mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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dade19d7a4
* [Feature] support logprobs in chat/completions and completions endpoints * Temporarily comment out text_offset due to incorrect logic * Clean up temporary debug prints * [Feature] support logprobs in offline mode via SamplingParams * fix: serialize Logprob as dict before zmq send to fix msgpack error * refactor: remove redundant methods to simplify codebase * Fix missing fields in CompletionOutput.to_dict affecting msgpack serialization * refactor: centralize param validation in engine_client to reduce duplication * revert: rollback changes in offline_demo.py * revert: rollback changes in offline_demo.py * [bugfix] fix parameter validation for logprobs * [bugfix] fix parameter validation for logprobs * [bugfix] fix parameter validation for logprobs * [bugfix] fix parameter validation for logprobs --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
551 lines
24 KiB
Python
551 lines
24 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 time
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import uuid
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from typing import List, Optional
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import aiozmq
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import msgpack
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import numpy as np
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from aiozmq import zmq
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from fastdeploy.engine.request import RequestOutput
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from fastdeploy.entrypoints.openai.protocol import (
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CompletionLogprobs,
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CompletionRequest,
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CompletionResponse,
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CompletionResponseChoice,
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CompletionResponseStreamChoice,
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CompletionStreamResponse,
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ErrorResponse,
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UsageInfo,
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)
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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 OpenAIServingCompletion:
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def __init__(self, engine_client, pid, ips):
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self.engine_client = engine_client
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self.pid = pid
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self.master_ip = ips
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self.host_ip = get_host_ip()
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if self.master_ip is not None:
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if isinstance(self.master_ip, list):
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self.master_ip = self.master_ip[0]
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else:
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self.master_ip = self.master_ip.split(",")[0]
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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_completion(self, request: CompletionRequest):
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"""
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Create a completion for the given prompt.
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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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created_time = int(time.time())
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if request.user is not None:
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request_id = f"cmpl-{request.user}-{uuid.uuid4()}"
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else:
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request_id = f"cmpl-{uuid.uuid4()}"
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api_server_logger.info(f"initialize request {request_id}")
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request_prompt_ids = None
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request_prompts = None
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try:
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if isinstance(request.prompt, str):
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request_prompts = [request.prompt]
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elif isinstance(request.prompt, list) and all(isinstance(item, int) for item in request.prompt):
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request_prompt_ids = [request.prompt]
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elif isinstance(request.prompt, list) and all(isinstance(item, str) for item in request.prompt):
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request_prompts = request.prompt
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elif isinstance(request.prompt, list):
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for item in request.prompt:
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if isinstance(item, list) and all(isinstance(x, int) for x in item):
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continue
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else:
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raise ValueError("Prompt must be a string, a list of strings or a list of integers.")
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request_prompt_ids = request.prompt
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else:
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raise ValueError("Prompt must be a string, a list of strings or a list of integers.")
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except Exception as e:
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return ErrorResponse(message=str(e), code=400)
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if request_prompt_ids is not None:
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request_prompts = request_prompt_ids
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num_choices = len(request_prompts)
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api_server_logger.info(f"start inference for request {num_choices}")
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prompt_batched_token_ids = []
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try:
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for idx, prompt in enumerate(request_prompts):
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request_id_idx = f"{request_id}-{idx}"
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current_req_dict = request.to_dict_for_infer(request_id_idx, prompt)
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try:
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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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if isinstance(prompt_token_ids, np.ndarray):
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prompt_token_ids = prompt_token_ids.tolist()
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prompt_batched_token_ids.append(prompt_token_ids)
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except Exception as e:
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return ErrorResponse(message=str(e), code=400)
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del current_req_dict
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if request.stream:
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return self.completion_stream_generator(
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request=request,
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num_choices=num_choices,
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request_id=request_id,
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created_time=created_time,
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model_name=request.model,
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prompt_batched_token_ids=prompt_batched_token_ids,
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)
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else:
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try:
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return await self.completion_full_generator(
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request=request,
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num_choices=num_choices,
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request_id=request_id,
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created_time=created_time,
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model_name=request.model,
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prompt_batched_token_ids=prompt_batched_token_ids,
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)
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except Exception as e:
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return ErrorResponse(code=400, message=str(e))
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except Exception as e:
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return ErrorResponse(message=str(e), code=400)
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async def completion_full_generator(
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self,
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request: CompletionRequest,
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num_choices: int,
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request_id: str,
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created_time: int,
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model_name: str,
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prompt_batched_token_ids: list(),
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):
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"""
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Process the full completion request with multiple choices.
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"""
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dealer = None
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try:
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request_ids = [f"{request_id}-{i}" for i in range(num_choices)]
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# create dealer
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dealer = await aiozmq.create_zmq_stream(zmq.DEALER, connect=f"ipc:///dev/shm/router_{self.pid}.ipc")
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for rid in request_ids:
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dealer.write([b"", rid.encode("utf-8")])
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valid_results = [dict()] * num_choices
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output_tokens = [0] * num_choices
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aggregated_top_logprobs = [[[], [], []]] * num_choices
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aggregated_token_ids = [[]] * num_choices
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completion_batched_token_ids = [[] for _ in range(num_choices)]
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current_waiting_time = 0
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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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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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for data in response:
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rid = int(data["request_id"].split("-")[-1])
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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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output = data["outputs"]
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output_top_logprobs = output["top_logprobs"]
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if output_top_logprobs is not None:
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aggregated_top_logprobs[rid][0].extend(output_top_logprobs[0])
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aggregated_top_logprobs[rid][1].extend(output_top_logprobs[1])
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aggregated_top_logprobs[rid][2].extend(output_top_logprobs[2])
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aggregated_token_ids[rid].extend(data["outputs"]["token_ids"])
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self.engine_client.data_processor.process_response_dict(
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data, stream=False, include_stop_str_in_output=request.include_stop_str_in_output
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)
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output_tokens[rid] += len(data["outputs"]["token_ids"])
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completion_batched_token_ids[rid].extend(data["outputs"]["token_ids"])
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if data.get("finished", False):
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data["output_token_ids"] = output_tokens[rid]
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data["outputs"]["top_logprobs"] = aggregated_top_logprobs[rid]
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data["outputs"]["token_ids"] = aggregated_token_ids[rid]
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valid_results[rid] = data
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num_choices -= 1
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break
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return self.request_output_to_completion_response(
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final_res_batch=valid_results,
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request=request,
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request_id=request_id,
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created_time=created_time,
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model_name=model_name,
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prompt_batched_token_ids=prompt_batched_token_ids,
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completion_batched_token_ids=completion_batched_token_ids,
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)
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except Exception as e:
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api_server_logger.error(f"Error in completion_full_generator: {e}", exc_info=True)
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raise
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finally:
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if dealer is not None:
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dealer.close()
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async def completion_stream_generator(
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self,
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request: CompletionRequest,
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num_choices: int,
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request_id: str,
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created_time: int,
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model_name: str,
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prompt_batched_token_ids: list(),
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):
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"""
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Process the stream completion request.
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"""
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try:
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dealer = await aiozmq.create_zmq_stream(zmq.DEALER, connect=f"ipc:///dev/shm/router_{self.pid}.ipc")
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for i in range(num_choices):
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req_id = f"{request_id}-{i}"
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dealer.write([b"", req_id.encode("utf-8")]) # 发送多路请求
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output_tokens = [0] * num_choices
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inference_start_time = [0] * num_choices
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first_iteration = [True] * num_choices
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max_streaming_response_tokens = (
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request.max_streaming_response_tokens
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if request.max_streaming_response_tokens is not None
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else (request.suffix or {}).get("max_streaming_response_tokens", 1)
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) # dierctly passed & passed in suffix
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choices = []
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chunk = CompletionStreamResponse(
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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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)
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current_waiting_time = 0
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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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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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for res in response:
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idx = int(res["request_id"].split("-")[-1])
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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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if first_iteration[idx]:
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if request.return_token_ids:
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chunk = CompletionStreamResponse(
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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=[
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CompletionResponseStreamChoice(
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index=idx,
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text="",
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prompt_token_ids=list(prompt_batched_token_ids[idx]),
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completion_token_ids=None,
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)
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],
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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[idx] = False
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self.engine_client.data_processor.process_response_dict(
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res, stream=True, include_stop_str_in_output=request.include_stop_str_in_output
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)
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if res["metrics"].get("first_token_time") is not None:
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arrival_time = res["metrics"]["first_token_time"]
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inference_start_time[idx] = res["metrics"]["inference_start_time"]
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else:
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arrival_time = res["metrics"]["arrival_time"] - inference_start_time[idx]
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output = res["outputs"]
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output_top_logprobs = output["top_logprobs"]
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logprobs_res: Optional[CompletionLogprobs] = None
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if request.logprobs and output_top_logprobs is not None:
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logprobs_res = self._create_completion_logprobs(output_top_logprobs, request.logprobs, 0)
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choices.append(
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CompletionResponseStreamChoice(
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index=idx,
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text=output["text"],
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prompt_token_ids=None,
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completion_token_ids=output.get("token_ids") if request.return_token_ids else None,
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tool_calls=output.get("tool_call_content"),
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reasoning_content=output.get("reasoning_content"),
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arrival_time=arrival_time,
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logprobs=logprobs_res,
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)
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)
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if res["finished"]:
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if request.max_tokens is None or output_tokens[idx] + 1 != request.max_tokens:
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chunk.choices[0].finish_reason = "stop"
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if (
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self.engine_client.reasoning_parser == "ernie_x1"
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and output.get("finish_reason", "") == "tool_calls"
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):
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chunk.choices[0].finish_reason = "tool_calls"
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else:
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chunk.choices[0].finish_reason = "length"
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output_tokens[idx] += 1
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if len(choices) == max_streaming_response_tokens or res["finished"]:
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chunk = CompletionStreamResponse(
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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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)
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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 res["finished"]:
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num_choices -= 1
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if getattr(request, "stream_options", None) and request.stream_options.include_usage:
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usage_chunk = CompletionStreamResponse(
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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=[],
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usage=UsageInfo(
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prompt_tokens=len(prompt_batched_token_ids[idx]),
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completion_tokens=output_tokens[idx],
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),
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)
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yield f"data: {usage_chunk.model_dump_json(exclude_unset=True)}\n\n"
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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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except Exception as e:
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yield f"data: {ErrorResponse(message=str(e), code=400).model_dump_json(exclude_unset=True)}\n\n"
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finally:
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del request
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if dealer is not None:
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dealer.close()
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yield "data: [DONE]\n\n"
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def request_output_to_completion_response(
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self,
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final_res_batch: List[RequestOutput],
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request: CompletionRequest,
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request_id: str,
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created_time: int,
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model_name: str,
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prompt_batched_token_ids: list(),
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completion_batched_token_ids: list(),
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) -> CompletionResponse:
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choices: List[CompletionResponseChoice] = []
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num_prompt_tokens = 0
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num_generated_tokens = 0
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aggregated_logprobs: Optional[CompletionLogprobs] = None
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for idx in range(len(final_res_batch)):
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final_res = final_res_batch[idx]
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prompt_token_ids = prompt_batched_token_ids[idx]
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assert prompt_token_ids is not None
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prompt_text = final_res["prompt"]
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completion_token_ids = completion_batched_token_ids[idx]
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output = final_res["outputs"]
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output_top_logprobs = output["top_logprobs"]
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if output_top_logprobs is not None:
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logprobs_res = self._create_completion_logprobs(output_top_logprobs, request.logprobs, 0)
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if aggregated_logprobs is None:
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aggregated_logprobs = logprobs_res
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else:
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aggregated_logprobs.tokens.extend(logprobs_res.tokens)
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aggregated_logprobs.token_logprobs.extend(logprobs_res.token_logprobs)
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aggregated_logprobs.top_logprobs.extend(logprobs_res.top_logprobs)
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aggregated_logprobs.text_offset.extend(logprobs_res.text_offset)
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if request.echo:
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assert prompt_text is not None
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if request.max_tokens == 0:
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token_ids = prompt_token_ids
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output_text = prompt_text
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else:
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token_ids = [*prompt_token_ids, *output["token_ids"]]
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output_text = prompt_text + output["text"]
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else:
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token_ids = output["token_ids"]
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output_text = output["text"]
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choice_data = CompletionResponseChoice(
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token_ids=token_ids,
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index=len(choices),
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text=output_text,
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prompt_token_ids=prompt_token_ids if request.return_token_ids else None,
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completion_token_ids=completion_token_ids if request.return_token_ids else None,
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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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logprobs=aggregated_logprobs,
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finish_reason=None,
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)
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choices.append(choice_data)
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num_generated_tokens += final_res["output_token_ids"]
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num_prompt_tokens += len(prompt_token_ids)
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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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)
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del request
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return CompletionResponse(
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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 _create_completion_logprobs(
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self,
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output_top_logprobs,
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request_logprobs: Optional[int] = None,
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prompt_text_offset: Optional[int] = None,
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) -> Optional[CompletionLogprobs]:
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"""Create OpenAI-style logprobs for completions."""
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# Parameter validation
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if output_top_logprobs is None or len(output_top_logprobs) < 3 or any(not lst for lst in output_top_logprobs):
|
|
return None
|
|
|
|
logprobs_res: Optional[CompletionLogprobs] = None
|
|
# Iterate over the top-k candidates for each token
|
|
for logprob_token_ids, logprobs, sampled_token_ranks in zip(
|
|
output_top_logprobs[0], output_top_logprobs[1], output_top_logprobs[2]
|
|
):
|
|
top_logprobs = LogprobsLists(
|
|
logprob_token_ids=[logprob_token_ids],
|
|
logprobs=[logprobs],
|
|
sampled_token_ranks=[sampled_token_ranks],
|
|
)
|
|
# Build the logprobs response
|
|
step_logprobs_res = self._build_logprobs_response(
|
|
response_logprobs=top_logprobs,
|
|
request_top_logprobs=request_logprobs,
|
|
prompt_text_offset=prompt_text_offset,
|
|
)
|
|
if logprobs_res is None:
|
|
logprobs_res = step_logprobs_res
|
|
else:
|
|
# Append the new tokens to the existing logprobs response
|
|
logprobs_res.tokens.extend(step_logprobs_res.tokens)
|
|
logprobs_res.token_logprobs.extend(step_logprobs_res.token_logprobs)
|
|
logprobs_res.top_logprobs.extend(step_logprobs_res.top_logprobs)
|
|
|
|
return logprobs_res
|
|
|
|
def _build_logprobs_response(
|
|
self,
|
|
response_logprobs: Optional[LogprobsLists] = None,
|
|
request_top_logprobs: Optional[int] = None,
|
|
prompt_text_offset: Optional[int] = None,
|
|
) -> Optional[CompletionLogprobs]:
|
|
"""
|
|
Construct a logprobs response object in line with the OpenAI style.
|
|
Retain the complete top-k candidates and avoid circular references.
|
|
"""
|
|
|
|
# Parameter validation
|
|
if response_logprobs is None or request_top_logprobs is None or request_top_logprobs < 0:
|
|
return None
|
|
|
|
try:
|
|
# The top-k candidates for the current token
|
|
topk_token_ids = []
|
|
topk_logprobs = []
|
|
|
|
if response_logprobs.logprob_token_ids and len(response_logprobs.logprob_token_ids) > 0:
|
|
topk_token_ids = response_logprobs.logprob_token_ids[0][: request_top_logprobs + 1]
|
|
|
|
if response_logprobs.logprobs and len(response_logprobs.logprobs) > 0:
|
|
topk_logprobs = response_logprobs.logprobs[0][: request_top_logprobs + 1]
|
|
|
|
# Construct the sampled token object (avoid sharing references with top_logprob_entries)
|
|
tokens = []
|
|
token_logprobs = []
|
|
top_logprobs = {}
|
|
idx = 0
|
|
for tid, lp in zip(topk_token_ids, topk_logprobs):
|
|
token_str = self.engine_client.data_processor.process_logprob_response(
|
|
[tid], clean_up_tokenization_spaces=False
|
|
)
|
|
if "\ufffd" in token_str:
|
|
token_bytes = token_str.encode("utf-8", errors="replace")
|
|
token_str = "bytes:" + "".join(f"\\x{byte:02x}" for byte in token_bytes)
|
|
if idx == 0:
|
|
tokens.append(token_str)
|
|
token_logprobs.append(lp)
|
|
else:
|
|
top_logprobs[token_str] = lp
|
|
idx += 1
|
|
|
|
# Construct the sampled token object (avoid sharing references with top_logprob_entries)
|
|
# text_offset = prompt_text_offset + len(tokens) - 1
|
|
return CompletionLogprobs(
|
|
tokens=tokens,
|
|
token_logprobs=token_logprobs,
|
|
top_logprobs=[top_logprobs],
|
|
# text_offset=[text_offset],
|
|
)
|
|
|
|
except Exception as e:
|
|
api_server_logger.error("Error in _build_logprobs_response: %s", e)
|
|
return None
|