,
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request.model,
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prompt_token_ids)
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except Exception as e:
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return ErrorResponse(code=400, message=str(e))
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def _create_streaming_error_response(self, message: str) -> str:
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error_response = ErrorResponse(
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code=400,
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message=message,
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)
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return error_response.model_dump_json()
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async def chat_completion_stream_generator(
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self,
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request: ChatCompletionRequest,
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request_id: str,
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model_name: str,
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prompt_token_ids: list()
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):
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"""
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Streaming chat completion generator.
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"""
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created_time = int(time.time())
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chunk_object_type: str = "chat.completion.chunk"
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first_iteration = True
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previous_num_tokens = 0
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num_prompt_tokens = 0
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num_choices = 1
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max_streaming_response_tokens = 1
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enable_thinking = None
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include_stop_str_in_output = False
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if request.metadata is not None and request.metadata.get("max_streaming_response_tokens", 1) > 1:
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max_streaming_response_tokens = request.metadata["max_streaming_response_tokens"]
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stream_options = request.stream_options
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if stream_options is None:
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include_usage = False
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include_continuous_usage = False
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else:
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include_usage = stream_options.include_usage
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include_continuous_usage = stream_options.continuous_usage_stats
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[],
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model=model_name
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)
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try:
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dealer = await aiozmq.create_zmq_stream(
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zmq.DEALER,
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connect=f"ipc:///dev/shm/router_{self.pid}.ipc"
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)
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dealer.write([b"", request_id.encode('utf-8')])
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choices = []
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current_waiting_time = 0
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if request.metadata is not None:
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enable_thinking = request.metadata.get("enable_thinking")
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include_stop_str_in_output = request.metadata.get("include_stop_str_in_output", False)
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while num_choices > 0:
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try:
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raw_data = await asyncio.wait_for(dealer.read(), timeout=10)
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current_waiting_time = 0
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except asyncio.TimeoutError:
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current_waiting_time += 10
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if current_waiting_time == 300:
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status, msg = self.engine_client.check_health()
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if not status:
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if choices:
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chunk.choices = choices
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yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
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raise ValueError(f"Engine is not healthy: {msg}")
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else:
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current_waiting_time = 0
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await asyncio.sleep(0.01)
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continue
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response = msgpack.unpackb(raw_data[-1])
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for res in response:
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if res.get("error_code", 200) != 200:
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raise ValueError("{}".format(res["error_msg"]))
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self.engine_client.data_processor.process_response_dict(
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res, stream=True, enable_thinking=enable_thinking, include_stop_str_in_output=include_stop_str_in_output)
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if res['metrics']['first_token_time'] is not None:
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arrival_time = res['metrics']['first_token_time']
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inference_start_time = res['metrics']['inference_start_time']
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else:
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arrival_time = res['metrics']['arrival_time'] - inference_start_time
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if first_iteration:
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num_prompt_tokens = len(prompt_token_ids)
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num_cached_tokens = res.get("num_cached_tokens", 0)
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for i in range(num_choices):
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choice = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(role="assistant", content="", reasoning_content="", tool_calls=None)
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)
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if request.metadata is not None and request.metadata.get("training", False):
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choice.delta.token_ids = prompt_token_ids
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice],
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model=model_name
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)
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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total_tokens=num_prompt_tokens,
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prompt_tokens_details=PromptTokenUsageInfo(cached_tokens=num_cached_tokens)
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)
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yield f"data: {chunk.model_dump_json(exclude_unset=True)} \n\n"
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first_iteration = False
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output = res["outputs"]
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delta_text = output["text"]
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raw_top_logprobs = output["top_logprobs"]
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logprobs_res = None
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if raw_top_logprobs is not None:
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top_logprobs = LogprobsLists(
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logprob_token_ids=raw_top_logprobs[0],
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logprobs=raw_top_logprobs[1],
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sampled_token_ranks=raw_top_logprobs[2],
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)
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logprobs_res = self.build_logprobs_response(
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request_logprobs=request.logprobs,
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response_logprobs=top_logprobs,
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request_top_logprobs=request.top_logprobs,
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)
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previous_num_tokens += len(output["token_ids"])
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delta_message = DeltaMessage(content=delta_text, reasoning_content=output.get("reasoning_content"), \
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token_ids=output.get("token_ids"), tool_calls=output.get("tool_call_content", []))
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choice = ChatCompletionResponseStreamChoice(
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index=0,
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delta=delta_message,
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logprobs=logprobs_res,
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arrival_time=arrival_time
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)
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if res["finished"]:
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num_choices -= 1
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work_process_metrics.e2e_request_latency.observe(time.time() - res["metrics"]["request_start_time"])
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has_no_token_limit = request.max_tokens is None and request.max_completion_tokens is None
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max_tokens = request.max_completion_tokens or request.max_tokens
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if has_no_token_limit or previous_num_tokens != max_tokens:
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choice.finish_reason = "stop"
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if self.engine_client.reasoning_parser == "ernie_x1" and \
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output.get("finish_reason", "") == "tool_calls":
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choice.finish_reason = "tool_calls"
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else:
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choice.finish_reason = "length"
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if res.get("error_msg") is not None and "Recover" in res["error_msg"]:
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choice.finish_reason = "recover_stop"
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if request.metadata is not None and request.metadata.get("training", False) and delta_text != "":
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choice.delta.token_ids = output["token_ids"]
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=previous_num_tokens,
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total_tokens=num_prompt_tokens + previous_num_tokens
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)
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choices.append(choice)
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if len(choices) == max_streaming_response_tokens or res["finished"]:
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chunk.choices = choices
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yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
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choices = []
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if choices:
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chunk.choices = choices
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yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
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choices = []
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if include_usage:
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completion_tokens = previous_num_tokens
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens + completion_tokens
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)
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[],
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model=model_name,
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usage=usage
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)
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yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
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except Exception as e:
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error_data = self._create_streaming_error_response(str(e))
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yield f"data: {error_data}\n\n"
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finally:
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dealer.close()
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yield "data: [DONE]\n\n"
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async def chat_completion_full_generator(
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self,
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request: ChatCompletionRequest,
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request_id: str,
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model_name: str,
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prompt_token_ids: list()
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):
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"""
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Full chat completion generator.
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"""
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created_time = int(time.time())
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final_res = None
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enable_thinking = None
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include_stop_str_in_output = False
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try:
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dealer = await aiozmq.create_zmq_stream(
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zmq.DEALER,
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connect=f"ipc:///dev/shm/router_{self.pid}.ipc"
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)
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dealer.write([b"", request_id.encode('utf-8')])
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final_res = None
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previous_num_tokens = 0
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current_waiting_time = 0
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logprob_contents = []
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while True:
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try:
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raw_data = await asyncio.wait_for(dealer.read(), timeout=10)
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current_waiting_time = 0
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except asyncio.TimeoutError:
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current_waiting_time += 10
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if current_waiting_time == 300:
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status, msg = self.engine_client.check_health()
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if not status:
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raise ValueError(f"Engine is not healthy: {msg}")
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else:
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current_waiting_time = 0
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await asyncio.sleep(0.1)
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continue
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response = msgpack.unpackb(raw_data[-1])
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task_is_finished = False
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for data in response:
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if data.get("error_code", 200) != 200:
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raise ValueError("{}".format(data["error_msg"]))
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if request.metadata is not None:
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enable_thinking = request.metadata.get("enable_thinking")
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include_stop_str_in_output = request.metadata.get("include_stop_str_in_output", False)
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data = self.engine_client.data_processor.process_response_dict(
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data, stream=False, enable_thinking=enable_thinking, include_stop_str_in_output=include_stop_str_in_output)
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# api_server_logger.debug(f"Client {request_id} received: {data}")
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previous_num_tokens += len(data["outputs"]["token_ids"])
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# The logprob for handling the response
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output = data["outputs"]
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raw_top_logprobs = output["top_logprobs"]
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if raw_top_logprobs is not None:
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top_logprobs = LogprobsLists(
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logprob_token_ids=raw_top_logprobs[0],
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logprobs=raw_top_logprobs[1],
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sampled_token_ranks=raw_top_logprobs[2],
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)
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logprobs_res = self.build_logprobs_response(
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request_logprobs=request.logprobs,
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response_logprobs=top_logprobs,
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request_top_logprobs=request.top_logprobs,
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)
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if logprobs_res and logprobs_res.content is not None:
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logprob_contents.extend(logprobs_res.content)
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if data["finished"]:
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final_res = data
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task_is_finished = True
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break
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if task_is_finished:
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break
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finally:
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dealer.close()
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choices = []
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output = final_res["outputs"]
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message = ChatMessage(
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role="assistant",
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content=output["text"],
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reasoning_content=output.get("reasoning_content"),
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tool_calls=output.get("tool_call_content"),
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token_ids=output.get("token_ids")
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)
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logprobs_full_res = None
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if logprob_contents:
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logprobs_full_res = LogProbs(
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content=logprob_contents
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)
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choice = ChatCompletionResponseChoice(
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index=0,
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message=message,
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logprobs=logprobs_full_res,
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finish_reason=None
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)
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has_no_token_limit = request.max_tokens is None and request.max_completion_tokens is None
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max_tokens = request.max_completion_tokens or request.max_tokens
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if has_no_token_limit or previous_num_tokens != max_tokens:
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choice.finish_reason = "stop"
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if self.engine_client.reasoning_parser == "ernie_x1" and \
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output.get("finish_reason", "") == "tool_calls":
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choice.finish_reason = "tool_calls"
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else:
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choice.finish_reason = "length"
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if final_res.get("error_msg") is not None and "Recover" in final_res["error_msg"]:
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choice.finish_reason = "recover_stop"
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choices.append(choice)
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num_prompt_tokens = len(prompt_token_ids)
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num_generated_tokens = previous_num_tokens
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_generated_tokens,
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total_tokens=num_prompt_tokens + num_generated_tokens,
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prompt_tokens_details=PromptTokenUsageInfo(cached_tokens=final_res.get("num_cached_tokens", 0))
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)
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work_process_metrics.e2e_request_latency.observe(time.time() - final_res["metrics"]["request_start_time"])
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return ChatCompletionResponse(
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id=request_id,
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created=created_time,
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model=model_name,
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choices=choices,
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usage=usage
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)
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def build_logprobs_response(
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self,
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request_logprobs: bool,
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response_logprobs: Optional[LogprobsLists],
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request_top_logprobs: int,
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) -> Optional[LogProbs]:
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"""
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Construct a logprobs response object in line with the OpenAI style.
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Retain the complete top-k candidates and avoid circular references.
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"""
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# Parameter validation
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if (
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response_logprobs is None
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or not request_logprobs
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or request_top_logprobs is None
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or request_top_logprobs < 0
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):
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return None
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try:
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# The top-k candidates for the current token
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topk_token_ids = []
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topk_logprobs = []
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if response_logprobs.logprob_token_ids and len(response_logprobs.logprob_token_ids) > 0:
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topk_token_ids = response_logprobs.logprob_token_ids[0][:request_top_logprobs + 1]
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if response_logprobs.logprobs and len(response_logprobs.logprobs) > 0:
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topk_logprobs = response_logprobs.logprobs[0][:request_top_logprobs + 1]
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# Construct the candidate token structure (LogProbEntry) of topk
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top_logprob_entries: List[LogProbEntry] = []
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for tid, lp in zip(topk_token_ids, topk_logprobs):
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token_str = self.engine_client.data_processor.process_logprob_response([tid],
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clean_up_tokenization_spaces=False)
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# token_bytes = token_str.encode("utf-8", errors="replace")
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entry = LogProbEntry(
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token=token_str,
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logprob=lp,
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# bytes=list(token_bytes)
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)
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top_logprob_entries.append(entry)
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# Construct the sampled token object (avoid sharing references with top_logprob_entries)
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sampled_entry = LogProbEntry(
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token=top_logprob_entries[0].token,
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logprob=top_logprob_entries[0].logprob,
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bytes=top_logprob_entries[0].bytes,
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top_logprobs=top_logprob_entries[1:] # Here are the complete topk candidates
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)
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return LogProbs(content=[sampled_entry])
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except Exception as e:
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api_server_logger.error("Error in build_logprobs_response: %s", e)
|
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api_server_logger.error(traceback.format_exc())
|
|
return None
|