headroom/tests/test_stats_new_input_savings_rate.py
gglucass 112d95b618
feat(proxy): report new-content-relative input savings rate in /stats (#2058)
## Description

The whole-request savings ratios in `/stats` (`proxy_savings_percent`,
`savings_percent`) divide by a per-request recount of the full
transcript: a session at turn 200 has had its history counted 200 times
into the denominator. Long-running cached sessions — 1M-context models
especially, since they never compact — therefore read as ~0% savings no
matter how well compression performs on content that actually newly
enters context.

Field example that motivated this: one day of 1M-context Claude Code
traffic saved 641K tokens against ~13.4M tokens of genuinely new content
(~4.8%), but displayed as 0.14% because the summed full-transcript
denominator was 475M.

This PR adds a new-content-relative rate alongside the existing fields:

- `tokens.new_input_tokens` — provider-billed non-cache-read input
(uncached + cache-write tokens, summed from response usage across
providers; the cache accumulators already track both).
- `tokens.new_input_savings_percent` — `saved / (new_input + saved)`.
Tokens Headroom removed never reached the provider, so they're added
back to form the baseline: "of the input that would have newly entered
context, what fraction did Headroom remove?"

Purely additive — no existing field changes, no new accumulators.

## Type of Change

- [ ] Bug fix (non-breaking change that fixes an issue)
- [x] 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

- `headroom/proxy/server.py`: compute `new_input_tokens` from
`prefix_cache_stats["totals"]` (already built for `/stats`) and emit the
two new fields in the `tokens` block. Rate is guarded on
`new_input_tokens > 0`: the cache accumulators only see requests with
cache activity, so a deployment with no cache metrics (e.g. Bedrock)
would otherwise divide savings by themselves and report ~100% — it
reports 0 instead.
- `tests/test_stats_new_input_savings_rate.py`: endpoint-level tests via
`TestClient(create_app(...))` — a long-cached-session request shows
9.09% new-content rate while `proxy_savings_percent` stays diluted at
0.5%; and the no-cache-usage-data case reports 0.
- `CHANGELOG.md`: Features entry.

## Testing

- [x] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [x] Type checking passes (`mypy headroom`)
- [x] New tests added for new functionality
- [ ] Manual testing performed

### Test Output

```text
$ uv run --frozen --extra dev pytest tests/test_stats_new_input_savings_rate.py -v
tests/test_stats_new_input_savings_rate.py::test_stats_reports_new_input_savings_rate PASSED
tests/test_stats_new_input_savings_rate.py::test_stats_new_input_rate_is_zero_without_cache_usage_data PASSED
========================= 2 passed, 1 warning in 6.78s =========================

$ uv run --frozen --extra dev pytest tests/test_proxy_savings_history.py tests/test_dashboard_token_savings.py tests/test_proxy_cache_ttl_metrics.py
======================== 57 passed, 1 warning in 10.70s ========================

$ uv run --frozen --extra dev mypy headroom/proxy/server.py
Success: no issues found in 1 source file

$ ruff check headroom/proxy/server.py tests/test_stats_new_input_savings_rate.py
All checks passed!
$ ruff format --check headroom/proxy/server.py tests/test_stats_new_input_savings_rate.py
2 files already formatted
```

## Real Behavior Proof

- Environment: macOS 15 (darwin 24.6.0), Python 3.10 via `uv run
--frozen --extra dev`.
- Exact command / steps: `TestClient(create_app(config))`, record a
request shaped like a late turn of a long cached session
(`input_tokens=1_000_000, tokens_saved=5_000, cache_read=900_000,
cache_write=45_000, uncached=5_000`), then `GET /stats`.
- Observed result: `tokens.new_input_tokens == 50_000`,
`tokens.new_input_savings_percent == 9.09`, while
`proxy_savings_percent` stays `0.5` — the dilution the new field exists
to correct, reproduced side by side.
- Not tested: not run against a live proxy with real provider traffic;
`ruff`/`mypy` run scoped to the changed files rather than the whole
repo.

## Review Readiness

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

## Checklist

- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] New and existing unit tests pass locally with my changes
- [x] I have updated the CHANGELOG.md if applicable

## Screenshots (if applicable)

N/A — JSON API addition; dashboard adoption can follow separately.

## Additional Notes

- No linked issue; companion to the nested tool_result image
token-counting fix (same investigation — that PR fixes the inflated
numerator/denominator counts, this one fixes the metric that divides by
transcript recounts).
- Caveat worth a reviewer's eye: the numerator (`tokens_saved_total`,
local tokenizer) and denominator (provider-reported usage) come from
different counters. They're on the same scale, but the rate is
honest-approximate rather than exact — comment in code says so.
- Deliberately did not change the dashboard headline or any existing
field semantics; consumers can opt into the new rate.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: JerrettDavis <mxjerrett@gmail.com>
2026-07-12 18:48:19 -04:00

81 lines
3 KiB
Python

"""New-content-relative savings rate in /stats (tokens.new_input_savings_percent).
The whole-request ratios recount the full transcript on every turn, so long
cached sessions dilute toward 0% regardless of how well compression performs
on content that newly enters context. The new rate divides by provider-billed
non-cache-read input (uncached + cache-write) plus the tokens compression
removed before they could be billed.
"""
from __future__ import annotations
import asyncio
from fastapi.testclient import TestClient
from headroom.proxy.server import ProxyConfig, create_app
def _make_client(tmp_path, monkeypatch) -> TestClient:
monkeypatch.setenv("HEADROOM_SAVINGS_PATH", str(tmp_path / "proxy_savings.json"))
config = ProxyConfig(
cache_enabled=False,
rate_limit_enabled=False,
log_requests=False,
)
return TestClient(create_app(config))
def test_stats_reports_new_input_savings_rate(tmp_path, monkeypatch):
with _make_client(tmp_path, monkeypatch) as client:
proxy = client.app.state.proxy
# A late turn of a long cached session: the local transcript recount
# (input_tokens) dwarfs what the provider newly billed (uncached +
# cache_write = 50k), so the whole-request ratio dilutes to ~0.5%
# while the new-content rate reports the undiluted 9.09%.
asyncio.run(
proxy.metrics.record_request(
provider="anthropic",
model="claude-opus-4-6",
input_tokens=1_000_000,
output_tokens=200,
tokens_saved=5_000,
latency_ms=10.0,
cache_read_tokens=900_000,
cache_write_tokens=45_000,
uncached_input_tokens=5_000,
)
)
stats = client.get("/stats")
assert stats.status_code == 200
tokens = stats.json()["tokens"]
assert tokens["new_input_tokens"] == 50_000
# 5_000 saved / (50_000 billed-new + 5_000 saved) = 9.09%
assert tokens["new_input_savings_percent"] == 9.09
# The transcript-diluted ratio stays as-is — the new rate sits alongside,
# it does not replace existing fields.
assert tokens["proxy_savings_percent"] == 0.5
def test_stats_new_input_rate_is_zero_without_cache_usage_data(tmp_path, monkeypatch):
with _make_client(tmp_path, monkeypatch) as client:
proxy = client.app.state.proxy
# Savings recorded but no cache usage observed (provider without
# cache metrics): the rate must report 0, not savings/savings=100%.
asyncio.run(
proxy.metrics.record_request(
provider="bedrock",
model="claude-opus-4-6",
input_tokens=10_000,
output_tokens=200,
tokens_saved=2_000,
latency_ms=10.0,
)
)
tokens = client.get("/stats").json()["tokens"]
assert tokens["new_input_tokens"] == 0
assert tokens["new_input_savings_percent"] == 0