commit 48bc92b9af67ce9fedfe1e94d7eb9aebfa4386cc Author: ducoterra Date: Fri Oct 16 12:49:45 2026 -0400 initial import: read-only gufo + vLLM dashboard Single-file FastAPI proxy plus a no-CDN vanilla-JS page for watching a gufo server and a vLLM server side by side: one Prometheus parser (class Prom) with per-engine field maps, measured Δcounter/Δt rates for engines that export no rate gauge, and strict read-only GET-only routing. Engines are opt-in via *_BASE_URL — unset means no panel, no poll, and a 404 {"configured": false} without dialling. Includes the container image and CI so the dashboard can be deployed as an image rather than a checkout: - Containerfile: uv-locked two-stage build on python:3.14-slim, non-root (uid 10001), binds 8090 (8000 is vLLM's port in this fleet), healthcheck on /api/config so a dead engine never marks the dashboard unhealthy. No frontend build stage — static/index.html is bundler-free by design and is copied as-is. - .gitea/workflows/build-and-push.yml: publishes gitea.reeseapps.com/Experimental/llm-monitor on main + release tags. 114 tests, no network and no real engines required. LLM-Generated: true diff --git a/.gitea/workflows/build-and-push.yml b/.gitea/workflows/build-and-push.yml new file mode 100644 index 0000000..50e4fb9 --- /dev/null +++ b/.gitea/workflows/build-and-push.yml @@ -0,0 +1,41 @@ +name: Build and Push Container + +on: + push: + branches: + - main + release: + types: + - published + +jobs: + build-and-push: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Login to Gitea Container Registry + uses: docker/login-action@v3 + with: + registry: gitea.reeseapps.com + username: ${{ secrets.REGISTRY_USERNAME }} + password: ${{ secrets.REGISTRY_PASSWORD }} + + - name: Extract metadata + id: meta + uses: docker/metadata-action@v5 + with: + images: gitea.reeseapps.com/Experimental/llm-monitor + tags: | + type=raw,value=main,enable=${{ github.ref == 'refs/heads/main' }} + type=ref,event=tag + + - name: Build and push + uses: docker/build-push-action@v6 + with: + context: . + file: Containerfile + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5c873e3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,19 @@ +# uv / python +.venv/ +__pycache__/ +*.py[cod] +.pytest_cache/ +.ruff_cache/ +.mypy_cache/ +.coverage +coverage.xml +htmlcov/ +# commit uv.lock and requirements.txt — this is a deployable app, not a library +.env +.env.* +!.env.example + +# logs & misc +*.log +.DS_Store +Thumbs.db diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..d3615da --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,231 @@ +# AGENTS.md — llm_monitor + +Read-only real-time dashboard for **two LLM engines side by side**: a gufo server +(llama.cpp-family, `llamacpp:*` metrics + `/slots`) and a vLLM server (`vllm:*` metrics). +Either engine is optional — it appears only if its `*_BASE_URL` env var is set, so a vLLM-only +user gets a one-panel dashboard with no dead gufo half. FastAPI backend, one HTML page, vanilla +JS, inline-SVG sparklines. No build step. + +## Commands + +```bash +uv sync # install (incl. dev group) -> .venv +uv run uvicorn app:app --host 127.0.0.1 --port 8090 # run — ALWAYS pass --port 8090 +uv run python app.py # same, via DASHBOARD_HOST/DASHBOARD_PORT +uv run pytest # 91 tests, no network, no engines needed +uv run pytest --cov # coverage gate: app.py should stay >=95% +uv run -p 3.10 pytest # requires-python is >=3.10; keep the floor +uv lock # after editing pyproject.toml deps +uv export --no-dev --no-hashes > requirements.txt # regenerate the pip shim +``` + +`uv run` auto-syncs, so you rarely need a bare `uv sync`. Use `uv run --no-dev sync` for a +production install. Never `pip install` into the system interpreter here. + +## Non-negotiables + +1. **Read-only. GET only.** The backend must only ever call `client.get(...)`. Do not add a + POST/PUT/PATCH/DELETE route, do not add "clear cache"/"unload model" buttons, do not POST + a test prompt to the engines. Tests enforce 405 on every non-GET verb for every route. +2. **Engines are opt-in, and ports come from the deployment repo, not from memory.** An + engine exists **only** when its `GUFO_BASE_URL` / `VLLM_BASE_URL` is set: there is no + in-code default host on purpose, because a built-in `127.0.0.1:8731` gives a vLLM-only user + a permanently red panel for a server they do not run. Unset → `/api/gufo/*` answers + `404 {"configured": false}` **without dialling**, and the page drops that section + (panel, strip card, polling, fleet share); neither set → the setup card. The enabled state + is derived from the base URL (`bool(GUFO_BASE_URL)`) and published as `gufo_enabled` / + `vllm_enabled` — never add a separate `*_ENABLED` flag that could disagree with it. + Source of truth for real endpoints: `~/Deployments/reeseapps/ai/deployments/*/quadlets/*.container` + — today gufo is `8731` (`deskwork-ai`, `deskwork.reeselink.com`), vLLM radiance is `8000` + (`driveripper-ai`, `driveripper.reeselink.com`). Re-check those files before documenting a + port, and keep examples tunnel-friendly (`127.0.0.1`). Never make the dashboard bind + `:8000` — that is an engine's port, and the dashboard would either fail to start or scrape itself. +3. **No CDN, no third-party assets, no bundler.** These engines sit on an internet-disconnected + podman network; a CDN ` + + diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..be08601 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,128 @@ +"""Test fixtures: a fake upstream (httpx-shaped) so no sockets are involved. + +The app only ever calls `client.get(...)`, so a stub with an async `get` is a +complete substitute for httpx.AsyncClient — which keeps the suite network-free, +deterministic, and able to simulate outages exactly. +""" + +from __future__ import annotations + +import httpx +import pytest +from fastapi.testclient import TestClient + +import app as app_module + +GUFO_FIXTURE = """# HELP llamacpp:requests_processing how many requests are processing +# TYPE llamacpp:requests_processing gauge +llama:requests_processing 99 +llamacpp:requests_processing 2 +# TYPE llamacpp:requests_deferred gauge +llamacpp:requests_deferred 1 +# TYPE llamacpp:kv_cache_usage_ratio gauge +llamacpp:kv_cache_usage_ratio 0.42 +# TYPE llamacpp:tokens_prompt_total counter +llamacpp:tokens_prompt_total{model="turbo"} 12345 +# TYPE llamacpp:tokens_generated_total counter +llamacpp:tokens_generated_total{model="turbo"} 6789 +# TYPE llamacpp:prompt_tokens_per_second gauge +llamacpp:prompt_tokens_per_second 1900.5 +# TYPE llamacpp:decode_tokens_per_second gauge +llamacpp:decode_tokens_per_second 31.5 +""" + +VLLM_FIXTURE = """# TYPE vllm:num_requests_running gauge +vllm:num_requests_running{model_name="juggernaut"} 3 +# TYPE vllm:num_requests_waiting gauge +vllm:num_requests_waiting{model_name="juggernaut"} 5 +# TYPE vllm:num_requests_waiting_by_reason gauge +vllm:num_requests_waiting_by_reason{model_name="juggernaut",reason="capacity"} 5 +vllm:num_requests_waiting_by_reason{model_name="juggernaut",reason="defragmentation"} 0 +# TYPE vllm:gpu_cache_usage gauge +vllm:gpu_cache_usage{model_name="juggernaut"} 0.61 +# TYPE vllm:prompt_tokens_total counter +vllm:prompt_tokens_total{model_name="juggernaut"} 1000000 +# TYPE vllm:generation_tokens_total counter +vllm:generation_tokens_total{model_name="juggernaut"} 250000 +# TYPE vllm:time_to_first_token_seconds histogram +vllm:time_to_first_token_seconds_bucket{le="0.1",model_name="juggernaut"} 2 +vllm:time_to_first_token_seconds_bucket{le="1",model_name="juggernaut"} 25 +vllm:time_to_first_token_seconds_bucket{le="+Inf",model_name="juggernaut"} 30 +vllm:time_to_first_token_seconds_sum{model_name="juggernaut"} 30.0 +vllm:time_to_first_token_seconds_count{model_name="juggernaut"} 30 +# TYPE vllm:time_per_output_token_seconds histogram +vllm:time_per_output_token_seconds_bucket{le="0.01",model_name="juggernaut"} 1500 +vllm:time_per_output_token_seconds_bucket{le="+Inf",model_name="juggernaut"} 3000 +vllm:time_per_output_token_seconds_sum{model_name="juggernaut"} 2.4 +vllm:time_per_output_token_seconds_count{model_name="juggernaut"} 3000 +""" + +SLOTS_FIXTURE = ( + '{"slots":[{"id":0,"state":1,"id_task":12,"n_prompt_tokens":48,' + '"n_prompt_tokens_cache":40,"n_prompt_tokens_processed":48,' + '"next_token":[{"id":29871,"text":" ","prob":0.98,"n_decoded":3,"n_remain":127}],' + '"n_remain":128}]}' +) + + +def response( + status: int = 200, + content: bytes = b"", + content_type: str = "text/plain; version=0.0.4", +) -> httpx.Response: + # A real httpx.Response rather than a mock: .text / .content / .reason_phrase + # then behave exactly as app.fetch() expects. Attaching a request is mandatory + # before a body is read. + return httpx.Response( + status, + content=content, + headers={"content-type": content_type}, + request=httpx.Request("GET", "http://upstream.test"), + ) + + +class FakeUpstream: + """Stands in for httpx.AsyncClient and records what was requested.""" + + def __init__(self) -> None: + self.routes: dict[str, object] = {} + self.calls: list[tuple[str, dict[str, str]]] = [] + + def route(self, url: str, result: object) -> None: + self.routes[url] = result + + async def get(self, url: str, headers: dict[str, str] | None = None) -> httpx.Response: + self.calls.append((url, dict(headers or {}))) + result = self.routes.get(url) + if isinstance(result, Exception): + raise result + if result is None: + return response(404, b'{"detail":"Not Found"}', "application/json") + return result # type: ignore[return-value] + + @property + def requested_urls(self) -> list[str]: + return [url for url, _headers in self.calls] + + @property + def methods(self) -> set[str]: + # the only recorded verb is GET; anything else would not reach the stub + return {"GET"} if self.calls else set() + + +@pytest.fixture +def upstream(monkeypatch) -> FakeUpstream: + fake = FakeUpstream() + monkeypatch.setattr(app_module, "GUFO_BASE_URL", "http://gufo.test") + monkeypatch.setattr(app_module, "VLLM_BASE_URL", "http://vllm.test") + monkeypatch.setattr(app_module, "GUFO_API_KEY", "") + monkeypatch.setattr(app_module, "VLLM_API_KEY", "") + return fake + + +@pytest.fixture +def client(upstream: FakeUpstream): + """TestClient without `with` => lifespan skipped, so we inject the stub client.""" + test_client = TestClient(app_module.app) + app_module.app.state.client = upstream + return test_client diff --git a/tests/fixtures/gufo_metrics_real.txt b/tests/fixtures/gufo_metrics_real.txt new file mode 100644 index 0000000..92a247a --- /dev/null +++ b/tests/fixtures/gufo_metrics_real.txt @@ -0,0 +1,49 @@ +# Captured verbatim from http://deskwork.reeselink.com:8731/metrics (gufo 0.16.0, Qwen3.8 Flash Next, +# --sessions 2, --speculative mtp). Re-capture after an engine upgrade; see tests/test_real_contracts.py. +# Values are point-in-time and drift between captures — assert on names/sources, not exact numbers. + +# HELP gufo_device_lost_total Confirmed GPU context losses +# TYPE gufo_device_lost_total counter +gufo_device_lost_total 0 +# HELP llamacpp:prompt_tokens_total Total prompt tokens processed, excluding cache hits +# TYPE llamacpp:prompt_tokens_total counter +llamacpp:prompt_tokens_total 374263 +# HELP llamacpp:prompt_tokens_cached_total Total prompt tokens reused from cache +# TYPE llamacpp:prompt_tokens_cached_total counter +llamacpp:prompt_tokens_cached_total 29650554 +# HELP llamacpp:prompt_seconds_total Prompt process time +# TYPE llamacpp:prompt_seconds_total counter +llamacpp:prompt_seconds_total 341.91072852000008 +# HELP llamacpp:tokens_predicted_total Total tokens generated +# TYPE llamacpp:tokens_predicted_total counter +llamacpp:tokens_predicted_total 173091 +# HELP llamacpp:tokens_predicted_seconds_total Predict process time +# TYPE llamacpp:tokens_predicted_seconds_total counter +llamacpp:tokens_predicted_seconds_total 5458.0845807790029 +# HELP llamacpp:n_tokens_max Largest observed n_tokens. +# TYPE llamacpp:n_tokens_max counter +llamacpp:n_tokens_max 252622 +# HELP llamacpp:spec_decode_num_drafts_total Total speculative verification rounds +# TYPE llamacpp:spec_decode_num_drafts_total counter +llamacpp:spec_decode_num_drafts_total 54515 +# HELP llamacpp:spec_decode_num_draft_tokens_total Total draft tokens proposed +# TYPE llamacpp:spec_decode_num_draft_tokens_total counter +llamacpp:spec_decode_num_draft_tokens_total 158803 +# HELP llamacpp:spec_decode_num_accepted_tokens_total Total draft tokens accepted +# TYPE llamacpp:spec_decode_num_accepted_tokens_total counter +llamacpp:spec_decode_num_accepted_tokens_total 117552 +# HELP llamacpp:prompt_tokens_seconds Prompt processing speed in tokens per second +# TYPE llamacpp:prompt_tokens_seconds gauge +llamacpp:prompt_tokens_seconds 820.2841172907755 +# HELP llamacpp:predicted_tokens_seconds Generation speed in tokens per second +# TYPE llamacpp:predicted_tokens_seconds gauge +llamacpp:predicted_tokens_seconds 43.015060732721352 +# HELP llamacpp:requests_processing Number of admitted requests, including cache preparation +# TYPE llamacpp:requests_processing gauge +llamacpp:requests_processing 0 +# HELP llamacpp:requests_deferred Number of requests waiting for a session +# TYPE llamacpp:requests_deferred gauge +llamacpp:requests_deferred 0 +# HELP llamacpp:kv_cache_usage_ratio In-flight prompt and generated tokens over sessions times context; excludes retained cache +# TYPE llamacpp:kv_cache_usage_ratio gauge +llamacpp:kv_cache_usage_ratio 0 diff --git a/tests/fixtures/gufo_slots_real.json b/tests/fixtures/gufo_slots_real.json new file mode 100644 index 0000000..1608d84 --- /dev/null +++ b/tests/fixtures/gufo_slots_real.json @@ -0,0 +1 @@ +[{"id":0,"n_ctx":262144,"speculative":true,"is_processing":false,"id_task":-1,"task_id":-1,"state":0,"n_prompt_tokens":0,"n_prompt_tokens_cache":0,"n_prompt_tokens_processed":0,"prompt":"","next_token":[{"has_next_token":false,"has_new_line":false,"n_remain":-1,"n_decoded":0}],"model":"turbo"},{"id":1,"n_ctx":262144,"speculative":true,"is_processing":false,"id_task":-1,"task_id":-1,"state":0,"n_prompt_tokens":0,"n_prompt_tokens_cache":0,"n_prompt_tokens_processed":0,"prompt":"","next_token":[{"has_next_token":false,"has_new_line":false,"n_remain":-1,"n_decoded":0}],"model":"turbo"}] \ No newline at end of file diff --git a/tests/fixtures/vllm_metrics_real.txt b/tests/fixtures/vllm_metrics_real.txt new file mode 100644 index 0000000..d7a30ed --- /dev/null +++ b/tests/fixtures/vllm_metrics_real.txt @@ -0,0 +1,163 @@ +# Captured verbatim from http://driveripper.reeselink.com:8000/metrics (vllm-radiance). +# Trimmed only by dropping series the dashboard does not read; every kept line is real. +process_cpu_seconds_total 240.62 +# HELP vllm:num_requests_running Number of requests in model execution batches. +# TYPE vllm:num_requests_running gauge +vllm:num_requests_running{engine="0",model_name="juggernaut"} 2.0 +# HELP vllm:num_requests_waiting Number of requests waiting to be processed. +# TYPE vllm:num_requests_waiting gauge +vllm:num_requests_waiting{engine="0",model_name="juggernaut"} 0.0 +# HELP vllm:num_requests_waiting_by_reason Number of waiting requests by reason. Reason labels: 'capacity' = waiting for scheduling capacity; 'deferred' = deferred by transient constraints (LoRA budget, KV transfer, blocked status). Sum of all reasons equals vllm:num_requests_waiting. +# TYPE vllm:num_requests_waiting_by_reason gauge +vllm:num_requests_waiting_by_reason{engine="0",model_name="juggernaut",reason="capacity"} 0.0 +vllm:num_requests_waiting_by_reason{engine="0",model_name="juggernaut",reason="deferred"} 0.0 +# HELP vllm:kv_cache_usage_perc KV-cache usage. 1 means 100 percent usage. +# TYPE vllm:kv_cache_usage_perc gauge +vllm:kv_cache_usage_perc{engine="0",model_name="juggernaut"} 0.39790575916230364 +# HELP vllm:prefix_cache_queries_total Prefix cache queries, in terms of number of queried tokens. +# TYPE vllm:prefix_cache_queries_total counter +vllm:prefix_cache_queries_total{engine="0",model_name="juggernaut"} 8.582355e+06 +# HELP vllm:prefix_cache_hits_total Prefix cache hits, in terms of number of cached tokens. +# TYPE vllm:prefix_cache_hits_total counter +vllm:prefix_cache_hits_total{engine="0",model_name="juggernaut"} 4.464096e+06 +# HELP vllm:num_preemptions_total Cumulative number of preemption from the engine. +# TYPE vllm:num_preemptions_total counter +vllm:num_preemptions_total{engine="0",model_name="juggernaut"} 0.0 +# HELP vllm:prompt_tokens_total Number of prefill tokens processed. +# TYPE vllm:prompt_tokens_total counter +vllm:prompt_tokens_total{engine="0",model_name="juggernaut"} 8.582355e+06 +# HELP vllm:generation_tokens_total Number of generation tokens processed. +# TYPE vllm:generation_tokens_total counter +vllm:generation_tokens_total{engine="0",model_name="juggernaut"} 339918.0 +vllm:request_success_total{engine="0",finished_reason="stop",model_name="juggernaut"} 2145.0 +vllm:request_success_total{engine="0",finished_reason="length",model_name="juggernaut"} 0.0 +vllm:request_success_total{engine="0",finished_reason="abort",model_name="juggernaut"} 0.0 +vllm:request_success_total{engine="0",finished_reason="error",model_name="juggernaut"} 0.0 +vllm:request_success_total{engine="0",finished_reason="repetition",model_name="juggernaut"} 0.0 +# HELP vllm:time_to_first_token_seconds Histogram of time to first token in seconds. +# TYPE vllm:time_to_first_token_seconds histogram +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.001",model_name="juggernaut"} 0.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.005",model_name="juggernaut"} 0.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.01",model_name="juggernaut"} 0.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.02",model_name="juggernaut"} 0.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.04",model_name="juggernaut"} 0.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.06",model_name="juggernaut"} 0.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.08",model_name="juggernaut"} 3.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.1",model_name="juggernaut"} 3.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.25",model_name="juggernaut"} 341.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.5",model_name="juggernaut"} 724.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="0.75",model_name="juggernaut"} 1076.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="1.0",model_name="juggernaut"} 1291.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="2.5",model_name="juggernaut"} 2114.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="5.0",model_name="juggernaut"} 2148.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="7.5",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="10.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="20.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="40.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="80.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="160.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="640.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="2560.0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_bucket{engine="0",le="+Inf",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_count{engine="0",model_name="juggernaut"} 2149.0 +vllm:time_to_first_token_seconds_sum{engine="0",model_name="juggernaut"} 1861.7778627872467 +# HELP vllm:inter_token_latency_seconds Histogram of inter-token latency in seconds. +# TYPE vllm:inter_token_latency_seconds histogram +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.01",model_name="juggernaut"} 0.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.025",model_name="juggernaut"} 0.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.05",model_name="juggernaut"} 327550.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.075",model_name="juggernaut"} 337018.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.1",model_name="juggernaut"} 337051.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.15",model_name="juggernaut"} 337141.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.2",model_name="juggernaut"} 337229.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.3",model_name="juggernaut"} 337313.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.4",model_name="juggernaut"} 337368.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.5",model_name="juggernaut"} 337430.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="0.75",model_name="juggernaut"} 337631.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="1.0",model_name="juggernaut"} 337651.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="2.5",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="5.0",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="7.5",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="10.0",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="20.0",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="40.0",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="80.0",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_bucket{engine="0",le="+Inf",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_count{engine="0",model_name="juggernaut"} 337769.0 +vllm:inter_token_latency_seconds_sum{engine="0",model_name="juggernaut"} 12194.350635488052 +# HELP vllm:request_queue_time_seconds Histogram of time spent in WAITING phase for request. +# TYPE vllm:request_queue_time_seconds histogram +vllm:request_queue_time_seconds_bucket{engine="0",le="0.3",model_name="juggernaut"} 2143.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="0.5",model_name="juggernaut"} 2143.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="0.8",model_name="juggernaut"} 2143.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="1.0",model_name="juggernaut"} 2143.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="1.5",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="2.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="2.5",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="5.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="10.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="15.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="20.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="30.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="40.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="50.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="60.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="120.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="240.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="480.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="960.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="1920.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="7680.0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_bucket{engine="0",le="+Inf",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_count{engine="0",model_name="juggernaut"} 2145.0 +vllm:request_queue_time_seconds_sum{engine="0",model_name="juggernaut"} 2.707464350562077 +vllm:cache_config_info{_block_size_resolved="True",block_size="1568",cache_dtype="fp8",enable_mamba_fine_grained_prefix_cache="False",enable_prefix_caching="True",engine="0",gpu_memory_utilization="0.95",is_attention_free="False",kv_cache_dtype_skip_layers="[]",kv_cache_layout="None",kv_cache_max_concurrency="2.1333333333333333",kv_cache_memory_bytes="None",kv_cache_size_tokens="279620",kv_offloading_backend="native",kv_offloading_size="None",kv_sharing_fast_prefill="False",mamba_block_size="1568",mamba_cache_dtype="auto",mamba_cache_mode="align",mamba_page_size_padded="None",mamba_ssm_cache_dtype="float32",num_cpu_blocks="None",num_gpu_blocks="192",num_gpu_blocks_override="None",prefix_cache_retention_interval="0",prefix_caching_hash_algo="sha256",prefix_match_unit="None",replayssm_buffer_len="16",skip_page_size_padded="None",sliding_window="None",use_kda_recoverssm="False",use_replayssm="False",user_specified_block_size="False",user_specified_mamba_block_size="False"} 1.0 + +# prefill series (used for the lifetime prompt-speed derivation) +# HELP vllm:request_prompt_tokens Number of prefill tokens processed. +# TYPE vllm:request_prompt_tokens histogram +vllm:request_prompt_tokens_bucket{engine="0",le="1.0",model_name="juggernaut"} 0.0 +vllm:request_prompt_tokens_bucket{engine="0",le="2.0",model_name="juggernaut"} 0.0 +vllm:request_prompt_tokens_bucket{engine="0",le="5.0",model_name="juggernaut"} 0.0 +vllm:request_prompt_tokens_bucket{engine="0",le="10.0",model_name="juggernaut"} 0.0 +vllm:request_prompt_tokens_bucket{engine="0",le="20.0",model_name="juggernaut"} 5.0 +vllm:request_prompt_tokens_bucket{engine="0",le="50.0",model_name="juggernaut"} 5.0 +vllm:request_prompt_tokens_bucket{engine="0",le="100.0",model_name="juggernaut"} 20.0 +vllm:request_prompt_tokens_bucket{engine="0",le="200.0",model_name="juggernaut"} 71.0 +vllm:request_prompt_tokens_bucket{engine="0",le="500.0",model_name="juggernaut"} 361.0 +vllm:request_prompt_tokens_bucket{engine="0",le="1000.0",model_name="juggernaut"} 638.0 +vllm:request_prompt_tokens_bucket{engine="0",le="2000.0",model_name="juggernaut"} 1134.0 +vllm:request_prompt_tokens_bucket{engine="0",le="5000.0",model_name="juggernaut"} 1943.0 +vllm:request_prompt_tokens_bucket{engine="0",le="10000.0",model_name="juggernaut"} 2026.0 +vllm:request_prompt_tokens_bucket{engine="0",le="20000.0",model_name="juggernaut"} 2072.0 +vllm:request_prompt_tokens_bucket{engine="0",le="50000.0",model_name="juggernaut"} 2112.0 +vllm:request_prompt_tokens_bucket{engine="0",le="100000.0",model_name="juggernaut"} 2145.0 +vllm:request_prompt_tokens_bucket{engine="0",le="+Inf",model_name="juggernaut"} 2145.0 +vllm:request_prompt_tokens_count{engine="0",model_name="juggernaut"} 2145.0 +vllm:request_prompt_tokens_sum{engine="0",model_name="juggernaut"} 8.441975e+06 +# HELP vllm:request_prefill_time_seconds Histogram of time spent in PREFILL phase for request. +# TYPE vllm:request_prefill_time_seconds histogram +vllm:request_prefill_time_seconds_bucket{engine="0",le="0.3",model_name="juggernaut"} 472.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="0.5",model_name="juggernaut"} 742.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="0.8",model_name="juggernaut"} 1138.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="1.0",model_name="juggernaut"} 1318.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="1.5",model_name="juggernaut"} 1976.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="2.0",model_name="juggernaut"} 2078.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="2.5",model_name="juggernaut"} 2117.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="5.0",model_name="juggernaut"} 2144.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="10.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="15.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="20.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="30.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="40.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="50.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="60.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="120.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="240.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="480.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="960.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="1920.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="7680.0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_bucket{engine="0",le="+Inf",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_count{engine="0",model_name="juggernaut"} 2145.0 +vllm:request_prefill_time_seconds_sum{engine="0",model_name="juggernaut"} 1796.7229758803733 diff --git a/tests/test_parsing.py b/tests/test_parsing.py new file mode 100644 index 0000000..76243a5 --- /dev/null +++ b/tests/test_parsing.py @@ -0,0 +1,252 @@ +"""Prometheus text parser + per-engine field maps. + +This is the code most likely to silently rot: an engine renames a series and the +dashboard must degrade to `n/a`, not crash and not misreport. The aggregation +rules (sum / max / avg) are asserted explicitly because getting them wrong is +how a cache ratio ends up over 100%. +""" + +from __future__ import annotations + +import math + +import pytest + +from app import Prom, parse_gufo_metrics, parse_vllm_metrics +from tests.conftest import GUFO_FIXTURE, VLLM_FIXTURE + + +# --------------------------------------------------------------------------- # +# Prom +# --------------------------------------------------------------------------- # +def test_ignores_comments_help_and_type_lines() -> None: + p = Prom("# HELP a hi\n# TYPE a gauge\na 1\n\n") + assert list(p.series) == ["a"] + assert p.parse_errors == 0 + + +def test_label_block_with_brace_and_escape() -> None: + p = Prom(r'a{x="a}b\"c"} 5') + assert p.series["a"][0][0] == {"x": 'a}b"c'} + assert p.series["a"][0][1] == 5.0 + + +def test_tolerates_space_before_labels_and_trailing_comment() -> None: + p = Prom('a {m="x"} 1 # a comment\nb 2 1700000000000\n') + assert p.scalar(["a"]) == 1.0 + assert p.scalar(["b"]) == 2.0 + + +def test_counts_garbage_instead_of_raising() -> None: + p = Prom("this is not prometheus\nalso bad x y z\n") + assert p.series == {} + assert p.parse_errors == 2 + + +def test_non_finite_values_are_dropped_from_aggregates() -> None: + p = Prom("a 1\na nan\na +Inf\na 3\n") + assert p.scalar(["a"]) == 4.0 # sum of the finite ones only + assert p.scalar(["a"], "max") == 3.0 + assert p.scalar(["a"], "avg") == 2.0 + + +def test_absent_series_is_none_never_zero() -> None: + p = Prom("other 1\n") + assert p.scalar(["nope"]) is None + assert p.resolve(["nope", "other"]) == "other" + + +def test_alias_resolution_prefers_first_present() -> None: + p = Prom("llama:requests_processing 7\n") + assert p.resolve(["llamacpp:requests_processing", "llama:requests_processing"]) == ( + "llama:requests_processing" + ) + + +def test_by_label_grouping() -> None: + p = Prom('w{reason="capacity"} 2\nw{reason="capacity"} 3\nw{reason="other"} 1\n') + assert p.by_label(["w"], "reason") == {"capacity": 5.0, "other": 1.0} + + +# --------------------------------------------------------------------------- # +# Histograms +# --------------------------------------------------------------------------- # +def test_histogram_mean_and_p95() -> None: + p = Prom('b_bucket{le="1"} 1\nb_bucket{le="10"} 10\nb_bucket{le="+Inf"} 10\nb_sum 40\nb_count 10\n') + h = p.histogram(["b"]) + assert h is not None + assert h["mean"] == 4.0 + # 95th of 10 observations is the 9.5th, interpolated inside the (1,10] bucket + assert h["p95"] == pytest.approx(9.5) + + +def test_histogram_p95_when_mass_exceeds_the_last_finite_bucket() -> None: + # almost everything above the last real bound: report that bound, never +Inf + p = Prom('b_bucket{le="1"} 5\nb_bucket{le="10"} 9\nb_bucket{le="+Inf"} 10\nb_sum 40\nb_count 10\n') + assert p.histogram(["b"])["p95"] == pytest.approx(10.0) + + +def test_histogram_resolves_without_a_bare_series_line() -> None: + # real expositions never emit a series named after the histogram itself + p = Prom('vllm:x_sum 6\nvllm:x_count 3\nvllm:x_bucket{le="+Inf"} 3\n') + assert p.histogram(["vllm:x"])["mean"] == 2.0 + + +def test_histogram_p95_suppressed_across_label_sets() -> None: + p = Prom( + 'c_sum{dp="0"} 10\nc_sum{dp="1"} 20\n' + 'c_count{dp="0"} 2\nc_count{dp="1"} 3\n' + 'c_bucket{dp="0",le="1"} 2\nc_bucket{dp="1",le="1"} 3\n' + ) + h = p.histogram(["c"]) + assert h["mean"] == 6.0 # still aggregated + assert h["p95"] is None # but blending label sets would invent a percentile + + +def test_histogram_missing_is_none() -> None: + assert Prom("unrelated 1\n").histogram(["b"]) is None + + +def test_quantile_on_empty_buckets() -> None: + from app import _quantile + + assert _quantile([], 0, 0.95) is None + + +# --------------------------------------------------------------------------- # +# gufo field map +# --------------------------------------------------------------------------- # +def test_gufo_field_map() -> None: + m = parse_gufo_metrics(GUFO_FIXTURE) + assert m["requests_processing"] == {"value": 2.0, "source": "llamacpp:requests_processing", "derived": False} + assert m["requests_deferred"]["value"] == 1.0 + assert m["kv_cache_usage_ratio"]["value"] == pytest.approx(0.42) + assert m["tokens_prompt_total"]["value"] == 12345.0 + assert m["tokens_generated_total"]["value"] == 6789.0 + assert m["prompt_tokens_per_second"]["value"] == pytest.approx(1900.5) + assert m["prompt_tokens_per_second_native"]["value"] == pytest.approx(1900.5) + # no rates injected -> the cell falls back to the scraped single-request gauge + assert m["decode_tokens_per_second"]["value"] == pytest.approx(31.5) + assert m["decode_tokens_per_second"]["source"] == "llamacpp:decode_tokens_per_second" + assert m["decode_tokens_per_second_native"]["value"] == pytest.approx(31.5) + assert m["tokens_total"] == { + "value": 19134.0, # no cached series in this fixture -> prompt + generated + "source": "computed:prompt+cached+generated", + "derived": False, + } + + +def test_gufo_total_counts_cache_hits_once() -> None: + # gufo's prompt_tokens_total EXCLUDES cache hits, so a naive sum under-reports + m = parse_gufo_metrics( + "llamacpp:prompt_tokens_total 100\n" + "llamacpp:prompt_tokens_cached_total 900\n" + "llamacpp:tokens_predicted_total 50\n" + ) + assert m["tokens_total"]["value"] == 1050.0 + assert m["prompt_cache_hit_ratio"]["value"] == pytest.approx(0.9) + + +def test_gufo_spec_accept_rate_absent_without_the_series() -> None: + assert parse_gufo_metrics(GUFO_FIXTURE)["spec_accept_rate"]["value"] is None + + +def test_gufo_spec_accept_rate_from_real_names() -> None: + m = parse_gufo_metrics( + "llamacpp:spec_decode_num_drafts_total 100\n" + "llamacpp:spec_decode_num_draft_tokens_total 400\n" + "llamacpp:spec_decode_num_accepted_tokens_total 200\n" + ) + assert m["spec_accept_rate"]["value"] == pytest.approx(0.5) + assert m["spec_accept_rate"]["derived"] is True + + +def test_gufo_kv_ratio_uses_max_not_sum() -> None: + # two contexts at 0.4 and 0.5 must report 0.5, never 0.9 + m = parse_gufo_metrics("llamacpp:kv_cache_usage_ratio 0.4\nllamacpp:kv_cache_usage_ratio 0.5\n") + assert m["kv_cache_usage_ratio"]["value"] == pytest.approx(0.5) + assert m["kv_cache_usage_ratio"]["value"] <= 1.0 + + +def test_gufo_empty_exposition_is_all_none() -> None: + m = parse_gufo_metrics("") + assert all(cell["value"] is None for cell in m.values()) + assert m["tokens_total"]["value"] is None + + +# --------------------------------------------------------------------------- # +# vLLM field map +# --------------------------------------------------------------------------- # +def test_vllm_field_map() -> None: + m = parse_vllm_metrics(VLLM_FIXTURE) + assert m["num_requests_running"]["value"] == 3.0 + assert m["num_requests_waiting"]["value"] == 5.0 + assert m["waiting_by_reason"]["value"] == {"capacity": 5.0, "defragmentation": 0.0} + assert m["waiting_capacity"]["value"] == 5.0 + assert m["gpu_cache_usage"]["value"] == pytest.approx(0.61) + assert m["prompt_tokens_total"]["value"] == 1_000_000.0 + assert m["generation_tokens_total"]["value"] == 250_000.0 + assert m["time_to_first_token_seconds"]["mean"] == pytest.approx(1.0) + assert m["time_to_first_token_seconds__mean"]["value"] == pytest.approx(1.0) + assert m["request_queue_time_seconds"] is None # absent on this server -> n/a + + +def test_vllm_decode_throughput_derived_from_tpot() -> None: + m = parse_vllm_metrics(VLLM_FIXTURE) + tpot = m["time_per_output_token_seconds"]["mean"] + assert m["decode_tokens_per_second"]["derived"] is True + assert m["decode_tokens_per_second"]["source"] == "computed:running/tpot" + assert m["decode_tokens_per_second"]["value"] == pytest.approx(3 / tpot) + assert m["per_request_decode_tokens_per_second"]["value"] == pytest.approx(1 / tpot) + + +def test_vllm_native_throughput_wins_over_derivation() -> None: + m = parse_vllm_metrics( + 'vllm:num_requests_running{model_name="m"} 2\n' + 'vllm:generation_throughput{model_name="m"} 77.5\n' + "vllm:time_per_output_token_seconds_sum 1.0\n" + "vllm:time_per_output_token_seconds_count 100\n" + ) + assert m["decode_tokens_per_second"] == { + "value": 77.5, + "source": "vllm:generation_throughput", + "derived": False, + } + # per-request figure is still useful next to a native aggregate + assert m["per_request_decode_tokens_per_second"]["value"] == pytest.approx(100.0) + + +def test_vllm_inter_token_latency_alias() -> None: + m = parse_vllm_metrics( + "vllm:inter_token_latency_seconds_sum 4\nvllm:inter_token_latency_seconds_count 8\n" + ) + assert m["time_per_output_token_seconds"]["source"] == "vllm:inter_token_latency_seconds" + assert m["time_per_output_token_seconds"]["mean"] == pytest.approx(0.5) + + +def test_partial_exposition_totals_do_not_raise() -> None: + """Regression: `None + value` used to 500 the endpoint when one of the two token + counters was missing — a renamed series must shrink the total, not break it.""" + assert parse_vllm_metrics("vllm:prompt_tokens_total 100\n")["tokens_total"]["value"] == 100.0 + assert parse_vllm_metrics("vllm:generation_tokens_total 7\n")["tokens_total"]["value"] == 7.0 + + +def test_vllm_empty_exposition_is_all_none() -> None: + m = parse_vllm_metrics("# nothing here\nunrelated_metric_total 1\n") + for key in ( + "num_requests_running", + "num_requests_waiting", + "gpu_cache_usage", + "prompt_tokens_total", + "decode_tokens_per_second", + "tokens_total", + ): + assert m[key]["value"] is None, key + assert m["time_to_first_token_seconds"] is None + assert m["waiting_by_reason"]["value"] == {} + + +def test_values_are_finite_or_none() -> None: + for cell in parse_vllm_metrics(VLLM_FIXTURE).values(): + if isinstance(cell, dict) and "value" in cell and isinstance(cell["value"], float): + assert math.isfinite(cell["value"]) diff --git a/tests/test_proxy.py b/tests/test_proxy.py new file mode 100644 index 0000000..c16c9e2 --- /dev/null +++ b/tests/test_proxy.py @@ -0,0 +1,299 @@ +"""Proxy routes: read-only guarantee, upstream failure translation, JSON shape. + +Nothing here touches a socket — see conftest.FakeUpstream. The point is to pin +the contract the browser depends on: what passes through, what becomes JSON, and +exactly how an engine outage shows up. +""" + +from __future__ import annotations + +import json + +import httpx +import pytest + +import app as app_module +from tests.conftest import GUFO_FIXTURE, SLOTS_FIXTURE, VLLM_FIXTURE, response + +API_PATHS = ["/api/gufo/slots", "/api/gufo/metrics", "/api/vllm/metrics", "/api/config"] +GUFO_PATHS = ["/api/gufo/slots", "/api/gufo/metrics"] +VLLM_PATHS = ["/api/vllm/metrics"] + + +# --------------------------------------------------------------------------- # +# read-only by construction +# --------------------------------------------------------------------------- # +@pytest.mark.parametrize("method", ["post", "put", "patch", "delete"]) +@pytest.mark.parametrize("path", API_PATHS) +def test_no_writing_verbs(client, method: str, path: str) -> None: + assert getattr(client, method)(path).status_code == 405 + + +def test_only_get_reaches_upstream(client, upstream) -> None: + upstream.route("http://gufo.test/slots", response(200, SLOTS_FIXTURE.encode())) + client.get("/api/gufo/slots") + upstream.route("http://gufo.test/metrics", response(200, GUFO_FIXTURE.encode())) + client.get("/api/gufo/metrics") + assert upstream.methods == {"GET"} + + +# --------------------------------------------------------------------------- # +# /api/gufo/slots — passthrough +# --------------------------------------------------------------------------- # +def test_slots_pass_through_byte_for_byte(client, upstream) -> None: + upstream.route( + "http://gufo.test/slots", response(200, SLOTS_FIXTURE.encode(), "application/json") + ) + r = client.get("/api/gufo/slots") + assert r.status_code == 200 + assert r.content.decode() == SLOTS_FIXTURE # not re-serialised, not reformatted + assert r.headers["content-type"].startswith("application/json") + assert "no-store" in r.headers["cache-control"] + assert r.json()["slots"][0]["id_task"] == 12 + + +def test_slots_accepts_a_bare_array_payload(client, upstream) -> None: + """The deployed gufo returns `[{...}]`, not `{"slots": [...]}`. The proxy must not + care — it forwards whatever JSON the engine sent, unchanged.""" + bare = '[{"id":0,"is_processing":true,"id_task":7,"next_token":[{"n_decoded":3,"n_remain":9}]}]' + upstream.route("http://gufo.test/slots", response(200, bare.encode(), "application/json")) + r = client.get("/api/gufo/slots") + assert r.status_code == 200 + assert r.json() == json.loads(bare) + assert isinstance(r.json(), list) + + +def test_upstream_content_type_is_passed_through(client, upstream) -> None: + # whatever gufo claims, the browser gets the same thing — no re-typing + upstream.route( + "http://gufo.test/slots", + response(200, SLOTS_FIXTURE.encode(), "application/stream+json"), + ) + assert client.get("/api/gufo/slots").headers["content-type"] == "application/stream+json" + + +def test_slots_falls_back_to_the_alias(client, upstream) -> None: + # /slots 404s (the stub's default for unrouted URLs), the alias answers + upstream.route("http://gufo.test/v1/slots", response(200, SLOTS_FIXTURE.encode(), "application/json")) + r = client.get("/api/gufo/slots") + assert r.status_code == 200 + assert upstream.requested_urls == ["http://gufo.test/slots", "http://gufo.test/v1/slots"] + + +def test_slots_reports_missing_endpoints(client, upstream) -> None: + r = client.get("/api/gufo/slots") + assert r.status_code == 502 + assert "/slots" in r.json()["detail"] and "/v1/slots" in r.json()["detail"] + + +def test_slots_does_not_swallow_a_server_error(client, upstream) -> None: + upstream.route("http://gufo.test/slots", response(500, b"boom")) + r = client.get("/api/gufo/slots") + assert r.status_code == 502 # engine is unhealthy: that is a bad gateway, not a fallback + assert "HTTP 500" in r.json()["detail"] + assert upstream.requested_urls == ["http://gufo.test/slots"] # no pointless alias retry + + +# --------------------------------------------------------------------------- # +# /metrics — Prometheus -> JSON +# --------------------------------------------------------------------------- # +def test_gufo_metrics_endpoint(client, upstream) -> None: + upstream.route("http://gufo.test/metrics", response(200, GUFO_FIXTURE.encode())) + body = client.get("/api/gufo/metrics").json() + assert body["ok"] is True + assert body["engine"] == "gufo" + assert body["upstream"] == "http://gufo.test/metrics" + assert body["series"] == 8 # 7 llamacpp:* series + the legacy llama:* one + assert body["unparsed"] == 0 + assert body["metrics"]["requests_processing"]["value"] == 2.0 + assert body["scraped_at"] > 0 + + +def test_vllm_metrics_endpoint(client, upstream) -> None: + upstream.route("http://vllm.test/metrics", response(200, VLLM_FIXTURE.encode())) + m = client.get("/api/vllm/metrics").json()["metrics"] + assert m["num_requests_running"]["value"] == 3.0 + assert m["waiting_capacity"]["value"] == 5.0 + assert m["time_to_first_token_seconds"]["mean"] == pytest.approx(1.0) + + +def test_garbage_exposition_is_ok_with_no_series(client, upstream) -> None: + upstream.route("http://vllm.test/metrics", response(200, b"not prometheus at all\n")) + body = client.get("/api/vllm/metrics").json() + assert body["ok"] is True + assert body["series"] == 0 and body["unparsed"] == 1 + assert body["metrics"]["num_requests_running"]["value"] is None # renders as n/a + + +# --------------------------------------------------------------------------- # +# engine outage translation (the per-engine health contract) +# --------------------------------------------------------------------------- # +def test_unreachable_engine_is_a_502_with_a_readable_detail(client, upstream) -> None: + upstream.route("http://vllm.test/metrics", httpx.ConnectError("connection refused")) + r = client.get("/api/vllm/metrics") + assert r.status_code == 502 + assert r.json()["ok"] is False + assert "unreachable" in r.json()["detail"] + + +def test_slow_engine_is_a_504(client, upstream) -> None: + upstream.route("http://gufo.test/metrics", httpx.TimeoutException("too slow")) + r = client.get("/api/gufo/metrics") + assert r.status_code == 504 + assert "timed out" in r.json()["detail"] + + +def test_one_engine_being_down_does_not_affect_the_other(client, upstream) -> None: + upstream.route("http://gufo.test/slots", response(200, SLOTS_FIXTURE.encode(), "application/json")) + upstream.route("http://vllm.test/metrics", httpx.ConnectError("connection refused")) + assert client.get("/api/vllm/metrics").status_code == 502 + assert client.get("/api/gufo/slots").status_code == 200 + + +# --------------------------------------------------------------------------- # +# auth + config +# --------------------------------------------------------------------------- # +def test_no_authorization_header_without_keys(client, upstream) -> None: + upstream.route("http://gufo.test/slots", response(200, b"{}", "application/json")) + upstream.route("http://vllm.test/metrics", response(200, b"", "text/plain")) + client.get("/api/gufo/slots") + client.get("/api/vllm/metrics") + assert all("authorization" not in headers for _url, headers in upstream.calls) + + +def test_bearer_tokens_are_per_engine(client, upstream, monkeypatch) -> None: + monkeypatch.setattr(app_module, "GUFO_API_KEY", "sk-gufo") + monkeypatch.setattr(app_module, "VLLM_API_KEY", "sk-vllm") + upstream.route("http://gufo.test/slots", response(200, b"{}", "application/json")) + upstream.route("http://vllm.test/metrics", response(200, b"", "text/plain")) + client.get("/api/gufo/slots") + client.get("/api/vllm/metrics") + assert upstream.calls[0][1]["Authorization"] == "Bearer sk-gufo" + assert upstream.calls[1][1]["Authorization"] == "Bearer sk-vllm" + + +def test_config_never_leaks_keys(client, monkeypatch) -> None: + monkeypatch.setattr(app_module, "GUFO_API_KEY", "sk-super-secret") + r = client.get("/api/config") + assert "sk-super-secret" not in r.text + body = r.json() + assert body["gufo_auth"] is True and body["vllm_auth"] is False + assert body["poll_ms"] == app_module.POLL_MS + + +# --------------------------------------------------------------------------- # +# opt-in engines: an unset *_BASE_URL means this deployment does not run that server +# --------------------------------------------------------------------------- # +@pytest.mark.parametrize( + "raw, expected", + [ + ("http://box:1", "http://box:1"), + ("http://box:1/", "http://box:1"), # never a doubled //metrics + (" http://box:1 ", "http://box:1"), + ("", ""), # set-but-empty is 'not configured', not 'http:///metrics' + (" ", ""), + ], +) +def test_base_url_env_parsing(monkeypatch, raw: str, expected: str) -> None: + monkeypatch.setenv("TEST_ENGINE_BASE_URL", raw) + assert app_module._base_url_from_env("TEST_ENGINE_BASE_URL") == expected + # and unset means the same as blank: no default host is invented + monkeypatch.delenv("TEST_ENGINE_BASE_URL") + assert app_module._base_url_from_env("TEST_ENGINE_BASE_URL") == "" + + +@pytest.mark.parametrize("path", GUFO_PATHS + VLLM_PATHS) +def test_unset_base_url_is_404_not_configured(client, upstream, monkeypatch, path: str) -> None: + """A server nobody runs must not look like a server that is down: no upstream call, + no 502, and a detail that names the env var to set.""" + engine = "gufo" if "gufo" in path else "vllm" + monkeypatch.setattr(app_module, f"{engine.upper()}_BASE_URL", "") + r = client.get(path) + assert r.status_code == 404 + body = r.json() + assert body["ok"] is False + assert body["configured"] is False + assert body["engine"] == engine + assert f"{engine.upper()}_BASE_URL" in body["detail"] + assert "no such route" not in body["detail"] # distinct from an unknown path + assert upstream.requested_urls == [] # nothing was dialled + + +def test_one_configured_engine_is_unaffected_by_the_other_being_absent( + client, upstream, monkeypatch +) -> None: + """The vLLM-only recipient of this repo gets a working dashboard, not a half-broken one.""" + monkeypatch.setattr(app_module, "GUFO_BASE_URL", "") + upstream.route("http://vllm.test/metrics", response(200, VLLM_FIXTURE.encode())) + assert client.get("/api/gufo/metrics").status_code == 404 + assert client.get("/api/gufo/slots").status_code == 404 + body = client.get("/api/vllm/metrics").json() + assert body["ok"] is True + assert upstream.requested_urls == ["http://vllm.test/metrics"] + + +def test_config_reports_which_engines_exist(client, monkeypatch) -> None: + body = client.get("/api/config").json() + assert body["gufo_enabled"] is True and body["vllm_enabled"] is True + + monkeypatch.setattr(app_module, "GUFO_BASE_URL", "") + body = client.get("/api/config").json() + assert body["gufo_enabled"] is False + assert body["gufo_base_url"] == "" + assert body["vllm_enabled"] is True # the other engine keeps its section + + +def test_lifespan_owns_one_shared_client(client) -> None: + """Entering lifespan installs exactly one AsyncClient on app.state and closes it + on shutdown — the poll loop depends on connection reuse, and a leaked client + would keep sockets open after Ctrl-C.""" + from fastapi.testclient import TestClient + + with TestClient(app_module.app) as running: + shared = app_module.app.state.client + assert isinstance(shared, httpx.AsyncClient) + assert not shared.is_closed + assert running.get("/api/config").status_code == 200 + assert app_module.app.state.client is shared + assert shared.is_closed + + +# --------------------------------------------------------------------------- # +# page + misc +# --------------------------------------------------------------------------- # +def test_dashboard_is_served(client) -> None: + r = client.get("/") + assert r.status_code == 200 + assert "text/html" in r.headers["content-type"] + assert "LLM Monitor" in r.text + + +def test_dashboard_layout_is_driven_by_the_config_flags(client) -> None: + """The page must render from /api/config's *_enabled flags instead of hardcoding two + engines: an absent engine loses its panel, its strip card, its polling and its share + of the fleet totals, and nothing configured at all shows the setup card. Asserted on + the shipped HTML because the layout logic lives in the inline script.""" + html = client.get("/").text + # one generic engine list, revealed from the backend's flags + assert "const ENGINE_IDS" in html and "_enabled`" in html + assert "isEnabled(job.engine)" in html # an absent engine is never polled + # what gets hidden, and what shows instead when there is nothing to show + for marker in ("panel-${engine}", "strip-${engine}", 'id="no-engines"'): + assert marker in html + + +def test_dashboard_loads_no_third_party_assets(client) -> None: + """The engines live on an internet-disconnected network: no CDN, no webfonts, + no build step. Only same-origin requests are allowed from the page.""" + import re + + html = client.get("/").text + refs = re.findall(r"""(?:src|href)\s*=\s*["']([^"']+)["']""", html) + external = [r for r in refs if r.startswith(("http://", "//"))] + assert external == [], f"page references external assets: {external}" + + +def test_unknown_route_is_json(client) -> None: + r = client.get("/api/nope") + assert r.status_code == 404 + assert "no such route" in r.json()["detail"] diff --git a/tests/test_rates.py b/tests/test_rates.py new file mode 100644 index 0000000..5d66316 --- /dev/null +++ b/tests/test_rates.py @@ -0,0 +1,299 @@ +"""Derived throughput: Δcounter/Δt and the lifetime-average fallback. + +The deployed vLLM exports no rate gauge of any kind (verified: zero series with a +throughput/rate name), so prompt tok/s can only be *computed*. These tests pin the +precedence — native gauge > measured Δcounter > estimate > n/a — and the counter-reset +handling, because a naive delta shows up as a giant negative spike after an engine +restart. +""" + +from __future__ import annotations + +import pytest + +from app import RateEstimator, parse_gufo_metrics, parse_vllm_metrics + +# gufo's generation-speed gauge is ONE request's last decode pass; the counter is every +# slot's tokens. 30 tok/s on the gauge with 60 tokens landing per second = two slots. +GUFO_SCRAPE = """ +llamacpp:requests_processing 2 +llamacpp:tokens_predicted_total 1000 +llamacpp:predicted_tokens_seconds 30 +""" + +COUNTERS = """ +vllm:num_requests_running{engine="0"} 2 +vllm:prompt_tokens_total{engine="0"} 1000 +vllm:generation_tokens_total{engine="0"} 400 +vllm:inter_token_latency_seconds_sum 0.04 +vllm:inter_token_latency_seconds_count 2 +""" + + +def test_first_scrape_has_nothing_to_diff_against() -> None: + rates = RateEstimator() + assert rates.rate("k", 100.0, now=10.0) == (None, None) + + +def test_second_scrape_measures_the_delta() -> None: + rates = RateEstimator() + rates.rate("k", 100.0, now=10.0) + rate, window = rates.rate("k", 130.0, now=11.0) + assert rate == pytest.approx(30.0) + assert window == pytest.approx(1.0) + + +def test_counter_reset_yields_none_not_a_negative_spike() -> None: + rates = RateEstimator() + rates.rate("k", 5000.0, now=10.0) + assert rates.rate("k", 12.0, now=11.0) == (None, None) # engine restarted + # and it recovers on the following scrape + assert rates.rate("k", 22.0, now=12.0)[0] == pytest.approx(10.0) + + +def test_absent_counter_and_zero_window_are_none() -> None: + rates = RateEstimator() + assert rates.rate("k", None, now=1.0) == (None, None) + rates.rate("k", 1.0, now=5.0) + assert rates.rate("k", 2.0, now=5.0) == (None, None) # dt == 0 + assert rates.rate("other", 1.0, now=6.0) == (None, None) # unknown key + + +def test_rate_smooths_over_the_span_once_enough_samples_exist() -> None: + # a bare 1s delta jitters (prompts arrive in bursts); the span averages it out + rates = RateEstimator(span=10.0) + rates.rate("k", 0.0, now=0.0) # outside the span by t=12 + rates.rate("k", 100.0, now=5.0) + rate, window = rates.rate("k", 400.0, now=12.0) + assert window == pytest.approx(7.0) # measured from the oldest in-span sample + assert rate == pytest.approx(300.0 / 7.0) + + +def test_only_two_in_window_samples_still_produce_a_shorter_rate() -> None: + rates = RateEstimator(span=10.0) + rates.rate("k", 0.0, now=0.0) + rates.rate("k", 100.0, now=20.0) # first sample is long gone + rate, window = rates.rate("k", 150.0, now=21.0) + assert window == pytest.approx(1.0) + assert rate == pytest.approx(50.0) + + +def test_history_is_bounded() -> None: + rates = RateEstimator(span=1.0, max_samples=8) + for i in range(50): + rates.rate("k", float(i), now=float(i)) + assert len(rates._samples["k"]) <= 8 + + +def test_keys_are_independent() -> None: + rates = RateEstimator() + rates.rate("a", 0.0, now=0.0) + rates.rate("b", 0.0, now=0.0) + assert rates.rate("a", 10.0, now=1.0)[0] == pytest.approx(10.0) + assert rates.rate("b", 4.0, now=1.0)[0] == pytest.approx(4.0) + + +# --------------------------------------------------------------------------- # +# gufo aggregate decode rate: the counter adds slots, the gauge cannot +# --------------------------------------------------------------------------- # +def test_gufo_decode_rate_adds_every_slot_generating_at_once() -> None: + """The whole point: two slots at 30 tok/s must read 60, not 30. + + gufo's `predicted_tokens_seconds` is a single unlabeled gauge holding one request's + last decode pass, so it can never sum concurrency. `tokens_predicted_total` advances + once per token from every slot, so Δ/Δt is the server-aggregate. + """ + rates = RateEstimator() + parse_gufo_metrics(GUFO_SCRAPE, rates, now=100.0) + m = parse_gufo_metrics(GUFO_SCRAPE.replace("1000", "1060"), rates, now=101.0) + d = m["decode_tokens_per_second"] + assert d["value"] == pytest.approx(60.0) + assert d["derived"] is True + assert d["window"] == pytest.approx(1.0) + assert d["source"] == "computed:Δllamacpp:tokens_predicted_total/Δt" + # the single-request gauge stays exposed, untouched, as the native reference + assert m["decode_tokens_per_second_native"]["value"] == pytest.approx(30.0) + + +def test_gufo_first_scrape_falls_back_to_the_gauge_not_na() -> None: + # nothing to diff against yet, but the scrape can still answer + m = parse_gufo_metrics(GUFO_SCRAPE, RateEstimator(), now=10.0) + assert m["decode_tokens_per_second"] == { + "value": 30.0, + "source": "llamacpp:predicted_tokens_seconds", + "derived": False, + } + + +def test_gufo_idle_reads_zero_not_the_stale_gauge() -> None: + # an engine that generated nothing in the window is at 0 tok/s; the gauge would + # happily keep showing the last session's speed forever + rates = RateEstimator() + parse_gufo_metrics(GUFO_SCRAPE, rates, now=0.0) + m = parse_gufo_metrics(GUFO_SCRAPE, rates, now=1.0) + assert m["decode_tokens_per_second"]["value"] == 0.0 + assert m["decode_tokens_per_second_native"]["value"] == pytest.approx(30.0) + + +def test_gufo_counter_reset_falls_back_instead_of_going_negative() -> None: + rates = RateEstimator() + parse_gufo_metrics(GUFO_SCRAPE, rates, now=0.0) + # engine restarted: the counter dropped. Never -995 tok/s. + m = parse_gufo_metrics(GUFO_SCRAPE.replace("1000", "5"), rates, now=1.0) + assert m["decode_tokens_per_second"]["value"] == pytest.approx(30.0) + assert m["decode_tokens_per_second"]["derived"] is False + # and it recovers on the following scrape + m = parse_gufo_metrics(GUFO_SCRAPE.replace("1000", "25"), rates, now=2.0) + assert m["decode_tokens_per_second"]["value"] == pytest.approx(20.0) + + +def test_gufo_alias_rename_starts_a_fresh_window_instead_of_a_fake_rate() -> None: + # the estimator keys off the RESOLVED series name, so a renamed counter cannot diff + # against a different series' history (and a flapping alias cannot invent throughput) + rates = RateEstimator() + parse_gufo_metrics("llamacpp:tokens_generated_total 1000\n", rates, now=0.0) + m = parse_gufo_metrics("llamacpp:tokens_predicted_total 1000\n", rates, now=1.0) + assert m["decode_tokens_per_second"]["value"] is None + + +def test_gufo_prompt_rate_is_measured_and_adds_slots_too() -> None: + """Prefill gets the same treatment as decode, so a cross-engine prompt sum is additive. + + gufo's `prompt_tokens_seconds` is one request's last prefill pass; the counter is every + slot's newly-computed prompt tokens. + """ + text = "llamacpp:prompt_tokens_total 1000\nllamacpp:prompt_tokens_seconds 700\n" + rates = RateEstimator() + parse_gufo_metrics(text, rates, now=0.0) + m = parse_gufo_metrics(text.replace("1000", "3400"), rates, now=2.0) + p = m["prompt_tokens_per_second"] + assert p["value"] == pytest.approx(1200.0) + assert p["source"] == "computed:Δllamacpp:prompt_tokens_total/Δt" + assert p["derived"] is True and p["window"] == pytest.approx(2.0) + # the one-request gauge stays exposed next to it + assert m["prompt_tokens_per_second_native"]["value"] == pytest.approx(700.0) + + +def test_gufo_prompt_first_scrape_falls_back_to_the_gauge() -> None: + text = "llamacpp:prompt_tokens_total 1000\nllamacpp:prompt_tokens_seconds 700\n" + m = parse_gufo_metrics(text, RateEstimator(), now=5.0) + assert m["prompt_tokens_per_second"] == { + "value": 700.0, + "source": "llamacpp:prompt_tokens_seconds", + "derived": False, + } + + +def test_gufo_prompt_and_decode_keep_separate_windows() -> None: + # one estimator, but the two counters must not read each other's history + rates = RateEstimator() + base = "llamacpp:prompt_tokens_total 1000\nllamacpp:tokens_predicted_total 500\n" + parse_gufo_metrics(base, rates, now=0.0) + m = parse_gufo_metrics(base.replace("1000", "3000").replace("500", "550"), rates, now=2.0) + assert m["prompt_tokens_per_second"]["value"] == pytest.approx(1000.0) + assert m["decode_tokens_per_second"]["value"] == pytest.approx(25.0) + + +def test_gufo_prompt_idle_reads_zero_not_the_stale_gauge() -> None: + text = "llamacpp:prompt_tokens_total 1000\nllamacpp:prompt_tokens_seconds 700\n" + rates = RateEstimator() + parse_gufo_metrics(text, rates, now=0.0) + m = parse_gufo_metrics(text, rates, now=1.0) + assert m["prompt_tokens_per_second"]["value"] == 0.0 + assert m["prompt_tokens_per_second_native"]["value"] == pytest.approx(700.0) + + +def test_gufo_parse_without_rates_stays_pure() -> None: + assert parse_gufo_metrics(GUFO_SCRAPE) == parse_gufo_metrics(GUFO_SCRAPE) + assert parse_gufo_metrics(GUFO_SCRAPE)["decode_tokens_per_second"]["derived"] is False + + +def test_both_engines_share_one_estimator_without_cross_reading() -> None: + # RATES is one object for both engines; the series names keep the histories apart + rates = RateEstimator() + parse_gufo_metrics(GUFO_SCRAPE, rates, now=0.0) + parse_vllm_metrics(COUNTERS, rates, now=0.0) + g = parse_gufo_metrics(GUFO_SCRAPE.replace("1000", "1040"), rates, now=1.0) + v = parse_vllm_metrics(COUNTERS.replace("400", "410"), rates, now=1.0) + assert g["decode_tokens_per_second"]["value"] == pytest.approx(40.0) + assert v["decode_tokens_per_second"]["source"] == "computed:Δvllm:generation_tokens_total/Δt" + assert v["decode_tokens_per_second"]["value"] == pytest.approx(10.0) + + +# --------------------------------------------------------------------------- # +# parser integration +# --------------------------------------------------------------------------- # +def test_prompt_and_decode_use_measured_deltas_when_rates_are_supplied() -> None: + rates = RateEstimator() + parse_vllm_metrics(COUNTERS, rates, now=100.0) # seeds the previous sample + m = parse_vllm_metrics( + COUNTERS.replace("1000", "1500").replace("400", "450"), rates, now=101.0 + ) + p = m["prompt_tokens_per_second"] + d = m["decode_tokens_per_second"] + assert p["derived"] is True and p["window"] == pytest.approx(1.0) + assert p["value"] == pytest.approx(500.0) + assert p["source"] == "computed:Δvllm:prompt_tokens_total/Δt" + assert d["value"] == pytest.approx(50.0) + assert d["source"] == "computed:Δvllm:generation_tokens_total/Δt" + + +def test_first_scrape_falls_back_to_the_lifetime_average() -> None: + # no previous sample to diff against, but prefill time + prompt length still answer + text = ( + "vllm:prompt_tokens_total 1000\n" + "vllm:request_prompt_tokens_sum 8000\nvllm:request_prompt_tokens_count 2\n" + "vllm:request_prefill_time_seconds_sum 1.6\nvllm:request_prefill_time_seconds_count 2\n" + ) + m = parse_vllm_metrics(text, RateEstimator(), now=10.0) + p = m["prompt_tokens_per_second"] + assert p["value"] == pytest.approx(5000.0) # 4000 tok mean / 0.8 s mean + assert p["source"] == "computed:mean(request_prompt_tokens)/mean(request_prefill_time_seconds)" + assert m["prefill_tokens_per_second_lifetime"]["value"] == pytest.approx(5000.0) + + +def test_measured_delta_beats_the_lifetime_average() -> None: + rates = RateEstimator() + text = ( + "vllm:prompt_tokens_total 1000\n" + "vllm:request_prompt_tokens_sum 8000\nvllm:request_prompt_tokens_count 2\n" + "vllm:request_prefill_time_seconds_sum 1.6\nvllm:request_prefill_time_seconds_count 2\n" + ) + parse_vllm_metrics(text, rates, now=0.0) + m = parse_vllm_metrics(text.replace("1000", "1200"), rates, now=1.0) + assert m["prompt_tokens_per_second"]["source"].startswith("computed:Δ") + assert m["prompt_tokens_per_second"]["value"] == pytest.approx(200.0) + + +def test_native_gauge_still_wins_over_any_derivation() -> None: + m = parse_vllm_metrics( + "vllm:prompt_throughput 1234.5\nvllm:prompt_tokens_total 10\n", + RateEstimator(), + now=1.0, + ) + assert m["prompt_tokens_per_second"] == { + "value": 1234.5, + "source": "vllm:prompt_throughput", + "derived": False, + } + + +def test_without_rates_the_parse_stays_pure() -> None: + # parse(text) alone must not depend on wall-clock or hidden state + a = parse_vllm_metrics(COUNTERS) + b = parse_vllm_metrics(COUNTERS) + assert a == b + assert a["prompt_tokens_per_second"]["value"] is None # no prefill series here + assert a["prefill_tokens_per_second_lifetime"]["value"] is None + # decode still answers via TPOT, which is stateless + assert a["decode_tokens_per_second"]["source"] == "computed:running/tpot" + + +def test_prompt_tok_s_is_never_a_silent_zero() -> None: + """The whole point: an engine that IS working must not render n/a because it lacks + a gauge, and an idle engine must show 0 rather than a stale estimate.""" + rates = RateEstimator() + text = "vllm:prompt_tokens_total 500\n" + parse_vllm_metrics(text, rates, now=0.0) + m = parse_vllm_metrics(text, rates, now=1.0) + assert m["prompt_tokens_per_second"]["value"] == 0.0 # genuinely idle, not unknown diff --git a/tests/test_real_contracts.py b/tests/test_real_contracts.py new file mode 100644 index 0000000..a2ef14b --- /dev/null +++ b/tests/test_real_contracts.py @@ -0,0 +1,229 @@ +"""Ground truth: the payloads captured from the DEPLOYED engines. + +Neither engine exports the series names people expect, and gufo's `/slots` is a bare +JSON array. These fixtures are verbatim captures (gufo on deskwork-ai:8731, vLLM on +driveripper-ai:8000); if a field stops mapping, the engine renamed something and this +test tells you which one instead of the dashboard quietly showing `n/a` in production. +""" + +from __future__ import annotations + +import json +import re +from pathlib import Path + +import pytest + +from app import RateEstimator, parse_gufo_metrics, parse_vllm_metrics + +FIXTURES = Path(__file__).resolve().parent / "fixtures" + + +@pytest.fixture(scope="module") +def gufo_text() -> str: + return (FIXTURES / "gufo_metrics_real.txt").read_text() + + +@pytest.fixture(scope="module") +def vllm_text() -> str: + return (FIXTURES / "vllm_metrics_real.txt").read_text() + + +# --------------------------------------------------------------------------- # +# gufo +# --------------------------------------------------------------------------- # +def test_gufo_real_names_are_the_primary_aliases(gufo_text: str) -> None: + m = parse_gufo_metrics(gufo_text) + assert m["tokens_prompt_total"]["source"] == "llamacpp:prompt_tokens_total" + assert m["tokens_generated_total"]["source"] == "llamacpp:tokens_predicted_total" + # gufo names its speed gauges *_seconds while the unit is tokens/second + assert m["prompt_tokens_per_second_native"]["source"] == "llamacpp:prompt_tokens_seconds" + assert m["decode_tokens_per_second_native"]["source"] == "llamacpp:predicted_tokens_seconds" + # parsed without an estimator, both throughput cells degrade to those one-request gauges + assert m["prompt_tokens_per_second"]["source"] == "llamacpp:prompt_tokens_seconds" + # parsed without an estimator, the throughput cell degrades to that one-request gauge + assert m["decode_tokens_per_second"]["source"] == "llamacpp:predicted_tokens_seconds" + + +def test_gufo_aggregate_decode_rate_resolves_on_the_real_counter(gufo_text: str) -> None: + """The Δ-rates key off the RESOLVED counter names, so they must work on gufo's real + names (`tokens_predicted_total`, `prompt_tokens_total`) and land in the same ballpark as + gufo's own busy-time counter (`tokens_predicted_seconds_total`). + """ + rates = RateEstimator() + parse_gufo_metrics(gufo_text, rates, now=0.0) + bumped = gufo_text.replace( + "llamacpp:tokens_predicted_total 173091", "llamacpp:tokens_predicted_total 173191" + ).replace( + # +1000 generated and +1000 prompt tokens over 2s -> 500 tok/s of each + "llamacpp:prompt_tokens_total 374263", "llamacpp:prompt_tokens_total 375263" + ) + assert bumped != gufo_text # the capture moved the counter; re-check this test + m = parse_gufo_metrics(bumped, rates, now=2.0) + d = m["decode_tokens_per_second"] + assert d["source"] == "computed:Δllamacpp:tokens_predicted_total/Δt" + assert d["value"] == pytest.approx(50.0) + # sanity: the lifetime mean from the two cumulative counters is the same order of + # magnitude, so the windowed aggregate is not quietly summing something else + busy = float(re.search(r"tokens_predicted_seconds_total ([0-9.]+)", gufo_text).group(1)) + assert 5.0 < (173091.0 / busy) < 100.0 + + # prompt throughput from the same two scrapes: gufo's prompt counter EXCLUDES cache hits, + # so this is newly-computed prefill work across all slots, not prompt tokens accepted + p = m["prompt_tokens_per_second"] + assert p["source"] == "computed:Δllamacpp:prompt_tokens_total/Δt" + assert p["value"] == pytest.approx(500.0) + assert m["prompt_tokens_per_second_native"]["value"] == pytest.approx(820.2841172907755) + + +def test_gufo_every_banner_and_strip_field_resolves(gufo_text: str) -> None: + m = parse_gufo_metrics(gufo_text) + for key in ( + "requests_processing", + "requests_deferred", + "kv_cache_usage_ratio", + "tokens_prompt_total", + "tokens_generated_total", + "prompt_tokens_per_second", + "decode_tokens_per_second", + "tokens_total", + "n_tokens_max", + "device_lost_total", + ): + assert m[key]["value"] is not None, f"gufo {key} stopped resolving" + assert 0.0 <= m["kv_cache_usage_ratio"]["value"] <= 1.0 + + +def test_gufo_totals_include_cache_hits_which_are_a_separate_counter(gufo_text: str) -> None: + m = parse_gufo_metrics(gufo_text) + prompt = m["tokens_prompt_total"]["value"] + cached = m["tokens_prompt_cached_total"]["value"] + generated = m["tokens_generated_total"]["value"] + assert m["tokens_total"]["value"] == pytest.approx(prompt + cached + generated) + # 29.6M cached vs 0.37M computed on the live box — cache hits dominate + assert m["prompt_cache_hit_ratio"]["value"] == pytest.approx(cached / (prompt + cached)) + assert 0.0 < m["prompt_cache_hit_ratio"]["value"] < 1.0 + + +def test_gufo_speculative_decoding_accept_rate(gufo_text: str) -> None: + m = parse_gufo_metrics(gufo_text) + accept = m["spec_accept_rate"] + assert accept["derived"] is True + assert 0.0 <= accept["value"] <= 1.0 + + +def test_gufo_device_lost_is_zero_on_a_healthy_box(gufo_text: str) -> None: + assert parse_gufo_metrics(gufo_text)["device_lost_total"]["value"] == 0.0 + + +# --------------------------------------------------------------------------- # +# gufo /slots +# --------------------------------------------------------------------------- # +def test_gufo_slots_is_a_bare_array_not_an_object() -> None: + """The UI must not assume {"slots": [...]} — the deployed engine returns a list.""" + data = json.loads((FIXTURES / "gufo_slots_real.json").read_text()) + assert isinstance(data, list) + assert len(data) >= 1 + assert all(isinstance(slot, dict) and "id" in slot for slot in data) + + +def test_gufo_slot_fields_the_cards_depend_on() -> None: + data = json.loads((FIXTURES / "gufo_slots_real.json").read_text()) + slot = data[0] + for key in ( + "id", + "state", + "is_processing", + "id_task", + "n_prompt_tokens", + "n_prompt_tokens_cache", + "n_ctx", + "next_token", + "model", + ): + assert key in slot, f"gufo slot no longer reports {key}" + assert slot["next_token"], "next_token[] should still be present, with n_decoded/n_remain" + assert "n_decoded" in slot["next_token"][0] + # an idle slot reports -1 for "nothing pending", which is not a count + if not slot["is_processing"]: + assert slot["id_task"] == -1 + + +# --------------------------------------------------------------------------- # +# vLLM +# --------------------------------------------------------------------------- # +def test_vllm_real_names_are_the_primary_aliases(vllm_text: str) -> None: + m = parse_vllm_metrics(vllm_text) + assert m["gpu_cache_usage"]["source"] == "vllm:kv_cache_usage_perc" # not gpu_cache_usage + assert m["generation_tokens_total"]["source"] == "vllm:generation_tokens_total" + assert m["time_per_output_token_seconds"]["source"] == "vllm:inter_token_latency_seconds" + + +def test_vllm_every_banner_and_card_field_resolves(vllm_text: str) -> None: + m = parse_vllm_metrics(vllm_text) + for key in ( + "num_requests_running", + "num_requests_waiting", + "gpu_cache_usage", + "prompt_tokens_total", + "generation_tokens_total", + "tokens_total", + "waiting_capacity", + "prefix_cache_hit_ratio", + "num_preemptions_total", + ): + assert m[key]["value"] is not None, f"vLLM {key} stopped resolving" + for key in ( + "time_to_first_token_seconds__mean", + "time_per_output_token_seconds__mean", + "request_queue_time_seconds__mean", + ): + assert m[key]["value"] is not None, f"vLLM {key} stopped resolving" + assert 0.0 <= m["gpu_cache_usage"]["value"] <= 1.0 + assert 0.0 <= m["prefix_cache_hit_ratio"]["value"] <= 1.0 + + +def test_vllm_waiting_reasons_from_the_live_labels(vllm_text: str) -> None: + # the deployed build tags reasons as capacity/deferred, not defragmentation + m = parse_vllm_metrics(vllm_text) + assert "capacity" in m["waiting_by_reason"]["value"] + + +def test_vllm_histograms_survive_engine_and_model_labels(vllm_text: str) -> None: + m = parse_vllm_metrics(vllm_text) + ttft = m["time_to_first_token_seconds"] + assert ttft["count"] > 0 and ttft["mean"] > 0 + # single engine label set here, so an interpolated p95 is legitimate + assert ttft["p95"] is None or ttft["p95"] >= ttft["mean"] + + +def test_vllm_prompt_tok_s_is_answered_even_without_a_rate_gauge(vllm_text: str) -> None: + # this build exports no throughput series at all, so prompt tok/s comes from the + # prefill histograms; it must not render n/a on a healthy, busy engine + m = parse_vllm_metrics(vllm_text) + p = m["prompt_tokens_per_second"] + assert p["value"] is not None, "prompt tok/s regressed to n/a" + assert p["derived"] is True + assert p["source"] == "computed:mean(request_prompt_tokens)/mean(request_prefill_time_seconds)" + assert p["value"] > 1000 # live box sits around 4.7k tok/s; single digits mean a bug + assert m["prefill_tokens_per_second_lifetime"]["value"] == pytest.approx(p["value"]) + + +def test_vllm_prompt_tok_s_switches_to_measured_rate_once_scraping(vllm_text: str) -> None: + from app import RateEstimator + + rates = RateEstimator() + 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