Align OpenAI serving behavior with Para deployments
Absorb PR 11's final Para compatibility surface as an opt-in OpenAI serving layer rather than hard-coding business defaults into protocol models. The change adds server args for Para chat defaults, Kimi/GLM compatibility, tool-choice normalization, tool-role text flattening, and streaming first-chunk error preflight while preserving default upstream behavior unless explicitly enabled. Reasoning token usage is also propagated through chat/completion usage paths, with GLM compatibility emitting completion_tokens_details.reasoning_tokens. Low-risk protocol fixes accept string image_url content parts and preserve GLM function-call argument value whitespace. Constraint: Online Para-compatible deployments require request/response semantics that differ from default OpenAI serving behavior. Constraint: Current CP/HiCache/bs>1 work must not be coupled to OpenAI serving compatibility changes. Rejected: Merge PR 11 history directly | intermediate commits briefly hard-code chat max_tokens=32768 before later gating it by server args. Rejected: Enable Para compatibility by default | would change non-Para OpenAI-compatible deployments. Confidence: high Scope-risk: moderate Directive: Keep Para-specific serving policies behind explicit server args unless the business contract changes globally. Tested: PYTHONPATH=python:. python -m unittest discover -s test/registered/unit/entrypoints/openai -p 'test_para_serving_protocol.py' -v (19 tests OK) Tested: python -m py_compile modified OpenAI serving, tokenizer manager, server_args, function-call detector, and test files Not-tested: Live router/prefill/decode OpenAI serving E2E after enabling Para flags. Co-authored-by: OmX <omx@oh-my-codex.dev>
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laoyao0822
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docs/references/para_openai_serving_alignment.md
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docs/references/para_openai_serving_alignment.md
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# Para OpenAI Serving 对齐说明
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本文总结当前分支对 `sglang-para` OpenAI API / serving 相关行为的对齐范围、默认策略和启用方式。
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## 总体策略
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本分支已经实现 Para 侧高风险 serving 行为，但为了降低耦合，**默认不启用 Para 兼容策略**。需要对齐 Para 业务行为时，通过 server args 显式打开。
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默认保持上游/本分支原行为：
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- chat 请求不自动填 `max_tokens=32768`。
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- 不强制把 `tool_choice` 改为 `auto`。
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- 不按模型路径自动启用 Kimi / GLM 特化逻辑。
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- 不默认 flatten tool role 的 content list。
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- streaming 不默认预拉首 chunk 做 HTTP error 转换。
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## 启用 Para 兼容的参数
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完整启用 Para OpenAI serving 对齐时，可在启动参数中加入：
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```bash
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--openai-chat-default-max-tokens 32768 \
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--openai-force-tool-choice-auto \
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--openai-kimi-compat \
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--openai-glm-compat \
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--openai-flatten-tool-role-text-content \
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--openai-streaming-error-preflight
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```
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参数含义：
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| 参数 | 默认值 | 启用后行为 |
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| ----------------------------------------- | ------- | ----------------------------------------------------------------------------------------- |
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| `--openai-chat-default-max-tokens 32768` | `0` | chat 请求未传 `max_tokens/max_completion_tokens` 时，在 serving 层补默认输出上限。 |
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| `--openai-force-tool-choice-auto` | `False` | 将显式非 `auto` 的 chat `tool_choice` 按 `auto` 服务。 |
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| `--openai-kimi-compat` | `False` | 根据 `model_path` 识别 Kimi，启用 Kimi `thinking` 映射和固定采样参数。 |
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| `--openai-glm-compat` | `False` | 根据 `model_path` 识别 GLM，启用 GLM tool choice 降级、413 超长错误和 GLM usage details。 |
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| `--openai-flatten-tool-role-text-content` | `False` | 将 tool role 的纯 text content parts list flatten 成字符串后交给 chat template。 |
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| `--openai-streaming-error-preflight` | `False` | streaming 先预拉首 chunk；若首 chunk 是错误，转成普通 HTTP error，而不是 SSE 内报错。 |
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## 已对齐的功能点
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### 1. Chat 默认输出长度
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Para 行为：未传 `max_tokens/max_completion_tokens` 时默认 `32768`。
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当前实现：
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- `ChatCompletionRequest.max_tokens` 仍保持 `None`，避免协议模型硬编码业务默认值。
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- serving 层读取 `openai_chat_default_max_tokens`。
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- 传 `--openai-chat-default-max-tokens 32768` 后，效果与 Para 一致。
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### 2. Kimi 兼容
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启用 `--openai-kimi-compat` 后：
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- 支持 chat 请求中的 `thinking` 字段。
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- Kimi 模型下将 `thinking.type != "disabled"` 映射到 `chat_template_kwargs["thinking"]`。
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- 对 Kimi 应用 Para 固定采样参数：
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- `top_p=0.95`
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- `presence_penalty=0.0`
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- `frequency_penalty=0.0`
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- `n=1`
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### 3. GLM 兼容
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启用 `--openai-glm-compat` 后：
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- 根据 `model_path` 包含 `glm` 识别 GLM。
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- GLM 下 `tool_choice="required"` 降级为 `"auto"`。
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- GLM input token 已超过 context length 时，抛 `PayloadTooLargeError`，OpenAI serving 返回 HTTP `413`。
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- GLM chat usage 使用 `completion_tokens_details.reasoning_tokens`。
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未启用时，GLM 超长输入仍走普通 `ValueError -> HTTP 400` 路径。
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### 4. Tool choice 全局兼容
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启用 `--openai-force-tool-choice-auto` 后：
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- chat 请求中显式非 `auto` 的 `tool_choice` 会在 serving 层转为 `auto`。
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- 协议层不再改写 `tool_choice`，便于关闭该行为并降低耦合。
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### 5. Tool role content flatten
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启用 `--openai-flatten-tool-role-text-content` 后：
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- 对 `role="tool"` 且 content 是纯 text parts list 的消息，将内容拼成字符串。
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- 仅 flatten 纯 text parts；包含其他结构字段的 list 保持原样，避免破坏依赖结构化 tool content 的模板。
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### 6. GLM function call 参数保留空格
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无条件对齐 Para：
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- `glm4_moe_detector.py`
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- `glm47_moe_detector.py`
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两处 detector 不再对参数值执行 `strip()`，只 strip 参数 key。这样可以保留模型输出或客户端参数值里的前后空格。
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### 7. `image_url` 字符串兼容
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无条件对齐 Para：
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- 支持 OpenAI multimodal content part 中 `image_url` 直接传字符串。
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- Pydantic validator 自动转成 `{ "url": ... }`。
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### 8. Streaming 首包错误转换
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启用 `--openai-streaming-error-preflight` 后：
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- chat streaming 会先拉取首个 chunk。
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- 如果首 chunk 是 SSE error payload，会转成普通 HTTP error response。
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- 如果首 chunk 正常，会 prepend 回 stream，不影响正常 streaming。
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### 9. Usage / reasoning tokens
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已对齐 Para usage 聚合能力：
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- `UsageInfo` 增加 `completion_tokens_details`。
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- `UsageProcessor` 聚合 `reasoning_tokens`。
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- Chat/Completion serving 都向 usage processor 传递 reasoning token 计数。
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- 启用 GLM compat 后，GLM chat 使用 `completion_tokens_details.reasoning_tokens`。
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## 主要改动文件
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- `python/sglang/srt/server_args.py`
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- `python/sglang/srt/entrypoints/openai/protocol.py`
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- `python/sglang/srt/entrypoints/openai/serving_chat.py`
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- `python/sglang/srt/entrypoints/openai/serving_base.py`
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- `python/sglang/srt/entrypoints/openai/usage_processor.py`
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- `python/sglang/srt/entrypoints/openai/serving_completions.py`
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- `python/sglang/srt/managers/tokenizer_manager.py`
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- `python/sglang/srt/function_call/glm4_moe_detector.py`
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- `python/sglang/srt/function_call/glm47_moe_detector.py`
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- `test/registered/unit/entrypoints/openai/test_para_serving_protocol.py`
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## 验证
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本地 macOS 使用 uv 虚拟环境完成轻量单测验证：
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```bash
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VIRTUAL_ENV=/private/tmp/sglang-para-test-venv \
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PATH=/private/tmp/sglang-para-test-venv/bin:$PATH \
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UV_CACHE_DIR=/private/tmp/uv-cache \
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PYTHONDONTWRITEBYTECODE=1 \
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PYTHONPATH=python:. \
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uv run --active --no-sync python -m unittest discover \
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-s test/registered/unit/entrypoints/openai \
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-p 'test_para_serving_protocol.py' -v
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```
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结果：`Ran 19 tests ... OK`。
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同时执行：
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```bash
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govctl check
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```
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结果：通过。
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