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7337729e57
* fix(studio/llama_cpp): disable trust_env on the loopback health probe _wait_for_health() polls http://127.0.0.1:<port>/health with the default httpx trust_env=True, so an ambient HTTP(S)_PROXY in the environment is applied to the loopback request. A proxy that returns 503 for 127.0.0.1 makes every probe fail, so the loop runs until timeout and Studio load hangs (trust_env=False returns 200 immediately). Pass trust_env=False so the local readiness probe never goes through a proxy. This mirrors the existing trust_env=False handling in the sibling llama_http / external_provider HTTP clients. * test(offline_gguf_cache): accept trust_env kwarg in fake_get mock _wait_for_health now calls httpx.get(..., trust_env=False); update the retry test's fake_get to accept the kwarg so it doesn't raise TypeError. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(studio/llama_cpp): bypass proxies for loopback clients * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(studio/routes): bypass proxies for llama streams * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: wasimysaid <wasimysdev@gmail.com>
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""_model_json_response produces the same body as JSONResponse(model.model_dump())."""
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import asyncio
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import json
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from typing import Optional
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel
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import routes.inference as inference_route
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from core.inference import llama_http
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class _Usage(BaseModel):
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prompt_tokens: int = 3
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completion_tokens: int = 5
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details: Optional[dict] = None
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class _Choice(BaseModel):
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index: int = 0
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text: str = "hello"
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logprobs: Optional[dict] = None
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class _Resp(BaseModel):
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id: str = "chatcmpl-abc"
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object: str = "chat.completion"
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created: int = 1700000000
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model: str = "unsloth/SmolLM2-135M-Instruct-GGUF"
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choices: list[_Choice] = [_Choice()]
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usage: _Usage = _Usage()
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system_fingerprint: Optional[str] = None
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def _old_body(model) -> bytes:
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# What the previous code emitted: dict -> Starlette json.dumps.
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return JSONResponse(content = model.model_dump()).body
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def test_body_matches_old_jsonresponse():
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model = _Resp()
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resp = inference_route._model_json_response(model)
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# Same decoded JSON (key order is irrelevant once parsed), nulls preserved.
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assert json.loads(resp.body) == json.loads(_old_body(model))
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assert json.loads(resp.body)["system_fingerprint"] is None # null kept, not dropped
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def test_media_type_and_status():
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resp = inference_route._model_json_response(_Resp(), status_code = 200)
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assert resp.media_type == "application/json"
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assert resp.status_code == 200
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err = inference_route._model_json_response(_Resp(), status_code = 503)
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assert err.status_code == 503
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def test_pooled_client_disables_proxy_env():
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async def _scenario():
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client = llama_http.nonstreaming_client()
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assert client.trust_env is False
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await llama_http.aclose()
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asyncio.run(_scenario())
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def test_pooled_client_reused_within_loop_and_recreated_after_close():
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async def _scenario():
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a = llama_http.nonstreaming_client()
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b = llama_http.nonstreaming_client()
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assert a is b # reused within one loop
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await llama_http.aclose()
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assert a.is_closed
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c = llama_http.nonstreaming_client() # must not return the closed client
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assert c is not a and not c.is_closed
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await llama_http.aclose()
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asyncio.run(_scenario())
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def test_pooled_client_is_per_event_loop():
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clients = []
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# Each asyncio.run uses a fresh loop; the pooled client must not leak across.
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for _ in range(2):
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async def _grab():
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clients.append(llama_http.nonstreaming_client())
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await llama_http.aclose()
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asyncio.run(_grab())
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assert clients[0] is not clients[1]
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