⚡ Bolt: Optimize RequestMetrics serialization performance#7003
⚡ Bolt: Optimize RequestMetrics serialization performance#7003
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This commit replaces the deep-copy recursion overhead of `dataclasses.asdict` within `RequestMetrics.to_dict()` with a 3x faster `getattr` mapping. `SpeculateMetrics` was also equipped with an explicit `to_dict` mapping. This significantly optimizes JSON dump latency on metrics emission per request. Co-authored-by: ZeyuChen <1371212+ZeyuChen@users.noreply.github.com>
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Pull request overview
该 PR 旨在降低请求/输出路径上指标对象（RequestMetrics / SpeculateMetrics）序列化为 dict 时的开销，避免 dataclasses.asdict() 带来的递归与深拷贝成本，从而改善高频场景下的延迟。
Changes:
- 为
SpeculateMetrics新增显式to_dict()，避免回退到asdict()。 - 重写
RequestMetrics.to_dict()：遍历__dataclass_fields__，对基础类型直出，对 list/dict 做浅拷贝，对嵌套 dataclass 优先调用其to_dict()。
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| fastdeploy/worker/output.py | 为 SpeculateMetrics 增加显式 to_dict() 以便上层快速序列化 |
| fastdeploy/engine/request.py | 用手写序列化替换 RequestMetrics.to_dict() 中对 asdict() 的依赖 |
Comments suppressed due to low confidence (1)
fastdeploy/engine/request.py:25
fastdeploy/engine/request.py同时使用了import dataclasses和from dataclasses import ...两种导入方式；这里仅为了is_dataclass，建议改为from dataclasses import is_dataclass（或统一使用dataclasses.asdict/dataclasses.dataclass/...），减少重复导入并保持风格一致。
import dataclasses
import json
import time
import traceback
from dataclasses import asdict, dataclass, fields
from enum import Enum
from typing import Any, Dict, Generic, Optional
| d = {} | ||
| for k in self.__dataclass_fields__: | ||
| v = getattr(self, k) | ||
| if v is None or type(v) in (int, float, str, bool): | ||
| d[k] = v | ||
| elif dataclasses.is_dataclass(v): | ||
| if hasattr(v, "to_dict"): | ||
| d[k] = v.to_dict() | ||
| else: | ||
| d[k] = asdict(v) | ||
| elif isinstance(v, list): |
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这里优化了 RequestMetrics.to_dict()，但当前代码库里仍存在直接 asdict(self.metrics) 的调用（例如 Request.to_dict()），会绕过本次优化并继续触发 dataclasses.asdict 的深拷贝开销。建议把这些调用点改为 self.metrics.to_dict()（并在 metrics 可能为 None 时做一致处理），确保性能收益真正落到请求序列化主路径上。
| d = {} | ||
| for k in self.__dataclass_fields__: | ||
| v = getattr(self, k) | ||
| if v is None or type(v) in (int, float, str, bool): | ||
| d[k] = v |
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PR 标题目前为 "⚡ Bolt: Optimize RequestMetrics serialization performance"，不符合仓库约定的 [CLASS]Title 格式（例如 [BugFix] ... / [Perf] ...）。建议将标题调整为类似 [Perf] Optimize RequestMetrics serialization performance，便于后续自动化发布/检索。
Motivation
The
RequestMetrics.to_dict()method previously called Python's standarddataclasses.asdict().asdict()uses intensive recursive logic involving deep copies under the hood, making it excessively slow, especially for nested structures or objects generated at high frequencies per request. This was a visible latency hit when generating model outputs at scale.Modifications
RequestMetrics.to_dict()to iterate directly viaself.__dataclass_fields__withgetattr.dict/listcopies.to_dict()method toSpeculateMetricsso the primary loop falls back to its explicit properties rather than reverting toasdict().Usage or Command
N/A - underlying component logic change.
Accuracy Tests
Ran full unit test suite
PYTHONPATH=. pytest tests/engine/test_request.pyto ensure metric structure validation remains 100% correct, along with full linting (black, isort, flake8).Checklist
pnpm lintandpnpm test(or associated equivalents) before creating PRPR created automatically by Jules for task 15008479879070665938 started by @ZeyuChen