unsloth/studio/backend/tests/test_openai_tool_passthrough.py
Daniel Han d79fd92798
studio: scope cancel-cleanup to in-flight tmp dirs; walk back tool_call_id (#5488)
* studio: scope cancel-cleanup to in-flight tmp dirs; walk back tool_call_id

Two follow-ups to #5375's training and chat hardening.

_cleanup_cancelled_checkpoints used to rmtree every checkpoint-N
directory on Cancel. That is the opposite of what the user expects.
A user cancelling an 8h run with save_steps=2000 loses every
completed checkpoint they could have resumed from. The 67 MB residue
the audit memo flagged is the HF Trainer atomic-rename partial
(tmp-checkpoint-N), not the completed ones. The cleanup now targets
only tmp-checkpoint subdirs; completed checkpoint-N directories are
user-owned and stay. Symlinked output_dir and symlinked children are
skipped so the realpath containment cannot be levered into deleting
arbitrary content via a symlink trick.

ChatMessage._validate_role_shape stamped a random secrets.token_hex
id on tool messages with no tool_call_id. That id is uncorrelated
with the prior assistant tool_calls id, so strict passthrough
backends (OpenAI, Anthropic) reject the request as orphaned and
llama.cpp treats the tool result as "no preceding call" and
hallucinates. The synthesis moves up to ChatCompletionRequest, where
the whole conversation is visible: for each tool message missing an
id we walk back to the most recent assistant turn with tool_calls
(stopping at user turns), prefer a function.name match, otherwise
take the first unconsumed tool_call. Synthesis is the fallback when
no candidate assistant turn exists, preserving the prior round-trip
guarantee for orphaned tool messages.

Tests:
  - test_cleanup_cancelled_checkpoints.py (new): pins that completed
    checkpoint subdirs survive, tmp-checkpoint partials are removed,
    non-int suffixes (checkpoint-final, checkpoint-best) are left
    alone, output_dir outside outputs_root is refused, symlinked
    output_dir and symlinked child are both skipped, missing dir is
    a no-op.
  - test_inference_model_validation.py: 6 new walkback cases covering
    name-match preference, first-unconsumed fallback, explicit-id
    passthrough, multi-tool-result pairing, synth-on-no-parent, and
    no-cross-user-turn invariant.
  - test_openai_tool_passthrough.py: the two ChatMessage-level
    synth-on-missing tests are rewritten to assert that the per-
    message validator now leaves tool_call_id untouched; resolution
    coverage lives in the request-level tests above.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* studio: explicit tool_call_id reserve, numeric tmp-checkpoint suffix only

Reviewer follow-ups to the training-cleanup + tool_call_id walkback PR.

tool_call_id walkback: a mixed assistant turn with [call_a, call_b]
followed by a tool result that carried tool_call_id="call_a" and a
sibling tool result with no id resolved to ['call_a', 'call_a']
because the explicit id never reserved call_a in the consumed set.
Added a pre-pass over the message list that walks back from every
role="tool" message carrying an explicit id and marks the matching
(asst_idx, tc_idx) consumed, then the missing-id walkback runs against
that pre-populated set. The second result now resolves to call_b.

While here, also harden the function-shape check: if a provider
ships a malformed tool_call where `function` is a string rather than
a dict, the old `(tc.get("function") or {}).get("name")` raised
AttributeError on the string's .get; now isinstance-gated so the
walkback falls through to the fallback id without raising.

Cancel cleanup: `tmp-checkpoint-*` is too broad. HF Trainer's
in-flight partials are always `tmp-checkpoint-<integer-step>`, so
constrain the cleanup regex to `^tmp-checkpoint-\d+$`. A user folder
named `tmp-checkpoint-final`, `tmp-checkpoint-backup`, or
`tmp-checkpoint-user-notes` is now preserved.

ChatMessage docstring still pointed at the pre-PR contract that
required `tool_call_id` on every role="tool" message. Updated to say
missing ids are accepted at message scope and resolved at
ChatCompletionRequest scope. Inline comment above the cancel-cleanup
call now describes the actual behaviour (in-flight tmp partials,
completed checkpoints preserved).

Test:
  - python -m pytest studio/backend/tests/test_inference_model_validation.py
    studio/backend/tests/test_cleanup_cancelled_checkpoints.py
    studio/backend/tests/test_openai_tool_passthrough.py -q
    -> 76 passed (was 67 before this commit; +2 walkback regression
       tests, +1 numeric-suffix preservation test)

* studio: trim verbose comments in cleanup + tool_call_id walkback

Move the HF tmp-checkpoint regex to module scope as a named constant.
Drop the multi-paragraph docstring on _cleanup_cancelled_checkpoints
and the inline call-site rationale; the function name + the test
class already cover the why.

Compress _resolve_missing_tool_call_ids docstring from a six-line
explanation to two. Same logic, fewer in-flow tutorials.

76 tests in cleanup + inference-model-validation + tool-passthrough pass.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2026-05-18 00:01:48 -07:00

618 lines
23 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
"""
Tests for the OpenAI /v1/chat/completions client-side tool pass-through.
Covers:
- ChatCompletionRequest accepts standard OpenAI `tools` / `tool_choice` / `stop`.
- ChatMessage accepts role="tool" with `tool_call_id` and role="assistant"
with `content: None` + `tool_calls`.
- ChatCompletionRequest carries unknown fields via `extra="allow"`.
- anthropic_tool_choice_to_openai() covers all four Anthropic shapes.
- _build_passthrough_payload() honors a caller-supplied tool_choice and
defaults to "auto" when unset.
- _friendly_error() maps httpx transport errors to a "Lost connection"
message so passthrough failures are legible instead of bare 500s.
No running server or GPU required.
"""
import os
import sys
_backend = os.path.join(os.path.dirname(__file__), "..")
sys.path.insert(0, _backend)
import httpx
import pytest
from pydantic import ValidationError
from models.inference import (
ChatCompletionRequest,
ChatMessage,
)
from core.inference.anthropic_compat import (
anthropic_tool_choice_to_openai,
)
from routes.inference import _build_passthrough_payload, _friendly_error
# =====================================================================
# ChatMessage — tool role, tool_calls, optional content
# =====================================================================
class TestChatMessageToolRoles:
def test_tool_role_with_tool_call_id(self):
msg = ChatMessage(
role = "tool",
tool_call_id = "call_abc123",
content = '{"temperature": 72}',
)
assert msg.role == "tool"
assert msg.tool_call_id == "call_abc123"
assert msg.content == '{"temperature": 72}'
def test_tool_role_with_name(self):
msg = ChatMessage(
role = "tool",
tool_call_id = "call_abc123",
name = "get_weather",
content = '{"temperature": 72}',
)
assert msg.name == "get_weather"
def test_assistant_with_tool_calls_no_content(self):
msg = ChatMessage(
role = "assistant",
content = None,
tool_calls = [
{
"id": "call_1",
"type": "function",
"function": {
"name": "get_weather",
"arguments": '{"city": "Paris"}',
},
}
],
)
assert msg.role == "assistant"
assert msg.content is None
assert msg.tool_calls is not None
assert len(msg.tool_calls) == 1
assert msg.tool_calls[0]["function"]["name"] == "get_weather"
def test_assistant_with_content_and_tool_calls(self):
msg = ChatMessage(
role = "assistant",
content = "Let me check the weather.",
tool_calls = [
{
"id": "call_1",
"type": "function",
"function": {"name": "get_weather", "arguments": "{}"},
}
],
)
assert msg.content == "Let me check the weather."
assert msg.tool_calls[0]["id"] == "call_1"
def test_plain_user_message_still_works(self):
msg = ChatMessage(role = "user", content = "Hello")
assert msg.role == "user"
assert msg.tool_call_id is None
assert msg.tool_calls is None
assert msg.name is None
def test_invalid_role_rejected(self):
with pytest.raises(ValidationError):
ChatMessage(role = "function", content = "x")
def test_content_absent_on_assistant_tool_call_defaults_to_none(self):
# Assistant messages that carry only tool_calls are the one
# documented case where `content=None` is permitted.
msg = ChatMessage(
role = "assistant",
tool_calls = [
{
"id": "call_1",
"type": "function",
"function": {"name": "f", "arguments": "{}"},
}
],
)
assert msg.content is None
def test_tool_role_missing_tool_call_id_left_for_request_validator(self):
# Per-message: missing tool_call_id is now allowed at this layer.
# ChatCompletionRequest's walkback fills it in from the prior
# assistant tool_calls; see test_inference_model_validation.py for
# the resolution coverage.
msg = ChatMessage(role = "tool", content = '{"temperature": 72}')
assert msg.tool_call_id is None
assert msg.content == '{"temperature": 72}'
def test_tool_role_empty_tool_call_id_left_for_request_validator(self):
msg = ChatMessage(
role = "tool",
tool_call_id = "",
content = '{"temperature": 72}',
)
# Empty-string is treated the same as missing by the walkback.
assert msg.tool_call_id in (None, "")
# ── Role-aware content requirements ────────────────────────────
@pytest.mark.parametrize("role", ["user", "system"])
def test_empty_string_content_allowed(self, role):
msg = ChatMessage(role = role, content = "")
assert msg.content == ""
def test_user_missing_content_rejected(self):
with pytest.raises(ValidationError):
ChatMessage(role = "user")
def test_user_empty_list_content_rejected(self):
with pytest.raises(ValidationError):
ChatMessage(role = "user", content = [])
def test_tool_empty_content_rejected(self):
with pytest.raises(ValidationError) as exc_info:
ChatMessage(role = "tool", tool_call_id = "call_1", content = "")
assert "content" in str(exc_info.value)
def test_assistant_without_content_or_tool_calls_tolerated(self):
# Stop-button leaves an empty assistant turn; tolerate so replay round-trips.
msg = ChatMessage(role = "assistant")
assert msg.content is None
assert msg.tool_calls is None
def test_assistant_empty_string_content_normalised_to_none(self):
msg = ChatMessage(role = "assistant", content = "")
assert msg.content is None
def test_assistant_empty_list_content_normalised_to_none(self):
msg = ChatMessage(role = "assistant", content = [])
assert msg.content is None
# ── Role-constrained tool-call metadata ────────────────────────
def test_tool_calls_on_user_rejected(self):
with pytest.raises(ValidationError) as exc_info:
ChatMessage(
role = "user",
content = "Hi",
tool_calls = [
{
"id": "c1",
"type": "function",
"function": {"name": "f", "arguments": "{}"},
}
],
)
assert "tool_calls" in str(exc_info.value)
def test_tool_call_id_on_user_rejected(self):
with pytest.raises(ValidationError) as exc_info:
ChatMessage(role = "user", content = "Hi", tool_call_id = "call_1")
assert "tool_call_id" in str(exc_info.value)
def test_name_on_user_rejected(self):
with pytest.raises(ValidationError) as exc_info:
ChatMessage(role = "user", content = "Hi", name = "get_weather")
assert "name" in str(exc_info.value)
# =====================================================================
# ChatCompletionRequest — standard OpenAI tool fields
# =====================================================================
class TestChatCompletionRequestToolFields:
def _make(self, **kwargs):
base = {"messages": [{"role": "user", "content": "Hi"}]}
base.update(kwargs)
return ChatCompletionRequest(**base)
def test_tools_parses(self):
req = self._make(
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Return the weather in a city",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
},
}
],
)
assert req.tools is not None
assert len(req.tools) == 1
assert req.tools[0]["function"]["name"] == "get_weather"
def test_image_base64_allows_empty_user_text(self):
req = ChatCompletionRequest(
messages = [{"role": "user", "content": ""}],
image_base64 = "aW1hZ2U=",
)
assert req.messages[0].content == ""
assert req.image_base64 == "aW1hZ2U="
def test_tool_choice_string_auto(self):
assert self._make(tool_choice = "auto").tool_choice == "auto"
def test_tool_choice_string_required(self):
assert self._make(tool_choice = "required").tool_choice == "required"
def test_tool_choice_string_none(self):
assert self._make(tool_choice = "none").tool_choice == "none"
def test_tool_choice_named_function(self):
tc = {"type": "function", "function": {"name": "get_weather"}}
assert self._make(tool_choice = tc).tool_choice == tc
def test_stop_string(self):
assert self._make(stop = "\nUser:").stop == "\nUser:"
def test_stop_list(self):
assert self._make(stop = ["\nUser:", "\nAssistant:"]).stop == [
"\nUser:",
"\nAssistant:",
]
def test_tools_default_none(self):
req = self._make()
assert req.tools is None
assert req.tool_choice is None
assert req.stop is None
def test_extra_fields_accepted(self):
# `frequency_penalty`, `seed`, `response_format` are not yet
# explicitly declared but must survive Pydantic parsing now that
# extra="allow" is set.
req = self._make(
frequency_penalty = 0.5,
seed = 42,
response_format = {"type": "json_object"},
)
# Extras land in model_extra
assert req.model_extra is not None
assert req.model_extra.get("frequency_penalty") == 0.5
assert req.model_extra.get("seed") == 42
assert req.model_extra.get("response_format") == {"type": "json_object"}
def test_unsloth_extensions_still_work(self):
req = self._make(
enable_tools = True,
enabled_tools = ["web_search", "python"],
session_id = "abc",
)
assert req.enable_tools is True
assert req.enabled_tools == ["web_search", "python"]
assert req.session_id == "abc"
def test_stream_defaults_false_matching_openai_spec(self):
# OpenAI's /v1/chat/completions spec defaults `stream` to false.
# Studio previously defaulted to true, which broke naive curl
# clients (and .NET / System.Text.Json SDKs per #5047) that omit
# `stream` -- they expect a JSON blob, got SSE.
# Pin the corrected default so it can't silently regress.
req = self._make()
assert req.stream is False
def test_post_without_stream_field_decodes_to_stream_false_over_http(
self, monkeypatch
):
# Wire-level guard for the same default: a POST body that omits
# `stream` entirely (the exact shape naive curl / .NET clients
# send) must deserialise into stream=False *and* the response
# must be `application/json`, never `text/event-stream`.
# Mounts the real `routes.inference.router` so this catches
# regressions in middleware/aliasing on the actual endpoint
# (e.g. someone adding a request layer that injects stream=True
# before pydantic builds the model). Backends are bypassed by
# routing through `provider_type` and stubbing the external
# provider proxy.
from fastapi import FastAPI
from fastapi.responses import JSONResponse
from fastapi.testclient import TestClient
import routes.inference as inference_route
from auth.authentication import get_current_subject
captured = {}
async def _fake_proxy(payload, request):
captured["stream"] = payload.stream
return JSONResponse({"choices": [], "object": "chat.completion"})
monkeypatch.setattr(inference_route, "_proxy_to_external_provider", _fake_proxy)
app = FastAPI()
app.include_router(inference_route.router)
app.dependency_overrides[get_current_subject] = lambda: "test-user"
client = TestClient(app)
resp = client.post(
"/chat/completions",
json = {
"messages": [{"role": "user", "content": "hi"}],
"provider_type": "openai",
},
)
assert resp.status_code == 200
assert resp.headers["content-type"].startswith("application/json")
assert "text/event-stream" not in resp.headers["content-type"]
assert captured["stream"] is False
def test_multiturn_tool_loop_messages(self):
req = ChatCompletionRequest(
messages = [
{"role": "user", "content": "What's the weather in Paris?"},
{
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {
"name": "get_weather",
"arguments": '{"city": "Paris"}',
},
}
],
},
{
"role": "tool",
"tool_call_id": "call_1",
"content": '{"temperature": 14, "unit": "celsius"}',
},
],
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {"type": "object"},
},
}
],
)
assert len(req.messages) == 3
assert req.messages[1].role == "assistant"
assert req.messages[1].content is None
assert req.messages[1].tool_calls[0]["id"] == "call_1"
assert req.messages[2].role == "tool"
assert req.messages[2].tool_call_id == "call_1"
# =====================================================================
# anthropic_tool_choice_to_openai — pure translation helper
# =====================================================================
class TestAnthropicToolChoiceToOpenAI:
def test_auto(self):
assert anthropic_tool_choice_to_openai({"type": "auto"}) == "auto"
def test_any_becomes_required(self):
assert anthropic_tool_choice_to_openai({"type": "any"}) == "required"
def test_none(self):
assert anthropic_tool_choice_to_openai({"type": "none"}) == "none"
def test_tool_named(self):
result = anthropic_tool_choice_to_openai(
{"type": "tool", "name": "get_weather"}
)
assert result == {
"type": "function",
"function": {"name": "get_weather"},
}
def test_tool_missing_name_returns_none(self):
assert anthropic_tool_choice_to_openai({"type": "tool"}) is None
def test_none_input_returns_none(self):
assert anthropic_tool_choice_to_openai(None) is None
def test_unrecognized_shape_returns_none(self):
assert anthropic_tool_choice_to_openai({"type": "wibble"}) is None
assert anthropic_tool_choice_to_openai("auto") is None
assert anthropic_tool_choice_to_openai(42) is None
# =====================================================================
# _build_passthrough_payload — tool_choice propagation
# =====================================================================
class TestBuildPassthroughPayloadToolChoice:
def _args(self):
return dict(
openai_messages = [{"role": "user", "content": "Hi"}],
openai_tools = [
{
"type": "function",
"function": {"name": "f", "parameters": {"type": "object"}},
}
],
temperature = 0.6,
top_p = 0.95,
top_k = 20,
max_tokens = 128,
stream = False,
)
def test_default_tool_choice_is_auto(self):
body = _build_passthrough_payload(**self._args())
assert body["tool_choice"] == "auto"
def test_override_tool_choice_required(self):
body = _build_passthrough_payload(**self._args(), tool_choice = "required")
assert body["tool_choice"] == "required"
def test_override_tool_choice_none(self):
body = _build_passthrough_payload(**self._args(), tool_choice = "none")
assert body["tool_choice"] == "none"
def test_override_tool_choice_named_function(self):
tc = {"type": "function", "function": {"name": "f"}}
body = _build_passthrough_payload(**self._args(), tool_choice = tc)
assert body["tool_choice"] == tc
def test_stream_adds_include_usage(self):
args = self._args()
args["stream"] = True
body = _build_passthrough_payload(**args)
assert body.get("stream_options") == {"include_usage": True}
def test_repetition_penalty_renamed(self):
body = _build_passthrough_payload(**self._args(), repetition_penalty = 1.1)
assert body.get("repeat_penalty") == 1.1
assert "repetition_penalty" not in body
# =====================================================================
# _friendly_error — httpx transport failures
# =====================================================================
class TestFriendlyErrorHttpx:
"""The async pass-through helpers talk to llama-server via httpx.
When the subprocess is down, httpx raises RequestError subclasses
whose string form (``"All connection attempts failed"``, ``"[Errno 111]
Connection refused"``, ...) does NOT contain the substring
``"Lost connection to llama-server"`` the sync path uses, so the
previous substring-only `_friendly_error` returned a useless generic
message. These tests pin the new isinstance-based mapping.
"""
def _req(self):
return httpx.Request("POST", "http://127.0.0.1:65535/v1/chat/completions")
def test_connect_error_mapped(self):
exc = httpx.ConnectError("All connection attempts failed", request = self._req())
assert "Lost connection" in _friendly_error(exc)
def test_read_error_mapped(self):
exc = httpx.ReadError("EOF", request = self._req())
assert "Lost connection" in _friendly_error(exc)
def test_remote_protocol_error_mapped(self):
exc = httpx.RemoteProtocolError("peer closed", request = self._req())
assert "Lost connection" in _friendly_error(exc)
def test_read_timeout_mapped(self):
exc = httpx.ReadTimeout("timed out", request = self._req())
assert "Lost connection" in _friendly_error(exc)
def test_non_httpx_unchanged(self):
# Non-httpx exceptions still fall through to the existing substring
# heuristics — a context-size message must still produce the
# "Message too long" path.
ctx_msg = (
"request (4096 tokens) exceeds the available context size (2048 tokens)"
)
assert "Message too long" in _friendly_error(ValueError(ctx_msg))
def test_generic_exception_returns_generic_message(self):
assert (
_friendly_error(RuntimeError("unrelated")) == "An internal error occurred"
)
from routes.inference import ( # noqa: E402
_drop_empty_assistant_sentinels,
_openai_messages_for_passthrough,
)
class TestDropEmptyAssistantSentinels:
def test_drops_empty_assistant_between_real_turns(self):
msgs = [
{"role": "user", "content": "hi"},
{"role": "assistant", "content": ""},
{"role": "user", "content": "again"},
]
out = _drop_empty_assistant_sentinels(msgs)
assert out == [
{"role": "user", "content": "hi"},
{"role": "user", "content": "again"},
]
def test_drops_assistant_with_no_content_key(self):
# exclude_none=True strips the content key entirely; filter must catch this.
msgs = [
{"role": "user", "content": "hi"},
{"role": "assistant"},
{"role": "user", "content": "ok"},
]
out = _drop_empty_assistant_sentinels(msgs)
assert out == [
{"role": "user", "content": "hi"},
{"role": "user", "content": "ok"},
]
def test_preserves_assistant_with_text(self):
msgs = [
{"role": "user", "content": "hi"},
{"role": "assistant", "content": "hello back"},
]
out = _drop_empty_assistant_sentinels(msgs)
assert out == msgs
def test_preserves_assistant_with_tool_calls_only(self):
msgs = [
{"role": "user", "content": "weather?"},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {"name": "get_weather", "arguments": "{}"},
},
],
},
{
"role": "tool",
"tool_call_id": "call_1",
"content": '{"t": 72}',
},
]
out = _drop_empty_assistant_sentinels(msgs)
assert out == msgs
def test_preserves_user_and_system_with_empty_content(self):
# Filter scoped to role="assistant" only.
msgs = [
{"role": "system", "content": ""},
{"role": "user", "content": ""},
]
out = _drop_empty_assistant_sentinels(msgs)
assert out == msgs
def test_openai_messages_for_passthrough_drops_sentinel(self):
"""End-to-end: Stop-sentinel must not reach the wire."""
req = ChatCompletionRequest(
model = "default",
messages = [
ChatMessage(role = "user", content = "hi"),
ChatMessage(role = "assistant", content = ""),
ChatMessage(role = "user", content = "again"),
],
)
out = _openai_messages_for_passthrough(req)
roles = [m["role"] for m in out]
assert roles == ["user", "user"]
for m in out:
assert m.get("content"), m