headroom/tests/test_proxy_openai_cache_stability.py
Tejas Chopra 248ae0f3e0
fix(proxy): freeze must forward cached (compressed) prefix byte-identical — stop token-mode cache busting (#1850)
The freeze path (both providers) emits the agent's ORIGINAL bytes for a
frozen message, but the provider cached whatever we FORWARDED last turn
(the compressed form). Forwarding original then mismatches the cached
prefix and busts it from that point — re-creating the whole suffix.
Measured on a real SWE-bench run: 100% of attributed misses were
prefix_change, ~56% of ALL cache-writes were bust-induced (2.8M tokens),
driving cache_create +150% and cost +41% vs baseline.

Cache mode already avoided this via _extract_cache_stable_delta (replay
the previously-forwarded prefix, compress only the delta). Token mode
called apply(frozen_count) directly, which forwards original for the
frozen region.

Fix: add a shared, provider-agnostic overlay_cached_prefix() that
replays the previously-forwarded (cached, compressed) prefix
byte-identical, append-only guarded and idempotent, and apply it in BOTH
the Anthropic and OpenAI handlers right before forwarding. This makes
freezing byte-identical in every mode, so the only remaining difference
between "token" and "cache" mode is how large a mutable
(still-compressible) tail each leaves — not whether the frozen prefix
busts the cache.

Tests:
- test_cache_prefix_overlay.py: the helper (replay, append-only guard,
idempotence).
- test_cross_turn_cache_safety.py: the invariant that was missing —
drive the REAL tracker + freeze + overlay over multiple append-only
turns against a simulated provider prefix cache and assert the forwarded
prefix stays byte-identical turn-over-turn. Load-bearing: it fails
(detects the bust) without the overlay.

## Description

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Closes #

## Type of Change

- [ ] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)

## Changes Made

- 

## Testing

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below. -->

- [ ] Unit tests pass (`pytest`)
- [ ] Linting passes (`ruff check .`)
- [ ] Type checking passes (`mypy headroom`)
- [ ] New tests added for new functionality
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### Test Output

```text
# Paste relevant command output or artifact links here
```

## Real Behavior Proof

- Environment:
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## Review Readiness

- [ ] I have performed a self-review
- [ ] This PR is ready for human review

## Checklist

- [ ] My code follows the project's style guidelines
- [ ] I have performed a self-review of my code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
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2026-07-06 14:54:39 -07:00

292 lines
10 KiB
Python

"""Regression tests for OpenAI cache-mode stability in proxy mode."""
from __future__ import annotations
from types import SimpleNamespace
import httpx
import pytest
pytest.importorskip("fastapi")
from fastapi.testclient import TestClient
from headroom.proxy.server import ProxyConfig, create_app
class _FakePrefixTracker:
def __init__(self, frozen_count: int):
self._frozen_count = frozen_count
def get_frozen_message_count(self) -> int:
return self._frozen_count
# Empty history → overlay_cached_prefix() is a no-op here, so these tests
# keep asserting the cache-freeze behavior they always have. The cross-turn
# overlay itself is exercised in test_cross_turn_cache_safety.py against the
# real tracker; these stubs just satisfy the handler's overlay call.
def get_last_original_messages(self): # noqa: ANN201
return []
def get_last_forwarded_messages(self): # noqa: ANN201
return []
def update_from_response(self, **kwargs): # noqa: ANN003
return None
def _make_proxy_client() -> TestClient:
config = ProxyConfig(
optimize=False,
cache_enabled=False,
rate_limit_enabled=False,
cost_tracking_enabled=False,
log_requests=False,
ccr_inject_tool=False,
ccr_handle_responses=False,
ccr_context_tracking=False,
image_optimize=False,
)
app = create_app(config)
return TestClient(app)
def test_openai_cache_mode_freezes_previous_turns() -> None:
captured = {}
with _make_proxy_client() as client:
proxy = client.app.state.proxy
proxy.config.optimize = True
proxy.config.mode = "cache"
fake_tracker = _FakePrefixTracker(frozen_count=0)
proxy.session_tracker_store.compute_session_id = lambda request, model, messages: (
"stable-session"
)
proxy.session_tracker_store.get_or_create = lambda session_id, provider: fake_tracker
def _fake_apply(**kwargs):
captured["frozen_message_count"] = kwargs.get("frozen_message_count")
return SimpleNamespace(
messages=kwargs["messages"],
transforms_applied=[],
timing={},
tokens_before=60,
tokens_after=60,
waste_signals=None,
)
proxy.openai_pipeline.apply = _fake_apply
async def _fake_retry(method, url, headers, body, stream=False, **kwargs): # noqa: ANN001
return httpx.Response(
200,
json={
"id": "chatcmpl_1",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "ok"},
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 60, "completion_tokens": 3, "total_tokens": 63},
},
)
proxy._retry_request = _fake_retry
response = client.post(
"/v1/chat/completions",
headers={"authorization": "Bearer test-key"},
json={
"model": "gpt-4o-mini",
"messages": [
{"role": "user", "content": "turn1"},
{"role": "assistant", "content": "turn1-assistant"},
{"role": "user", "content": "current turn"},
],
},
)
assert response.status_code == 200
assert captured["frozen_message_count"] == 2
def test_openai_cache_mode_restores_mutated_frozen_prefix() -> None:
captured = {}
with _make_proxy_client() as client:
proxy = client.app.state.proxy
proxy.config.optimize = True
proxy.config.mode = "cache"
fake_tracker = _FakePrefixTracker(frozen_count=0)
proxy.session_tracker_store.compute_session_id = lambda request, model, messages: (
"stable-session"
)
proxy.session_tracker_store.get_or_create = lambda session_id, provider: fake_tracker
original_messages = [
{"role": "user", "content": "turn1"},
{"role": "assistant", "content": "turn1-assistant"},
{"role": "user", "content": "current turn"},
]
def _fake_apply(**kwargs):
mutated = list(kwargs["messages"])
mutated[0] = {**mutated[0], "content": "MUTATED_PREFIX"}
return SimpleNamespace(
messages=mutated,
transforms_applied=["fake:mutated"],
timing={},
tokens_before=70,
tokens_after=65,
waste_signals=None,
)
proxy.openai_pipeline.apply = _fake_apply
async def _fake_retry(method, url, headers, body, stream=False, **kwargs): # noqa: ANN001
captured["body"] = body
return httpx.Response(
200,
json={
"id": "chatcmpl_2",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "ok"},
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 65, "completion_tokens": 3, "total_tokens": 68},
},
)
proxy._retry_request = _fake_retry
response = client.post(
"/v1/chat/completions",
headers={"authorization": "Bearer test-key"},
json={
"model": "gpt-4o-mini",
"messages": original_messages,
},
)
assert response.status_code == 200
sent_messages = captured["body"]["messages"]
assert sent_messages[0] == original_messages[0]
assert sent_messages[1] == original_messages[1]
# ─── Issue #327 cross-handler regression ────────────────────────────────
#
# The OpenAI handler was never affected by issue #327's content-keyed walker
# bug — it has only ever used `compute_frozen_count` (positional). This test
# locks that property by spying on the OpenAI traffic path and asserting that
# the buggy walker functions (`should_defer_compression`, `mark_stable`) are
# never called from the production handler. If a future refactor accidentally
# adds the same walker to OpenAI, this test fails immediately.
def test_issue_327_openai_handler_does_not_call_walker_functions() -> None:
calls: list[tuple[str, tuple, dict]] = []
class _SpyCompCache:
def apply_cached(self, messages): # noqa: ANN001
calls.append(("apply_cached", (), {}))
return list(messages)
def compute_frozen_count(self, messages): # noqa: ANN001
calls.append(("compute_frozen_count", (), {}))
return 0
def update_from_result(self, originals, compressed): # noqa: ANN001
calls.append(("update_from_result", (), {}))
def mark_stable_from_messages(self, messages, up_to): # noqa: ANN001
calls.append(("mark_stable_from_messages", (up_to,), {}))
# Methods below MUST NOT be called from OpenAI handler.
def should_defer_compression(self, *args, **kwargs): # noqa: ANN001, ANN002, ANN003
calls.append(("should_defer_compression", args, kwargs))
return False
def mark_stable(self, content_hash): # noqa: ANN001
calls.append(("mark_stable", (content_hash,), {}))
@staticmethod
def content_hash(content): # noqa: ANN001
return f"H({content[:40] if isinstance(content, str) else 'list'})"
with _make_proxy_client() as client:
proxy = client.app.state.proxy
proxy.config.optimize = True
proxy.config.mode = "token" # token mode is where Anthropic had the bug
fake_tracker = _FakePrefixTracker(frozen_count=0)
proxy.session_tracker_store.compute_session_id = lambda request, model, messages: (
"openai-spy-session"
)
proxy.session_tracker_store.get_or_create = lambda s, p: fake_tracker
proxy._get_compression_cache = lambda s: _SpyCompCache()
def _fake_apply(**kwargs): # noqa: ANN003
return SimpleNamespace(
messages=list(kwargs["messages"]),
transforms_applied=[],
timing={},
tokens_before=60,
tokens_after=60,
waste_signals=None,
)
proxy.openai_pipeline.apply = _fake_apply
async def _fake_retry(method, url, headers, body, stream=False, **kwargs): # noqa: ANN001
return httpx.Response(
200,
json={
"id": "cmpl",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "ok"},
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 60, "completion_tokens": 3, "total_tokens": 63},
},
)
proxy._retry_request = _fake_retry
# Drive 5 turns so any walker bug would have time to fire repeatedly.
for turn in range(5):
r = client.post(
"/v1/chat/completions",
headers={"authorization": "Bearer test-key"},
json={
"model": "gpt-4o-mini",
"messages": [
{"role": "user", "content": f"turn-{turn}-q"},
{"role": "assistant", "content": f"turn-{turn}-a"},
{"role": "tool", "tool_call_id": "t1", "content": "x" * 600},
{"role": "user", "content": f"continue-{turn}"},
],
},
)
assert r.status_code == 200
method_names = [c[0] for c in calls]
assert "should_defer_compression" not in method_names, (
f"OpenAI handler unexpectedly called should_defer_compression. "
f"Calls observed: {method_names}"
)
assert "mark_stable" not in method_names, (
f"OpenAI handler unexpectedly called mark_stable (the walker side-effect). "
f"Calls observed: {method_names}"
)
# Sanity: the safe positional methods DID fire.
assert "compute_frozen_count" in method_names
assert "apply_cached" in method_names