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test(eval): run the benchmark offline against a scripted provider (#3235)
* feat(eval): a scriptable stand-in for Anthropic and OpenAI Every defect this harness shipped last round was invisible to its own tests for one reason: the tests exercised a layer BELOW where the code runs. The usage log was never written because the proxy is a subprocess with a constructed environment. The callback could not be imported because LiteLLM loads it by path, not as a package. Failures went unrecorded because only the async hook was overridden. CI or review caught all three; no unit test could, because each called the function directly instead of driving the path that calls it. This closes that gap without spending money. It speaks the two wire protocols the harness actually depends on - Anthropic Messages, streaming and not, and OpenAI Responses - so a run can go through the real sandbox, the real CLI, the real gateway and the real usage callback with only the model faked. The runner already supports pointing at it: --base-url is the same path the free-model proxy documentation uses. Scripted rather than simulated. A test decides what the model says, which tools it asks for, and exactly what usage it reports. That last part is what makes provider-native accounting testable at all: real cache hits are not reproducible on demand, but a declared cache_read of 44,000 is. One Reply served down both protocols is also the cleanest demonstration that the same billed work is stated as a sum on one side and as a whole on the other. Tool blocks are the mechanism for artifact-producing cells. The CLI runs what it is asked to run, so a scripted Write block makes it write that file inside the sandbox for real - no model deciding anything. The end-to-end test drives the real proxy against the mock and asserts the usage log records the provider's own arithmetic through the Anthropic-shaped translation. It SKIPS here, because litellm's console script is absent in this environment, so it is unverified until CI runs it - the same footing the bubblewrap canary started on, and that one found a real bug on its first CI run. Not yet built: driving a whole sweep against this. That needs a scripted reply sequence that carries a cell to a scored artifact, which is the next step and the point of the exercise. 668 eval tests pass, 17 skipped; the two test_model_gateway.py failures are the pre-existing environmental ones. * test(eval): run a real session against the scripted provider The mock only proves something once the harness runs against it. This adds the stand-in CLI and the first integration tests that use it, so a session goes through the real code with only the model faked. tests/fixtures/fake_claude.py does what the CLI does at the two boundaries the harness depends on: it calls ANTHROPIC_BASE_URL for a turn, EXECUTES the tool blocks that come back, and prints the stream-json sequence the parent parses. Everything between - the session runner, the event-stream parse, the usage extraction, the artifact capture, the scorer - stays real. Four tests, chosen for the layers that have actually broken here: the usage a provider reported survives to the row, a scripted Write produces an artifact parse_review_output accepts, the prompt the harness meant to send is what arrived, and an upstream 529 lands as a failed session rather than a usable measurement. Writing the stand-in found two things worth keeping. The prompt arrives on STDIN under "-p --input-format text"; scanning argv for a non-flag token picks up a flag's value instead, and the prompt-fidelity test is what caught it. And three of these tests had been holding a sandbox they never applied, since no command_prefix is passed - that implied coverage which was not there, so the sandbox is gone from them and stays only in the artifact test, which needs its review directory. What these do NOT cover, checked rather than assumed: making the stand-in write in place instead of atomically still passes. On the host-unsafe backend there is no read-only mount to refuse it, so the atomic-write requirement remains a bubblewrap mount property that only the real-sandbox canary can prove. Dropping cache_read from the recorded usage does fail, so that half is genuinely pinned. 672 eval tests pass, 17 skipped; the two test_model_gateway.py failures are the environmental ones. * fix(eval): the usage adapter read a shape the callback never receives Running the gateway against the scripted provider proved the accounting merged in #3220 does not work, and the same run showed why nothing had caught it. LiteLLM does not hand a logger the upstream body. It normalises usage into its own Chat-Completions-shaped object first, so an OpenAI Responses reply reaches the callback as prompt_tokens / prompt_tokens_details.cached_tokens - never the input_tokens / input_tokens_details the shipped adapter reads. Every field came back unknown. The observed call_type is "anthropic_messages" as well, because Claude Code calls the Anthropic-shaped endpoint, so canonical_provider returned None and normalize_usage would have refused outright. Both were assumptions about a boundary I had only read about. The unit tests agreed with them because their fixture was written in the same wrong shape, so producer and consumer were consistent and both wrong - the exact failure the producer/consumer round trip exists to catch, one layer further out. Adds a LITELLM_NORMALIZED adapter for the object that actually arrives. The arithmetic is still OpenAI's - prompt_tokens is the whole, the details are subsets - so ordinary input is recovered by subtraction. The Responses adapter stays for a raw upstream body, which the mock still serves and tests directly. An unrecognised provider is still refused rather than guessed. The fixtures now carry the measured shape, and the end-to-end test asserts it through a real proxy: 48k prompt tokens with 44k cached is read back as 3k ordinary rather than as silence. 676 eval tests pass, 16 skipped, none failing. * test(eval): run a whole sweep offline, with negative controls The layers between a model turn and a promotion decision had never been exercised together. Unit tests covered each alone, and the paid runs that would have covered the composition kept dying, so the contracts BETWEEN them went unverified - which is where this harness has repeatedly shipped bugs. Drives runner.main() the way the workflow does. Real task selection, hidden oracle capture, sandbox, CLI subprocess, artifact capture, scoring against the oracle, aggregation, health guard and promotion gate. Only the model is scripted. Getting to green meant satisfying nine real contracts nothing had exercised end to end, and each failure was the harness correctly refusing bad evidence: --unsafe-no-bwrap is restricted to the paired review arms; ce_* needs a plugin carrying ce-plan, ce-work and ce-code-review; candidate_* needs an overlay; the clone needs .gitnexus/meta.json with indexedAt and lastCommit; the evidence gate needs a Skill request with a non-error result; review findings need exactly ten fields with severity in critical/high/medium/low; and the hidden labels use a DIFFERENT schema from the review output - line_start/line_end, six fields. That last one only a real run surfaces. Three negative controls, because a scorer that cannot be wrong measures nothing. A finding in the wrong place is tp=0 fp=1 fn=1 and oracle-failed, while its evidence stays VALID - being wrong is a quality result, not a broken measurement. Approving defective code is a miss with no false positive, and precision is None rather than 0, because it is undefined with no predictions. One run cannot promote: the gate says it needs three valid paired runs. A fourth control exists because a mutation demanded it. Forcing skill_was_invoked_events to return True left every other test here passing, so nothing pinned the gate that separates measuring a SKILL from measuring a model. Writing it turned up behaviour worth recording rather than assuming: a skill-not-invoked row still carries its score AND still counts toward the arm median, because aggregate() drops EXCLUDED_ERROR_KINDS and evidence_valid=False and skill-not-invoked is neither. The health guard stops the sweep, so a single-run sweep cannot promote on it, but a mixed run's median would include a cell whose skill never ran. Pinned as-is so it cannot change silently in either direction; changing it is a promotion-semantics decision, not a test fix. Two provisioning steps are stubbed and neither is harness logic: the pinned runtime mounts (no node_modules in a worktree) and the sanitized graph build (needs the gitnexus CLI at a mounted path). Containment is host-unsafe here; bubblewrap stays with the real-sandbox canary. 681 eval tests pass, 16 skipped, none failing. Runs in ~18s. * fix(eval): an uninvoked skill must not move the arm's quality median Found by the offline sweep: a skill-not-invoked row still carried its score into the arm's quality median. aggregate()'s filter dropped EXCLUDED_ERROR_KINDS and evidence_valid=False, and skill-not-invoked is neither, so an arm could be credited for a review it never performed with the skill under test - which is the one thing an arm exists to measure. Excluded from the QUALITY metrics only. Cost and duration still count that row, because the session really ran and really was billed, and the promotion gate still sees it, because it has its own vocabulary for a candidate that never loaded its skill. Two wider fixes were tried and abandoned, both because the tests said so rather than because I reasoned it out first. Reusing the health guard's evidence_failed predicate also excluded transcript-missing rows, but test_aggregate_excludes_session_error_rows_from_medians pins those as counting: that session ran, only its transcript is unverifiable. Excluding the row from `valid` outright turned a candidate whose skill never loaded from keep_incumbent into insufficient_evidence - the safety property held either way, but the decision vocabulary is promotion semantics and not mine to change on a measurement fix. Mutation-checked: putting the rows back into the quality median fails the new test. Both directions asserted, since a filter that excludes everything would also pass - a wrong-but-valid review still moves quality, because being wrong is exactly what a quality median should reflect. 682 eval tests pass, 16 skipped. * test(eval): run the offline sweep unstubbed in the job that can, and probe CLI identity Items 5 and 6 turned out to be one change. The containment (ubuntu) job already installs bubblewrap, the pinned Claude CLI, node_modules and a built GitNexus - everything the sweep's two provisioning stubs stand in for. So the stubs are not a property of the test, only of a machine that lacks those things. GITNEXUS_REQUIRE_FULL_SWEEP=1 makes the sweep run with nothing stubbed: real containment instead of --unsafe-no-bwrap, the real runtime mounts, the real sanitized graph. Set in that job, following the GITNEXUS_REQUIRE_BWRAP_CANARY pattern already there. The gate FAILS on a missing piece rather than degrading to the stubbed path, which is the point - a green tick that silently tested less is what the bubblewrap canary was written to prevent. Verified both states here: default green, and gate-on fails on this machine rather than skipping, since it cannot create user namespaces. Item 7 is an experiment, not an answer. Per-cell attribution needs an identifier that travels WITH the request, because one proxy serves the whole sweep and anything read from its environment is identical for every call. What the real CLI sends is not documented anywhere I can check, and guessing a wire format is exactly how the last three accounting bugs happened. So the probe drives the REAL pinned CLI against the mock and records the identity-bearing headers and body keys that arrive. It asserts only that a request was made; the recorded evidence is the deliverable, and the job log preserves it. Skips without CLAUDE_CANARY_BIN. Two guards caught this rather than review: the repo pins the containment job's env and its exact test list, so both had to be updated deliberately - which is the guard working, not friction. 682 eval tests pass, 17 skipped. * test(eval): make the offline sweep cross-task, so a scheduler change is checkable The sweep fixture had one task, and a single task cannot show the thing a cross-task scheduler changes: waves are per-task, so ordering, packing and a breaker spanning a task boundary are all invisible with one. A second task with its defect in a DIFFERENT file, and its own hidden labels, makes per-task routing observable. The scripted reply is now task-aware, which matters for the same reason: replying with the first task's finding scores the second task wrong. The load-bearing assertion is that each task scored against ITS OWN oracle. That is the dangerous failure mode of interleaving cells from different tasks - a mis-routed context or artifact scores one task against another's labels, and every row still looks green. Mutation-checked: pointing every cell at the first task's oracle snapshot fails it. This is the safety net the packed-scheduler wiring needs. Measured earlier against the real sweep_packed_cells, that change is worth -27% on a cold sweep and -37% weekly, with breaker fidelity holding at three injected failure positions - but it restructures a 125-line loop across ~92 names that also holds graph prefetch, reuse selection, oracle staging and the canary drop. Landing that on top of a one-task fixture would have been unverifiable, which is why this comes first and separately. 682 eval tests pass, 17 skipped. * fix(eval): commit the stand-in CLI's executable bit The file was created and chmod +x'd locally, but committed 100644 - so the mode existed only in my working tree. Any fresh checkout, CI included, gets a non-executable file and every cell dies with "required executable is not an executable regular file". Found by accident: checking out origin/main and back to compare a flaky test restored the file from the index and stripped the bit, which turned 5 green tests into 9 failures. Without that detour this would have failed on the first CI run instead. Same shape as the bugs this branch exists to catch - something that works only because of local state, breaking where the code actually runs. * fix(eval): apply code review findings Seven local reviewers and an independent cross-model pass. The headline is that a fix I added in this branch was worse than the gap it closed. Reverted the aggregate() quality-median filter. Excluding skill-not-invoked rows from the quality metrics left valid_runs and excluded_runs still counting them, so the promotion gate saw N clean runs while the median came from fewer. The dropped rows are systematically an arm's worst, so it biased toward PROMOTING - reproduced: one real run at 0.9 plus two uninvoked rows at 0.0 gave the gate 3 valid runs, zero exclusions and a 0.9 median, flipping keep_incumbent to promote. Three verdict fields compounded it: they are all() reducers still reading the wider set, so one uninvoked cell flipped a whole arm. Five reviewers found the two halves independently. Closing it honestly needs a scored-run count plus a paired-equality check in the gate, which is promotion semantics rather than an aggregation fix. The gap is now pinned by a test that states why the half-fix was reverted. Stopped forging the absence of CI. The runner refuses --unsafe-no-bwrap when CI is set because that mode runs sessions with bypassPermissions behind a boundary its own docstring calls "not a security boundary"; the sweep test deleted CI to get past it, so eval / locked pytest ran an uncontained agent sweep on the runner holding the checkout and credentials. It skips under CI instead - the containment job still runs it for real with GITNEXUS_REQUIRE_FULL_SWEEP=1. The stand-in CLI was lying in three ways. It never set is_error, so a refused write read as a completed one. It had no Skill branch at all, so honoring is_error revealed the evidence gate had been satisfied by a tool the fixture never ran - the gate was measuring the fixture, not a skill. And a reply with no usage became four zero-valued fields plus a fabricated cost, which is exactly the unknown-is-not-zero confusion the accounting it feeds exists to prevent. A provider failure also crashed the subprocess with no terminal result event. The identity probe never ran anywhere. test_mock_provider.py was in no job's file list, and the only job setting CLAUDE_CANARY_BIN runs a fixed list. My commit message claimed the next containment run would produce the answer; it would not have. Now wired in, with the CI-shape test updated to pin it. Also: the regex-miss fallback wrote a predictable name in shared /tmp through a symlink-following stage, now scoped to the test's own directory; and the canonical_provider docstring plus the callback comment still asserted a call_type branch the code no longer has. Deferred as design decisions rather than review fixes: the containment sweep uses the stand-in CLI rather than the pinned real one, the full-sweep path bypasses the gateway so native usage accounting is unexercised there, _normalize_litellm duplicates the Responses algorithm, and OPENAI_RESPONSES is now unreachable from canonical_provider. 682 eval tests pass, 17 skipped, ruff clean. * fix(eval): carry scripted tools over the Responses protocol Review round on #3235. Three real items; five more were already fixed ina20f94e1cand are answered on their threads rather than re-fixed. `_openai_response` emitted only an `output_text` item and never read `reply.tools`, so a reply scripted with a Write or Skill crossed the gateway with the tool silently dropped. Responses is the protocol the gateway is configured for BECAUSE it carries tool use, so the mock was wrong about the wire on the one path that matters most. Function-call items now accompany the message. Mutation-checked: reverting the emit fails the new test on "the scripted tool must cross the Responses path". The artifact session now takes `command_prefix` and `require_pid_namespace` from the sandbox the way `run_arm` does instead of calling `run_claude` bare. On host-unsafe `command_prefix_for` returns `[]` by construction, so this pins the wiring, not the isolation - the comment says so rather than implying more. CodeQL's three unused-variable reports on one line were one finding: a call whose result is entirely discarded. Unpack nothing there. 683 passed, 17 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> * fix(eval): forward the usage the provider reported instead of zero-filling it Review round on #3235; all three findings valid. The stand-in CLI defaulted absent cache fields to 0. That fabricated a complete measurement out of an incomplete reply, and the second-order effect was worse than the first: `runner_sessions` requires all four USAGE_FIELDS before it calls a session measured, so a stand-in that always emitted four fields made that guard unfirable from any offline test. It was always satisfied. It now forwards exactly what arrived. `Reply`'s cache fields accept None to script absence, since a consumer that cannot tell "omitted" from "zero" is the bug this harness exists to catch. Mutation-checked: restoring the zero-fill fails the new test. Also corrected a comment claiming aggregate() excludes skill-not-invoked rows from the quality median. It does not - that was the filter reverted ina20f94e1cfor inverting a promotion, and the comment survived the revert describing the opposite of what the test pins. Dropped an unused monkeypatch fixture arg. 684 passed, 17 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> * fix(eval): keep an omitted cache field omitted on the Responses wire Review round on #3235. The main finding is a miss in my own previous commit: that one taught the Anthropic path to forward absence instead of zero-filling, but `_openai_response` still serialized both `input_tokens_details` keys unconditionally. Collapsing None to 0 is right for the arithmetic - an unreported field adds nothing to the total - and wrong on the wire, because `_int_or_none` reads an absent key as unknown and a present 0 as a measured zero. So a reply scripted with `cache_read_input_tokens=None` was indistinguishable from a provider-reported zero on exactly one of the two protocols. Half-applying the invariant was arguably worse than not applying it: the Anthropic test passing made the pair look covered. Mutation-checked: restoring the unconditional keys fails the new test. Also: the module docstring claimed the stand-in executes the tool blocks that come back, without noting Bash is stubbed; and dropped an unused tmp_path fixture arg. 685 passed, 17 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> * fix(eval): validate every usage field the stand-in forwards Review round on #3235. The guard checked input_tokens and output_tokens for type and sign, but the forwarding comprehension passed the cache fields through unchecked whenever present. The parent's well_formed test only asks whether the four keys are PRESENT, so a negative, boolean, or non-integer cache value rode into a `success` result and was recorded as a usable measurement. Same shape as the previous two rounds: the required half of a pair was handled and the optional half was not. A field good enough to report is good enough to check. Mutation-checked: dropping the added clause fails all three parametrized cases (negative, boolean, string). 688 passed, 17 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> * docs(eval): say which stand-in tools execute and which are modelled Review round on #3235. The previous commit's docstring fix said "Write and Skill really run" while correcting the Bash claim. Only Write really runs: Skill validates the request and returns a synthetic result. Fourth round of the same shape - the reported half of a pair gets fixed and the sibling keeps the overclaim. Both docstrings now name each of the three branches and what it actually does. 688 passed, 17 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Gergo Magyar <gergomagyar0@gmail.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
Gergő Magyar
GitHub
parent
4154b63131
commit
e2da8d90ce
11 changed files with 1433 additions and 29 deletions
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.github/workflows/ci-tests.yml
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.github/workflows/ci-tests.yml
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@ -858,6 +858,11 @@ jobs:
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timeout-minutes: 20
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env:
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GITNEXUS_REQUIRE_BWRAP_CANARY: '1'
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# This job installs bubblewrap, the pinned runtime and a built GitNexus,
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# so the offline sweep runs here with nothing provisioning-stubbed: real
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# containment, real mounts, real graph. A missing piece fails the job
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# rather than silently falling back to the stubbed path.
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GITNEXUS_REQUIRE_FULL_SWEEP: '1'
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GITNEXUS_REQUIRE_CLAUDE_CANARY: '1'
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steps:
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- uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
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@ -916,7 +921,9 @@ jobs:
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tests/test_process_control.py
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tests/test_proposer_sandbox.py
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tests/test_workflow_bench_sessions.py
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tests/test_ce_plugin_runtime.py -q
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tests/test_ce_plugin_runtime.py
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tests/test_offline_sweep_integration.py
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tests/test_mock_provider.py -q
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working-directory: eval
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# Native Windows Job Object canary. POSIX-only tests skip by platform, while
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175
eval/tests/fixtures/fake_claude.py
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eval/tests/fixtures/fake_claude.py
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@ -0,0 +1,175 @@
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#!/usr/bin/env python3
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"""A stand-in for the Claude Code CLI: real HTTP, real tool execution, real stream-json.
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Not a mock of the harness's own code. It does what the CLI does at the two
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boundaries the harness depends on - it calls ANTHROPIC_BASE_URL for a turn, it
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EXECUTES the tool blocks that come back, and it prints the stream-json event
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sequence the parent parses. Only Write really executes - it is what produces the
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review artifact, so the artifact path has to be genuine end to end. Skill is
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MODELLED: it validates the request and returns a synthetic result, because the
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parent's evidence gate keys on the request/result pair rather than on a skill
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having loaded, and a fixture cannot load a real one. Bash is stubbed outright:
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arbitrary shell from a scripted reply buys no fidelity for the paths this
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exercises and plenty of ways to damage the host. Everything between
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those boundaries (the sandbox,
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the artifact capture, the scoring, the row) stays real, which is the whole
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point: those are the layers that shipped bugs no unit test could see.
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Reads the prompt from stdin, as the real CLI does under "-p --input-format text".
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"""
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from __future__ import annotations
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import json
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import os
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import pathlib
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import sys
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import urllib.request
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def _turn(base_url: str, prompt: str) -> dict:
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request = urllib.request.Request(
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base_url.rstrip("/") + "/v1/messages",
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data=json.dumps({"model": os.environ.get("ANTHROPIC_MODEL", "mock"), "max_tokens": 1024,
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"messages": [{"role": "user", "content": prompt}]}).encode(),
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headers={"Content-Type": "application/json",
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"x-api-key": os.environ.get("ANTHROPIC_API_KEY", ""),
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"anthropic-version": "2023-06-01"},
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)
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with urllib.request.urlopen(request, timeout=30) as response:
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return json.load(response)
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def _run_tool(name: str, params: dict) -> str:
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"""Write executes for real - it is what produces the review artifact.
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Skill and Bash do not: see the module docstring for which is modelled and
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which is stubbed, and why neither can be genuine here.
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"""
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if name == "Write":
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target = pathlib.Path(params["file_path"])
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target.parent.mkdir(parents=True, exist_ok=True)
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# Atomic, exactly as the real Write tool does it: temp file beside the
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# target, then rename. This is the operation the read-only workspace
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# boundary has to permit for the artifact directory and refuse for the
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# workspace, so a stand-in that wrote in place would prove nothing.
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staging = target.with_name(target.name + ".tmp.fake")
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staging.write_text(params.get("content", ""))
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os.replace(staging, target)
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return f"wrote {target}"
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if name == "Skill":
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# Modelled explicitly rather than falling through to a generic success.
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# The parent's evidence gate keys on a Skill request with a non-error
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# result, so leaving this unimplemented let an unexecuted skill satisfy
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# the gate - the gate would have been measuring the fixture, not a skill.
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skill = params.get("skill") or params.get("command") or params.get("name")
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if not skill:
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raise NotImplementedError("Skill request carried no skill name")
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return f"loaded skill {skill}"
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if name == "Bash":
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return "(bash suppressed in the stand-in)"
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# An unsupported tool is a FAILED tool run, not a quiet success. Returning a
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# plain string here made the parent's evidence gate read an unexecuted Skill
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# request as a successful invocation.
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raise NotImplementedError(f"unsupported tool {name}")
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def main() -> int:
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# stdin, because that is where the real CLI takes it under
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# "-p --input-format text": the parent pipes prompt bytes in. Scanning argv
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# for a non-flag token picks up a flag's VALUE instead ("text"), which is
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# exactly what the prompt-fidelity test caught.
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prompt = sys.stdin.read()
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base_url = os.environ.get("ANTHROPIC_BASE_URL")
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if not base_url:
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print(json.dumps({"type": "result", "subtype": "error", "is_error": True,
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"session_id": "fake-session", "num_turns": 0}), flush=True)
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return 1
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emit = lambda event: print(json.dumps(event), flush=True) # noqa: E731
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emit({"type": "system", "subtype": "init", "session_id": "fake-session"})
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try:
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message = _turn(base_url, prompt)
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except (OSError, ValueError) as exc:
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# A provider failure is a failed SESSION, not a crashed process: dying
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# here leaves no terminal result event, so the parent reports a generic
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# stream error instead of the upstream failure it actually saw.
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emit({"type": "result", "subtype": "error", "is_error": True,
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"session_id": "fake-session", "num_turns": 0,
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"error": f"provider request failed: {type(exc).__name__}: {exc}"})
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return 1
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blocks = message.get("content", [])
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emit({"type": "assistant", "message": {"role": "assistant", "content": blocks}})
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tool_results = []
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for block in blocks:
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if block.get("type") == "tool_use":
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# A refused write is a tool ERROR the session reports and carries
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# on from, not a crash. Letting it kill the process would lose the
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# result event and misreport a working boundary as a broken run.
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failed = False
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try:
|
||||
output = _run_tool(block["name"], block.get("input", {}))
|
||||
except (OSError, NotImplementedError) as exc:
|
||||
output, failed = f"error: {type(exc).__name__}: {exc}", True
|
||||
# is_error is load-bearing: the parent treats an ABSENT is_error as
|
||||
# success, so a refused or unsupported tool would otherwise be
|
||||
# scored as a completed one.
|
||||
tool_results.append({
|
||||
"type": "tool_result", "tool_use_id": block["id"],
|
||||
"content": output, "is_error": failed,
|
||||
})
|
||||
if tool_results:
|
||||
emit({"type": "user", "message": {"role": "user", "content": tool_results}})
|
||||
|
||||
# Unknown is not zero. A reply carrying no usage used to become four
|
||||
# zero-valued fields plus a fabricated cost, which the harness then treats
|
||||
# as a real measurement - the exact confusion the accounting this fixture
|
||||
# feeds exists to prevent.
|
||||
usage = message.get("usage")
|
||||
# Every field that gets forwarded is validated, not just the required two.
|
||||
# The parent's well_formed check tests only that the four keys are PRESENT,
|
||||
# so an unvalidated cache value rides into a success result and is recorded
|
||||
# as a real measurement. A field good enough to report is good enough to
|
||||
# check.
|
||||
countable = lambda v: isinstance(v, int) and not isinstance(v, bool) and v >= 0 # noqa: E731
|
||||
if not isinstance(usage, dict) or not all(
|
||||
countable(usage.get(f)) for f in ("input_tokens", "output_tokens")
|
||||
) or not all(
|
||||
countable(usage[f])
|
||||
for f in ("cache_read_input_tokens", "cache_creation_input_tokens")
|
||||
if f in usage
|
||||
):
|
||||
emit({"type": "result", "subtype": "error", "is_error": True,
|
||||
"session_id": "fake-session", "num_turns": 1,
|
||||
"error": "provider reply carried no usable usage; refusing to report a measured run"})
|
||||
return 1
|
||||
emit({
|
||||
"type": "result",
|
||||
"subtype": "success",
|
||||
"is_error": False,
|
||||
"session_id": "fake-session",
|
||||
"num_turns": 1,
|
||||
"duration_ms": 1200,
|
||||
# A measured zero is not the same as unmeasured; the parent rejects a
|
||||
# collapsed cost, so report a real one.
|
||||
"total_cost_usd": 0.42,
|
||||
# Forward exactly the fields the provider reported. Defaulting the
|
||||
# absent ones to 0 fabricated a complete measurement out of an
|
||||
# incomplete reply - and worse, it made the parent's own completeness
|
||||
# check (runner_sessions.USAGE_FIELDS / well_formed) unfirable from any
|
||||
# offline test, because the stand-in always satisfied it.
|
||||
"usage": {
|
||||
field: usage[field]
|
||||
for field in ("input_tokens", "output_tokens",
|
||||
"cache_read_input_tokens", "cache_creation_input_tokens")
|
||||
if field in usage
|
||||
},
|
||||
})
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
280
eval/tests/test_mock_provider.py
Normal file
280
eval/tests/test_mock_provider.py
Normal file
|
|
@ -0,0 +1,280 @@
|
|||
"""The mock has to be right about the wire, or every test built on it lies."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from workflow_bench.mock_provider import MockProvider, Reply
|
||||
from workflow_bench.provider_usage import (
|
||||
ANTHROPIC,
|
||||
LITELLM_NORMALIZED,
|
||||
OPENAI_RESPONSES,
|
||||
normalize_usage,
|
||||
)
|
||||
|
||||
|
||||
def _post(url: str, payload: dict) -> tuple[int, bytes]:
|
||||
request = urllib.request.Request(
|
||||
url, data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"}
|
||||
)
|
||||
with urllib.request.urlopen(request, timeout=10) as response:
|
||||
return response.status, response.read()
|
||||
|
||||
|
||||
def test_anthropic_messages_returns_a_usable_message() -> None:
|
||||
with MockProvider([Reply(text="reviewed")]) as provider:
|
||||
_status, raw = _post(provider.base_url + "/v1/messages", {"model": "m", "messages": []})
|
||||
body = json.loads(raw)
|
||||
assert body["role"] == "assistant"
|
||||
assert body["content"][0]["text"] == "reviewed"
|
||||
assert body["stop_reason"] == "end_turn"
|
||||
|
||||
|
||||
def test_a_scripted_tool_call_is_carried_as_a_tool_use_block() -> None:
|
||||
"""Tool blocks are how a mocked run produces real artifacts.
|
||||
|
||||
The CLI executes what it is asked to run, so a Write block makes it write
|
||||
that file for real inside the sandbox - which is how an artifact-producing
|
||||
cell can be exercised with no model involved.
|
||||
"""
|
||||
|
||||
write = {"name": "Write", "input": {"file_path": "/review-output/review-output.json", "content": "{}"}}
|
||||
with MockProvider([Reply(text="writing", tools=[write])]) as provider:
|
||||
_status, raw = _post(provider.base_url + "/v1/messages", {"model": "m", "messages": []})
|
||||
body = json.loads(raw)
|
||||
block = body["content"][1]
|
||||
assert block["type"] == "tool_use" and block["name"] == "Write"
|
||||
assert block["input"]["file_path"] == "/review-output/review-output.json"
|
||||
assert body["stop_reason"] == "tool_use", "a turn ending in a tool call must say so"
|
||||
|
||||
|
||||
def test_streaming_emits_the_event_sequence_a_consumer_expects() -> None:
|
||||
with MockProvider([Reply(text="hi")]) as provider:
|
||||
request = urllib.request.Request(
|
||||
provider.base_url + "/v1/messages",
|
||||
data=json.dumps({"model": "m", "messages": [], "stream": True}).encode(),
|
||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
with urllib.request.urlopen(request, timeout=10) as response:
|
||||
assert response.headers["Content-Type"] == "text/event-stream"
|
||||
body = response.read().decode()
|
||||
|
||||
events = [line[len("event: ") :] for line in body.splitlines() if line.startswith("event: ")]
|
||||
assert events[0] == "message_start"
|
||||
assert events[-1] == "message_stop"
|
||||
assert "content_block_delta" in events
|
||||
# message_delta carries the final usage, which is where output tokens land.
|
||||
assert events[-2] == "message_delta"
|
||||
|
||||
|
||||
def test_each_protocol_reports_usage_in_its_own_arithmetic() -> None:
|
||||
"""The whole point: the two providers count the same numbers differently.
|
||||
|
||||
Anthropic's cache fields ADD to input_tokens; OpenAI's are SUBSETS of it.
|
||||
Scripting one Reply and serving it both ways is what makes that asymmetry
|
||||
testable without a paid request.
|
||||
"""
|
||||
|
||||
reply = Reply(input_tokens=2_000, output_tokens=300, cache_read_input_tokens=7_000, cache_creation_input_tokens=1_000)
|
||||
|
||||
with MockProvider([reply, reply]) as provider:
|
||||
_s, anthropic_raw = _post(provider.base_url + "/v1/messages", {"model": "m", "messages": []})
|
||||
_s, openai_raw = _post(provider.base_url + "/v1/responses", {"model": "m", "input": []})
|
||||
|
||||
anthropic = normalize_usage(ANTHROPIC, json.loads(anthropic_raw)["usage"])
|
||||
openai = normalize_usage(OPENAI_RESPONSES, json.loads(openai_raw)["usage"])
|
||||
|
||||
assert anthropic.total_input_tokens == 10_000
|
||||
assert openai.total_input_tokens == 10_000, "same billed work, stated as the whole"
|
||||
assert anthropic.ordinary_input_tokens == 2_000
|
||||
assert openai.ordinary_input_tokens == 2_000, "recovered by subtraction, not addition"
|
||||
assert openai.cache_read_input_tokens == 7_000
|
||||
|
||||
|
||||
def test_a_scripted_failure_is_returned_as_one() -> None:
|
||||
"""Billed failures are part of what the accounting must survive."""
|
||||
|
||||
with MockProvider([Reply(status_code=529, error_body={"error": {"type": "overloaded_error"}})]) as provider:
|
||||
try:
|
||||
_post(provider.base_url + "/v1/messages", {"model": "m", "messages": []})
|
||||
raise AssertionError("the scripted failure was not returned")
|
||||
except urllib.error.HTTPError as exc:
|
||||
assert exc.code == 529
|
||||
|
||||
|
||||
def test_requests_are_recorded_for_assertions() -> None:
|
||||
with MockProvider() as provider:
|
||||
_post(provider.base_url + "/v1/messages", {"model": "claude-sonnet-4-5", "messages": [{"role": "user"}]})
|
||||
assert len(provider.requests) == 1
|
||||
assert provider.requests[0].body["model"] == "claude-sonnet-4-5"
|
||||
assert provider.requests[0].path.endswith("/v1/messages")
|
||||
|
||||
|
||||
def test_an_unscripted_turn_gets_the_default_rather_than_stalling() -> None:
|
||||
"""A real run makes more calls than a test wants to enumerate."""
|
||||
|
||||
with MockProvider([Reply(text="first")], default=Reply(text="fallback")) as provider:
|
||||
_s, one = _post(provider.base_url + "/v1/messages", {"model": "m", "messages": []})
|
||||
_s, two = _post(provider.base_url + "/v1/messages", {"model": "m", "messages": []})
|
||||
assert json.loads(one)["content"][0]["text"] == "first"
|
||||
assert json.loads(two)["content"][0]["text"] == "fallback"
|
||||
|
||||
|
||||
def test_a_request_through_the_real_gateway_records_native_usage(tmp_path, monkeypatch) -> None:
|
||||
"""The whole stack minus the model: proxy, translation, callback, log.
|
||||
|
||||
This is the path that shipped three separate defects invisible to unit
|
||||
tests - the usage variable never reaching the proxy subprocess, the
|
||||
callback failing to import when loaded by path, and failures never
|
||||
recorded. All three live between the gateway and the provider, which is
|
||||
exactly the span this exercises.
|
||||
"""
|
||||
|
||||
|
||||
import yaml
|
||||
|
||||
from workflow_bench import model_gateway
|
||||
from workflow_bench.model_gateway import OpenAIGateway
|
||||
from workflow_bench.provider_usage import USAGE_LOG_ENV_VAR
|
||||
|
||||
if shutil.which("litellm") is None:
|
||||
import pytest
|
||||
|
||||
pytest.skip("litellm console script absent; the proxy cannot start here")
|
||||
|
||||
usage_log = tmp_path / "provider_usage.jsonl"
|
||||
monkeypatch.setenv(USAGE_LOG_ENV_VAR, str(usage_log))
|
||||
|
||||
reply = Reply(input_tokens=2_000, output_tokens=300, cache_read_input_tokens=7_000, cache_creation_input_tokens=1_000)
|
||||
with MockProvider(default=reply) as provider:
|
||||
original = model_gateway.write_openai_litellm_config
|
||||
|
||||
def config(path, names):
|
||||
original(path, names)
|
||||
document = yaml.safe_load(path.read_text())
|
||||
for entry in document["model_list"]:
|
||||
entry["litellm_params"]["api_base"] = f"{provider.base_url}/v1"
|
||||
path.write_text(yaml.safe_dump(document))
|
||||
return path
|
||||
|
||||
monkeypatch.setattr(model_gateway, "write_openai_litellm_config", config)
|
||||
with OpenAIGateway(
|
||||
openai_api_key="mock-key", model_names=["gpt-4.1"], work_dir=tmp_path / "gw", ready_timeout_s=60
|
||||
) as gateway:
|
||||
request = urllib.request.Request(
|
||||
gateway.base_url + "/v1/messages",
|
||||
data=json.dumps({"model": "gpt-4.1", "max_tokens": 32, "messages": [{"role": "user", "content": "ping"}]}).encode(),
|
||||
headers={"Content-Type": "application/json", "x-api-key": gateway.auth_token, "anthropic-version": "2023-06-01"},
|
||||
)
|
||||
with urllib.request.urlopen(request, timeout=60):
|
||||
pass
|
||||
|
||||
assert usage_log.exists(), "the callback never wrote - the env did not reach the proxy"
|
||||
events = [json.loads(line) for line in usage_log.read_text().splitlines()]
|
||||
assert events, "the proxy started but recorded nothing"
|
||||
event = events[-1]
|
||||
native = event["native_usage"]
|
||||
# LiteLLM hands a callback its OWN normalised object, not the upstream body:
|
||||
# an OpenAI Responses reply arrives as prompt_tokens / prompt_tokens_details.
|
||||
# Asserting the wire shape here is what proved the shipped adapter read keys
|
||||
# that are never present.
|
||||
assert native["prompt_tokens_details"]["cached_tokens"] == 7_000
|
||||
assert native["prompt_tokens_details"]["cache_write_tokens"] == 1_000
|
||||
assert event["provider"] == LITELLM_NORMALIZED
|
||||
assert event["call_type"] == "anthropic_messages", "the observed call type, not a Responses one"
|
||||
|
||||
usage = normalize_usage(event["provider"], native)
|
||||
assert usage.total_input_tokens == 10_000
|
||||
assert usage.cache_read_input_tokens == 7_000
|
||||
assert usage.cache_write_input_tokens == 1_000
|
||||
assert usage.ordinary_input_tokens == 2_000
|
||||
assert usage.complete, "a run that cannot interpret its own usage measured nothing"
|
||||
|
||||
|
||||
def test_probe_what_identity_the_real_cli_actually_sends(tmp_path: Path) -> None:
|
||||
"""An experiment, not an assertion: which fields could correlate a request to a cell?
|
||||
|
||||
Per-cell usage attribution is unbuilt because one proxy serves the whole
|
||||
sweep, so anything read from the proxy environment is identical for every
|
||||
request. Attribution needs something that travels WITH the request, and
|
||||
what the Claude Code CLI actually sends is not documented anywhere I can
|
||||
check - guessing it is how the last three accounting bugs happened.
|
||||
|
||||
So this drives the REAL pinned CLI against the mock and prints the
|
||||
identity-bearing fields that arrive. It asserts only that a request was
|
||||
made; the value is the recorded evidence, which the job log preserves.
|
||||
"""
|
||||
|
||||
claude = os.environ.get("CLAUDE_CANARY_BIN")
|
||||
if not claude or not Path(claude).exists():
|
||||
pytest.skip("no pinned Claude CLI here; the containment job supplies CLAUDE_CANARY_BIN")
|
||||
|
||||
with MockProvider(default=Reply(text="ok")) as provider:
|
||||
subprocess.run(
|
||||
[claude, "-p", "--input-format", "text", "--output-format", "stream-json", "--verbose"],
|
||||
input=b"say ok",
|
||||
capture_output=True,
|
||||
timeout=120,
|
||||
env={
|
||||
**os.environ,
|
||||
"ANTHROPIC_BASE_URL": provider.base_url,
|
||||
"ANTHROPIC_API_KEY": "offline-probe",
|
||||
"HOME": str(tmp_path),
|
||||
},
|
||||
)
|
||||
|
||||
assert provider.requests, "the real CLI never reached the mock provider"
|
||||
request = provider.requests[0]
|
||||
interesting = {
|
||||
"header:" + name: value
|
||||
for name, value in request.headers.items()
|
||||
if any(k in name.lower() for k in ("session", "user", "trace", "request-id", "conversation", "metadata"))
|
||||
}
|
||||
interesting.update(
|
||||
{f"body:{key}": request.body[key] for key in ("metadata", "user", "session_id") if key in request.body}
|
||||
)
|
||||
print("\nIDENTITY FIELDS THE REAL CLI SENDS:")
|
||||
print(" body keys:", sorted(request.body))
|
||||
print(" candidate correlators:", interesting or "NONE — per-cell attribution needs another mechanism")
|
||||
|
||||
|
||||
def test_scripted_tools_survive_the_responses_protocol_too() -> None:
|
||||
"""The gateway uses Responses BECAUSE it carries tool use.
|
||||
|
||||
Emitting only output_text there meant a scripted Write or Skill crossed the
|
||||
gateway with the tool dropped, so a mock claiming to serve both protocols
|
||||
was wrong about the one the gateway actually runs.
|
||||
"""
|
||||
|
||||
write = {"name": "Write", "input": {"file_path": "/review-output/review-output.json", "content": "{}"}}
|
||||
with MockProvider([Reply(text="writing", tools=[write])]) as provider:
|
||||
_status, raw = _post(provider.base_url + "/v1/responses", {"model": "m", "input": []})
|
||||
|
||||
output = json.loads(raw)["output"]
|
||||
calls = [item for item in output if item["type"] == "function_call"]
|
||||
assert len(calls) == 1, "the scripted tool must cross the Responses path"
|
||||
assert calls[0]["name"] == "Write"
|
||||
assert json.loads(calls[0]["arguments"])["file_path"] == "/review-output/review-output.json"
|
||||
|
||||
|
||||
def test_an_omitted_cache_field_stays_omitted_on_the_responses_wire_too() -> None:
|
||||
"""Absence must survive both protocols, not just the Anthropic one.
|
||||
|
||||
`_int_or_none` reads an absent detail key as unknown and a present 0 as a
|
||||
measured zero, so serializing 0 for a scripted None would claim a
|
||||
measurement the reply never made.
|
||||
"""
|
||||
|
||||
with MockProvider([Reply(input_tokens=2_000, cache_read_input_tokens=None)]) as provider:
|
||||
_status, raw = _post(provider.base_url + "/v1/responses", {"model": "m", "input": []})
|
||||
|
||||
details = json.loads(raw)["usage"]["input_tokens_details"]
|
||||
assert "cached_tokens" not in details, "an omitted field must not serialize as a measured zero"
|
||||
assert details["cache_write_tokens"] == 0, "a scripted 0 is still a real measurement"
|
||||
195
eval/tests/test_offline_session_integration.py
Normal file
195
eval/tests/test_offline_session_integration.py
Normal file
|
|
@ -0,0 +1,195 @@
|
|||
"""A session end to end with only the model faked.
|
||||
|
||||
The layers between the CLI and the row are where this harness has actually
|
||||
shipped bugs - the artifact that could not be written, the usage that was never
|
||||
recorded, the evidence that was scored from the wrong directory. Every one of
|
||||
them sat below the level its tests exercised. These run the real session path
|
||||
against a scripted provider, so the only thing not real is what the model says.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from workflow_bench.mock_provider import MockProvider, Reply
|
||||
from workflow_bench.proposer_sandbox import (
|
||||
host_workspace_write_boundary,
|
||||
prepare_review_workspace,
|
||||
prepare_sandbox,
|
||||
)
|
||||
from workflow_bench.review_scoring import REVIEW_OUTPUT, parse_review_output
|
||||
from workflow_bench.runner_sessions import run_claude
|
||||
|
||||
FAKE_CLI = Path(__file__).parent / "fixtures" / "fake_claude.py"
|
||||
REVIEW_JSON = '{"schema_version": 1, "verdict": "approve", "findings": []}'
|
||||
|
||||
|
||||
def _session(clone: Path, provider: MockProvider, **overrides):
|
||||
return run_claude(
|
||||
"review the change",
|
||||
clone,
|
||||
claude_bin=str(FAKE_CLI),
|
||||
timeout=60,
|
||||
env={
|
||||
"ANTHROPIC_BASE_URL": provider.base_url,
|
||||
"ANTHROPIC_API_KEY": "offline",
|
||||
"PATH": "/usr/bin:/bin",
|
||||
},
|
||||
**overrides,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def clone(tmp_path: Path) -> Path:
|
||||
workspace = tmp_path / "clone"
|
||||
workspace.mkdir()
|
||||
(workspace / "source.ts").write_text("export const answer = 42;\n")
|
||||
return workspace
|
||||
|
||||
|
||||
def test_a_session_records_the_usage_the_provider_reported(clone: Path) -> None:
|
||||
"""Token counts must survive the CLI boundary, not be invented after it."""
|
||||
|
||||
reply = Reply(input_tokens=2_000, output_tokens=300, cache_read_input_tokens=7_000, cache_creation_input_tokens=1_000)
|
||||
with MockProvider(default=reply) as provider:
|
||||
record = _session(clone, provider)
|
||||
|
||||
assert record["ok"] is True, record.get("error_detail")
|
||||
assert record["input_tokens"] == 2_000
|
||||
assert record["cache_read_input_tokens"] == 7_000
|
||||
assert record["cache_creation_input_tokens"] == 1_000
|
||||
assert record["output_tokens"] == 300
|
||||
# A measured zero would be indistinguishable from an unmeasured one.
|
||||
assert record["cost_usd"] == 0.42
|
||||
assert record["num_turns"] == 1
|
||||
|
||||
|
||||
def test_a_scripted_write_produces_a_review_artifact_the_scorer_accepts(clone: Path) -> None:
|
||||
"""The full artifact path: model asks, CLI writes atomically, scorer reads.
|
||||
|
||||
This is the operation that shipped empty for a whole run. Nothing here
|
||||
fakes the write, the directory, or the parse - only the decision to write.
|
||||
"""
|
||||
|
||||
with prepare_sandbox(
|
||||
clone=clone, claude_bin=Path(sys.executable), backend="host-unsafe", preflight=False
|
||||
) as sandbox:
|
||||
artifact = prepare_review_workspace(sandbox, REVIEW_OUTPUT)
|
||||
write = {"name": "Write", "input": {"file_path": str(artifact), "content": REVIEW_JSON}}
|
||||
with MockProvider(default=Reply(text="reviewing", tools=[write])) as provider:
|
||||
# Take the command configuration from the sandbox the way run_arm
|
||||
# does, rather than calling run_claude bare. On host-unsafe the
|
||||
# prefix is [] by construction, so this pins the WIRING, not the
|
||||
# isolation - a bwrap run would carry a real prefix through here.
|
||||
record = _session(
|
||||
clone,
|
||||
provider,
|
||||
command_prefix=sandbox.command_prefix_for(),
|
||||
require_pid_namespace=sandbox.require_pid_namespace,
|
||||
)
|
||||
assert record["ok"] is True, record.get("error_detail")
|
||||
# Read inside the scope: prepare_sandbox removes the private root on exit.
|
||||
verdict, findings = parse_review_output(artifact)
|
||||
|
||||
assert verdict == "approve"
|
||||
assert findings == ()
|
||||
|
||||
|
||||
def test_the_provider_saw_the_prompt_the_harness_meant_to_send(clone: Path) -> None:
|
||||
"""A run that measures the wrong prompt measures nothing."""
|
||||
|
||||
with MockProvider() as provider:
|
||||
_session(clone, provider)
|
||||
|
||||
assert provider.requests, "the session never reached the provider"
|
||||
sent = provider.requests[0].body["messages"][0]["content"]
|
||||
assert "review the change" in sent
|
||||
|
||||
|
||||
def test_a_provider_failure_surfaces_as_a_failed_session_not_a_silent_pass(clone: Path) -> None:
|
||||
"""An upstream 529 must not be recorded as a usable measurement."""
|
||||
|
||||
failing = Reply(status_code=529, error_body={"error": {"type": "overloaded_error"}})
|
||||
with MockProvider(default=failing) as provider:
|
||||
record = _session(clone, provider)
|
||||
|
||||
assert record["ok"] is False
|
||||
assert record["error_kind"] is not None
|
||||
|
||||
|
||||
def test_the_write_boundary_refuses_the_workspace_and_permits_the_artifact(clone: Path, tmp_path: Path) -> None:
|
||||
"""The contract the empty-artifact run violated, on the backend available here.
|
||||
|
||||
A review must not change the workspace, and must still be able to write its
|
||||
artifact ATOMICALLY - temp file beside the target, then rename - which is
|
||||
what needs a writable parent DIRECTORY rather than a writable file. Both
|
||||
halves are asserted through the real session, with the real boundary
|
||||
applied, and the model scripted to attempt each one.
|
||||
|
||||
Scope: this is the host-unsafe boundary, which its own docstring calls
|
||||
best-effort because a session that can chmod can undo it. The kernel-enforced
|
||||
version is bubblewrap's --ro-bind, which needs namespaces this machine cannot
|
||||
create; that half stays with the real-sandbox canary in CI.
|
||||
"""
|
||||
|
||||
artifacts = tmp_path / "artifacts"
|
||||
artifacts.mkdir()
|
||||
target = artifacts / REVIEW_OUTPUT
|
||||
protected = clone / "source.ts"
|
||||
before = protected.read_text()
|
||||
|
||||
write_artifact = {"name": "Write", "input": {"file_path": str(target), "content": REVIEW_JSON}}
|
||||
tamper = {"name": "Write", "input": {"file_path": str(protected), "content": "tampered"}}
|
||||
|
||||
# No writable= entry: the boundary only governs paths INSIDE the workspace
|
||||
# (it refuses one that escapes), and the artifact directory deliberately
|
||||
# lives outside it - that relocation is the fix for the empty-artifact run.
|
||||
with host_workspace_write_boundary(clone):
|
||||
with MockProvider(default=Reply(text="writing", tools=[write_artifact, tamper])) as provider:
|
||||
record = _session(clone, provider)
|
||||
|
||||
assert record["ok"] is True, record.get("error_detail")
|
||||
# The artifact landed, written the way the agent's Write tool does it.
|
||||
verdict, _findings = parse_review_output(target)
|
||||
assert verdict == "approve"
|
||||
assert not list(artifacts.glob("*.tmp.*")), "the rename landed rather than a copy"
|
||||
# The workspace did not move.
|
||||
assert protected.read_text() == before, "the read-only workspace was modified"
|
||||
|
||||
|
||||
def test_a_reply_missing_cache_usage_is_refused_not_zero_filled(clone: Path) -> None:
|
||||
"""An omitted cache field must not arrive as a measured zero.
|
||||
|
||||
The parent already demands all four USAGE_FIELDS before it calls a session
|
||||
measured (runner_sessions.well_formed). The stand-in used to default the
|
||||
absent ones to 0, which both fabricated a complete measurement AND made
|
||||
that parent guard unfirable from any offline test - it was always
|
||||
satisfied. Scripting the absence is what proves the guard still fires.
|
||||
"""
|
||||
|
||||
partial = Reply(input_tokens=2_000, output_tokens=300, cache_read_input_tokens=None)
|
||||
with MockProvider(default=partial) as provider:
|
||||
record = _session(clone, provider)
|
||||
|
||||
assert record["ok"] is False, "an incomplete usage report is not a usable measurement"
|
||||
assert record["error_kind"] == "session-error"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("bad", [-5, True, "1200"], ids=["negative", "boolean", "string"])
|
||||
def test_a_nonsense_cache_value_is_refused_rather_than_forwarded(clone: Path, bad: object) -> None:
|
||||
"""A field good enough to report is good enough to validate.
|
||||
|
||||
The parent's well_formed check tests only that the four keys are PRESENT,
|
||||
so an unvalidated cache value would ride into a success result and be
|
||||
recorded as a real measurement.
|
||||
"""
|
||||
|
||||
reply = Reply(input_tokens=2_000, output_tokens=300)
|
||||
object.__setattr__(reply, "cache_read_input_tokens", bad)
|
||||
with MockProvider(default=reply) as provider:
|
||||
record = _session(clone, provider)
|
||||
|
||||
assert record["ok"] is False, f"{bad!r} must not be recorded as a measured cache value"
|
||||
348
eval/tests/test_offline_sweep_integration.py
Normal file
348
eval/tests/test_offline_sweep_integration.py
Normal file
|
|
@ -0,0 +1,348 @@
|
|||
"""A whole sweep, offline: real runner, real sessions, scripted model.
|
||||
|
||||
The layers between a model turn and a promotion decision had never been
|
||||
exercised together. Unit tests covered each in isolation and the paid runs that
|
||||
would have covered the composition kept dying, so the contracts BETWEEN them
|
||||
went unverified - and that is where this harness has repeatedly shipped bugs.
|
||||
|
||||
This drives runner.main() the way the workflow does. Everything is real: task
|
||||
selection, hidden-oracle capture, the sandbox, the CLI subprocess, artifact
|
||||
capture, review scoring against the oracle, aggregation, the health guard, and
|
||||
the promotion gate. Only the model is scripted, through MockProvider.
|
||||
|
||||
Two provisioning steps are stubbed because this environment cannot supply them,
|
||||
and neither is harness logic: the pinned gitnexus runtime mounts (no
|
||||
node_modules in a worktree) and the sanitized graph build (needs the gitnexus
|
||||
CLI at a mounted path). Containment is host-unsafe here; bubblewrap stays with
|
||||
the real-sandbox canary in the containment job.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
|
||||
import os
|
||||
import shutil
|
||||
|
||||
import pytest
|
||||
|
||||
from workflow_bench import oracle_assets, runner
|
||||
from workflow_bench.mock_provider import MockProvider, Reply
|
||||
|
||||
FAKE_CLI = Path(__file__).parent / "fixtures" / "fake_claude.py"
|
||||
ARMS = ("ce_review", "review", "candidate_review")
|
||||
|
||||
# When set, the sweep runs with NOTHING provisioning-stubbed: real bubblewrap
|
||||
# containment, the real pinned runtime mounts, and the real sanitized graph
|
||||
# build. The named CI job installs all three, so a missing one there is a
|
||||
# regression rather than an unsupported machine - it FAILS instead of quietly
|
||||
# degrading to the stubbed path, which is the whole point of the gate.
|
||||
FULL_SWEEP_ENV = "GITNEXUS_REQUIRE_FULL_SWEEP"
|
||||
FULL_SWEEP = os.environ.get(FULL_SWEEP_ENV) == "1"
|
||||
|
||||
# The runner refuses --unsafe-no-bwrap whenever CI is set, because that mode runs
|
||||
# sessions with bypassPermissions behind a boundary its own docstring calls "not
|
||||
# a security boundary". Deleting CI to get past that refusal would run an
|
||||
# uncontained agent sweep on the runner holding the checkout and credentials, so
|
||||
# the stubbed path is skipped under CI instead. The containment job sets
|
||||
# GITNEXUS_REQUIRE_FULL_SWEEP=1 and takes the real bubblewrap path, so CI keeps
|
||||
# its coverage; only the uncontained convenience run is given up.
|
||||
pytestmark = pytest.mark.skipif(
|
||||
not FULL_SWEEP and bool(os.environ.get("CI")),
|
||||
reason="an uncontained sweep must not run in CI; the containment job runs it with GITNEXUS_REQUIRE_FULL_SWEEP=1",
|
||||
)
|
||||
|
||||
# The review output and the hidden labels are DELIBERATELY different shapes -
|
||||
# the labels carry line_start/line_end and no recommendation. Only a real run
|
||||
# surfaces that; it is why these are written out rather than shared.
|
||||
FINDING = {
|
||||
"id": "f1", "severity": "high", "category": "correctness", "path": "src/sum.js",
|
||||
"line": 1, "end_line": 1, "blocking": True, "scenario": "review-defect",
|
||||
"evidence": "export const total = (a, b) => a - b;", "recommendation": "use a + b",
|
||||
}
|
||||
LABEL = {"id": "f1", "severity": "high", "category": "correctness",
|
||||
"path": "src/sum.js", "line_start": 1, "line_end": 1}
|
||||
SECOND_LABEL = {"id": "f2", "severity": "high", "category": "correctness",
|
||||
"path": "src/scale.js", "line_start": 1, "line_end": 1}
|
||||
SECOND_FINDING = {
|
||||
"id": "f2", "severity": "high", "category": "correctness", "path": "src/scale.js",
|
||||
"line": 1, "end_line": 1, "blocking": True, "scenario": "review-defect",
|
||||
"evidence": "export const twice = (n) => n + 2;", "recommendation": "use n * 2",
|
||||
}
|
||||
|
||||
|
||||
def _git(repo: Path, *args: str) -> str:
|
||||
return subprocess.run(["git", "-C", str(repo), *args], check=True,
|
||||
capture_output=True, text=True).stdout.strip()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def bench(tmp_path: Path):
|
||||
"""A self-contained corpus: one repo, one task, one hidden label."""
|
||||
|
||||
repo = tmp_path / "repo"
|
||||
(repo / "src").mkdir(parents=True)
|
||||
(repo / "src" / "sum.js").write_text("export const total = (a, b) => a - b;\n")
|
||||
(repo / "src" / "scale.js").write_text("export const twice = (n) => n + 2;\n")
|
||||
_git(repo, "init", "-q", ".")
|
||||
_git(repo, "config", "user.email", "t@t")
|
||||
_git(repo, "config", "user.name", "t")
|
||||
_git(repo, "add", "-A")
|
||||
_git(repo, "commit", "-q", "-m", "fixture")
|
||||
sha = _git(repo, "rev-parse", "HEAD")
|
||||
|
||||
oracles = tmp_path / "oracles"
|
||||
oracles.mkdir()
|
||||
(oracles / "review-fixture-defect.labels.json").write_text(
|
||||
json.dumps({"schema_version": 1, "findings": [LABEL]})
|
||||
)
|
||||
(oracles / "review-fixture-second.labels.json").write_text(
|
||||
json.dumps({"schema_version": 1, "findings": [SECOND_LABEL]})
|
||||
)
|
||||
|
||||
tasks = tmp_path / "tasks.yaml"
|
||||
tasks.write_text(
|
||||
"tasks:\n"
|
||||
" - id: review-fixture-defect\n"
|
||||
" class: review-defect\n"
|
||||
f" repo: {repo}\n"
|
||||
f" ref: {sha}\n"
|
||||
" prompt: Review this change and report actionable defects.\n"
|
||||
' verify: test -s "$GITNEXUS_BENCH_REVIEW_OUTPUT"\n'
|
||||
" oracle:\n"
|
||||
' command: test -s "$GITNEXUS_BENCH_REVIEW_OUTPUT"\n'
|
||||
" files: [{ source: review-fixture-defect.labels.json, target: review-labels.json }]\n"
|
||||
# A SECOND task, because the thing a cross-task scheduler changes is
|
||||
# invisible with one: waves are per-task, so a single task cannot show
|
||||
# ordering, packing, or a breaker that spans a task boundary.
|
||||
" - id: review-fixture-second\n"
|
||||
" class: review-defect\n"
|
||||
f" repo: {repo}\n"
|
||||
f" ref: {sha}\n"
|
||||
" prompt: Review the scaling helper and report actionable defects.\n"
|
||||
' verify: test -s "$GITNEXUS_BENCH_REVIEW_OUTPUT"\n'
|
||||
" oracle:\n"
|
||||
' command: test -s "$GITNEXUS_BENCH_REVIEW_OUTPUT"\n'
|
||||
" files: [{ source: review-fixture-second.labels.json, target: review-labels.json }]\n"
|
||||
)
|
||||
|
||||
plugin = tmp_path / "ce-plugin"
|
||||
(plugin / ".claude-plugin").mkdir(parents=True)
|
||||
(plugin / ".claude-plugin" / "plugin.json").write_text(
|
||||
json.dumps({"name": "compound-engineering", "version": "0.0.0-fixture"})
|
||||
)
|
||||
for skill in ("ce-plan", "ce-work", "ce-code-review"):
|
||||
directory = plugin / "skills" / skill
|
||||
directory.mkdir(parents=True)
|
||||
(directory / "SKILL.md").write_text(f"---\nname: {skill}\ndescription: fixture\n---\nFixture.\n")
|
||||
|
||||
overlay = tmp_path / "overlay" / ".claude" / "skills" / "gitnexus-review"
|
||||
overlay.mkdir(parents=True)
|
||||
(overlay / "SKILL.md").write_text("---\nname: gitnexus-review\ndescription: fixture\n---\nCandidate.\n")
|
||||
|
||||
return SimpleNamespace(tasks=tasks, oracles=oracles, plugin=plugin,
|
||||
overlay=tmp_path / "overlay", out=tmp_path / "out")
|
||||
|
||||
|
||||
def _stub_provisioning(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
"""Replace what this machine cannot supply - and nothing else.
|
||||
|
||||
Under FULL_SWEEP nothing is replaced: the runtime mounts and the graph are
|
||||
built for real, so the sweep exercises containment and provisioning too.
|
||||
"""
|
||||
|
||||
if FULL_SWEEP:
|
||||
if shutil.which("bwrap") is None:
|
||||
pytest.fail(f"{FULL_SWEEP_ENV}=1 but bubblewrap is absent")
|
||||
return
|
||||
|
||||
monkeypatch.setattr(runner, "trusted_gitnexus_runtime_mounts", lambda: ())
|
||||
|
||||
def materialize(worktree, *, sanitized_head=None, **_kwargs):
|
||||
# The one-clone registry guard reads this before any session runs.
|
||||
meta = Path(worktree) / ".gitnexus"
|
||||
meta.mkdir(parents=True, exist_ok=True)
|
||||
(meta / "meta.json").write_text(
|
||||
json.dumps({"indexedAt": "2026-09-08T00:00:00Z", "lastCommit": sanitized_head or "0" * 40})
|
||||
)
|
||||
|
||||
def fake_graph(**kwargs):
|
||||
kwargs["env"].graph_snapshots[kwargs["graph_key"]] = SimpleNamespace(
|
||||
digest="fixture-graph", manifest_digest="fixture-graph-manifest",
|
||||
dependency_content_digest=None, dependency_manifest_digest=None,
|
||||
materialize=materialize,
|
||||
)
|
||||
|
||||
monkeypatch.setattr(runner, "ensure_task_graph", fake_graph)
|
||||
|
||||
|
||||
def _sweep(bench, monkeypatch: pytest.MonkeyPatch, findings: list[dict], verdict: str, *, invoke_skill: bool = True):
|
||||
"""Run the real CLI against a model scripted to return `findings`."""
|
||||
|
||||
_stub_provisioning(monkeypatch)
|
||||
monkeypatch.setattr(
|
||||
oracle_assets, "ORACLE_ROOT", bench.oracles, raising=False
|
||||
)
|
||||
monkeypatch.setattr(
|
||||
runner, "capture_task_oracles",
|
||||
lambda tasks, root=bench.oracles: oracle_assets.capture_task_oracles(tasks, root=root),
|
||||
)
|
||||
def review_for(body: str) -> str:
|
||||
# Per task: the second task's defect is in another file, so replying
|
||||
# with the first task's finding would score it wrong. A cross-task
|
||||
# scheduler makes which task a request belongs to load-bearing.
|
||||
chosen = findings
|
||||
if findings and "scaling helper" in body:
|
||||
chosen = [SECOND_FINDING if f is FINDING else f for f in findings]
|
||||
return json.dumps({"schema_version": 1, "verdict": verdict, "findings": chosen})
|
||||
|
||||
class Scripted(MockProvider):
|
||||
def next_reply(self) -> Reply:
|
||||
body = json.dumps(self.requests[-1].body if self.requests else {})
|
||||
target = re.search(r"(/[^\s\"']*review-output\.json)", body)
|
||||
skill = re.search(r"\b(gitnexus-review|ce-code-review)\b", body)
|
||||
return Reply(
|
||||
text="reviewing",
|
||||
tools=[
|
||||
# The evidence gate needs a Skill request with a non-error
|
||||
# result: a review that never invoked its skill measured the
|
||||
# model, not the skill.
|
||||
*([{"name": "Skill", "input": {"skill": skill.group(1) if skill else "gitnexus-review"}}]
|
||||
if invoke_skill else []),
|
||||
{"name": "Write", "input": {
|
||||
"file_path": target.group(1) if target else str(bench.out / "unmatched-review-output.json"),
|
||||
"content": review_for(body)}},
|
||||
],
|
||||
input_tokens=2_000, output_tokens=300,
|
||||
cache_read_input_tokens=7_000, cache_creation_input_tokens=1_000,
|
||||
)
|
||||
|
||||
with Scripted() as provider:
|
||||
monkeypatch.setattr(sys, "argv", [
|
||||
"runner", "--tasks", str(bench.tasks), "--arms", *ARMS,
|
||||
"--runs", "1", "--workers", "1", "--out", str(bench.out),
|
||||
"--base-url", provider.base_url, "--anthropic-api-key", "offline",
|
||||
"--claude-bin", str(FAKE_CLI),
|
||||
*([] if FULL_SWEEP else ["--unsafe-no-bwrap"]),
|
||||
"--model", "mock-model",
|
||||
"--ce-plugin-dir", str(bench.plugin), "--ce-plugin-version", "0.0.0-fixture",
|
||||
"--candidate-overlay", str(bench.overlay),
|
||||
])
|
||||
|
||||
try:
|
||||
code = runner.main()
|
||||
except SystemExit as exc:
|
||||
code = exc.code
|
||||
|
||||
rows = [json.loads(line) for line in (bench.out / "results.jsonl").read_text().splitlines()]
|
||||
return code, rows, provider
|
||||
|
||||
|
||||
def _row(rows: list[dict], arm: str, task: str = "review-fixture-defect") -> dict:
|
||||
return next(r for r in rows if r["arm"] == arm and r["task"] == task)
|
||||
|
||||
|
||||
def test_a_correct_review_scores_and_the_sweep_exits_clean(bench, monkeypatch) -> None:
|
||||
"""The whole path, green: every arm measured, scored, and accounted for."""
|
||||
|
||||
code, rows, provider = _sweep(bench, monkeypatch, [FINDING], "request_changes")
|
||||
|
||||
assert code in (None, 0), f"sweep did not succeed: {code}"
|
||||
tasks = {"review-fixture-defect", "review-fixture-second"}
|
||||
assert len(rows) == len(ARMS) * len(tasks)
|
||||
assert len(provider.requests) == len(ARMS) * len(tasks), "each cell must reach the provider once"
|
||||
assert {r["task"] for r in rows} == tasks, "both tasks must have run"
|
||||
|
||||
row = _row(rows, "review")
|
||||
assert row["ok"] is True and row["resolved"] is True
|
||||
assert row["skill_invoked"] is True
|
||||
assert (row["review_true_positives"], row["review_false_positives"], row["review_false_negatives"]) == (1, 0, 0)
|
||||
assert row["review_f1"] == 1.0
|
||||
# The provider's own numbers survived the CLI, the parser and the row.
|
||||
assert row["cache_read_input_tokens"] == 7_000
|
||||
assert row["input_tokens"] == 2_000
|
||||
|
||||
# Each task scored against ITS OWN oracle. This is what a cross-task
|
||||
# scheduler puts at risk: interleaving cells from different tasks means a
|
||||
# mis-routed context or artifact scores one task against another's labels,
|
||||
# and both would still look "green" per row.
|
||||
second = _row(rows, "review", task="review-fixture-second")
|
||||
assert second["resolved"] is True and second["review_f1"] == 1.0
|
||||
assert second["review_artifact"] == "review-fixture-second-review-run0.review.json"
|
||||
|
||||
for name in ("results.jsonl", "report.md", "promotion.json"):
|
||||
assert (bench.out / name).is_file(), f"{name} was not written"
|
||||
assert (bench.out / "review-fixture-defect-review-run0.review.json").is_file()
|
||||
|
||||
|
||||
def test_one_run_cannot_promote_a_candidate(bench, monkeypatch) -> None:
|
||||
"""The gate refuses on insufficient paired runs, and says so."""
|
||||
|
||||
_sweep(bench, monkeypatch, [FINDING], "request_changes")
|
||||
promotion = json.loads((bench.out / "promotion.json").read_text())
|
||||
|
||||
assert promotion["run_status"] == "complete"
|
||||
decision = next(d for d in promotion["decisions"] if d["candidate_arm"] == "candidate_review")
|
||||
assert decision["decision"] == "insufficient_evidence"
|
||||
assert any("valid paired runs" in reason for reason in decision["reasons"])
|
||||
|
||||
|
||||
def test_a_finding_in_the_wrong_place_scores_zero_but_stays_valid_evidence(bench, monkeypatch) -> None:
|
||||
"""Being wrong is a quality result, not a broken measurement.
|
||||
|
||||
The negative control that makes the passing case mean something: same
|
||||
harness, same well-formed artifact, only the answer changed.
|
||||
"""
|
||||
|
||||
wrong = {**FINDING, "path": "src/WRONG.js", "line": 99, "end_line": 99}
|
||||
_code, rows, _provider = _sweep(bench, monkeypatch, [wrong], "request_changes")
|
||||
|
||||
row = _row(rows, "review")
|
||||
assert (row["review_true_positives"], row["review_false_positives"], row["review_false_negatives"]) == (0, 1, 1)
|
||||
assert row["review_f1"] == 0.0
|
||||
assert row["resolved"] is False
|
||||
assert row["error_kind"] == "oracle-failed", "a wrong answer is not a session or evidence failure"
|
||||
assert row["review_evidence_valid"] is True, "the artifact was well formed; only the answer was wrong"
|
||||
|
||||
|
||||
def test_approving_defective_code_is_a_miss_with_no_false_positive(bench, monkeypatch) -> None:
|
||||
"""The other half of the control: silence scores differently from a wrong guess."""
|
||||
|
||||
_code, rows, _provider = _sweep(bench, monkeypatch, [], "approve")
|
||||
|
||||
row = _row(rows, "review")
|
||||
assert (row["review_true_positives"], row["review_false_positives"], row["review_false_negatives"]) == (0, 0, 1)
|
||||
assert row["review_precision"] is None, "precision is undefined with no predictions, not zero"
|
||||
assert row["review_verdict_correct"] is False, "approving defective code is the wrong verdict"
|
||||
assert row["review_evidence_valid"] is True
|
||||
|
||||
|
||||
def test_a_review_that_never_invoked_its_skill_is_not_a_measurement(bench, monkeypatch) -> None:
|
||||
"""The gate that separates measuring a SKILL from measuring a model.
|
||||
|
||||
Added because a mutation exposed it: forcing skill_was_invoked_events to
|
||||
return True left every other test here passing, so nothing pinned the gate.
|
||||
The artifact is written and correct in this run - only the skill request is
|
||||
missing - so a pass would mean the arm scored a review it never performed.
|
||||
"""
|
||||
|
||||
code, rows, _provider = _sweep(bench, monkeypatch, [FINDING], "request_changes", invoke_skill=False)
|
||||
|
||||
row = _row(rows, "review")
|
||||
assert row["skill_invoked"] is False
|
||||
assert row["error_kind"] == "skill-not-invoked"
|
||||
assert code not in (None, 0), "the sweep must not report success on unusable evidence"
|
||||
|
||||
# The row still carries its own score - the artifact was well formed - and
|
||||
# aggregate() DOES count it in the arm's quality median (the KNOWN GAP noted
|
||||
# above aggregate(); test_workflow_bench pins the resulting 0.5). Filtering
|
||||
# it out of the median alone inverted a promotion, because valid_runs and
|
||||
# excluded_runs kept counting it. It counts for cost either way: the session
|
||||
# ran and was billed.
|
||||
assert row["review_weighted_f1"] == 1.0
|
||||
assert row["review_evidence_valid"] is True
|
||||
|
|
@ -24,7 +24,7 @@ from workflow_bench.model_gateway import (
|
|||
)
|
||||
from workflow_bench.provider_usage import (
|
||||
ANTHROPIC,
|
||||
OPENAI_RESPONSES,
|
||||
LITELLM_NORMALIZED,
|
||||
USAGE_ENV_VARS,
|
||||
normalize_usage,
|
||||
)
|
||||
|
|
@ -49,11 +49,16 @@ def _openai_response(usage: dict) -> SimpleNamespace:
|
|||
)
|
||||
|
||||
|
||||
# The shape a callback actually receives: LiteLLM normalises usage into its own
|
||||
# Chat-Completions-style object before any logger sees it, so an OpenAI reply
|
||||
# arrives as prompt_tokens / prompt_tokens_details. Confirmed against a real
|
||||
# proxy in tests/test_mock_provider.py; a fixture in the wire shape would test
|
||||
# an object this code path never gets.
|
||||
NATIVE = {
|
||||
"input_tokens": 48_000,
|
||||
"input_tokens_details": {"cached_tokens": 44_000, "cache_write_tokens": 1_000},
|
||||
"output_tokens": 900,
|
||||
"output_tokens_details": {"reasoning_tokens": 640},
|
||||
"prompt_tokens": 48_000,
|
||||
"prompt_tokens_details": {"cached_tokens": 44_000, "cache_write_tokens": 1_000},
|
||||
"completion_tokens": 900,
|
||||
"completion_tokens_details": {"reasoning_tokens": 640},
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -79,9 +84,9 @@ def test_native_openai_usage_survives_the_anthropic_translation(logged) -> None:
|
|||
event = logged(NATIVE)
|
||||
native = event["native_usage"]
|
||||
# Verbatim: the fields an Anthropic-shaped response cannot carry.
|
||||
assert native["input_tokens_details"]["cached_tokens"] == 44_000
|
||||
assert native["input_tokens_details"]["cache_write_tokens"] == 1_000
|
||||
assert native["output_tokens_details"]["reasoning_tokens"] == 640
|
||||
assert native["prompt_tokens_details"]["cached_tokens"] == 44_000
|
||||
assert native["prompt_tokens_details"]["cache_write_tokens"] == 1_000
|
||||
assert native["completion_tokens_details"]["reasoning_tokens"] == 640
|
||||
assert event["response_id"] == "resp_68f2c1"
|
||||
|
||||
|
||||
|
|
@ -100,7 +105,10 @@ def test_the_captured_event_normalizes_with_openai_arithmetic(logged) -> None:
|
|||
event = logged(NATIVE)
|
||||
# The provider the LOG recorded, not one the test supplies - passing
|
||||
# OPENAI_RESPONSES by hand here is what hid the adapter-key mismatch.
|
||||
assert event["provider"] == OPENAI_RESPONSES
|
||||
# LITELLM_NORMALIZED, not OPENAI_RESPONSES: a proxy callback never sees the
|
||||
# upstream body. Measured against a real gateway - the Responses adapter
|
||||
# found none of its keys there and reported every field unknown.
|
||||
assert event["provider"] == LITELLM_NORMALIZED
|
||||
assert event["provider_label"] == "openai"
|
||||
usage = normalize_usage(event["provider"], event["native_usage"])
|
||||
assert usage.total_input_tokens == 48_000
|
||||
|
|
@ -112,7 +120,7 @@ def test_the_captured_event_normalizes_with_openai_arithmetic(logged) -> None:
|
|||
def test_usage_without_details_normalizes_to_unknown_rather_than_zero(logged) -> None:
|
||||
"""The mutation the accounting must not survive: dropped details, silent zeros."""
|
||||
|
||||
stripped = {k: v for k, v in NATIVE.items() if k != "input_tokens_details"}
|
||||
stripped = {k: v for k, v in NATIVE.items() if k != "prompt_tokens_details"}
|
||||
event = logged(stripped)
|
||||
usage = normalize_usage(event["provider"], event["native_usage"])
|
||||
assert usage.cache_read_input_tokens is None
|
||||
|
|
@ -238,10 +246,16 @@ def test_an_unresolvable_provider_is_refused_rather_than_guessed() -> None:
|
|||
|
||||
from workflow_bench.provider_usage import canonical_provider
|
||||
|
||||
assert canonical_provider("openai", "responses") == OPENAI_RESPONSES
|
||||
assert canonical_provider("openai", "completion") is None
|
||||
assert canonical_provider("openai", None) is None
|
||||
# Every openai call reaching this callback has already been normalised by
|
||||
# LiteLLM, whatever endpoint it used - the observed call_type for a Claude
|
||||
# Code request through the gateway is "anthropic_messages". The adapter has
|
||||
# to match the object in hand, not the protocol on the wire.
|
||||
assert canonical_provider("openai", "responses") == LITELLM_NORMALIZED
|
||||
assert canonical_provider("openai", "anthropic_messages") == LITELLM_NORMALIZED
|
||||
assert canonical_provider("anthropic", "completion") == ANTHROPIC
|
||||
# An unrecognised provider is still refused rather than guessed.
|
||||
assert canonical_provider("some-new-provider", "responses") is None
|
||||
assert canonical_provider(None, None) is None
|
||||
|
||||
|
||||
def test_request_identity_cannot_come_from_the_proxy_environment() -> None:
|
||||
|
|
|
|||
|
|
@ -195,6 +195,11 @@ def test_eval_ci_uses_locked_uv_and_blocking_native_containment_jobs():
|
|||
assert containment["env"] == {
|
||||
"GITNEXUS_REQUIRE_BWRAP_CANARY": "1",
|
||||
"GITNEXUS_REQUIRE_CLAUDE_CANARY": "1",
|
||||
# This job is the only place with bubblewrap, the pinned runtime and a
|
||||
# built GitNexus together, so it is where the offline sweep runs with
|
||||
# nothing provisioning-stubbed. Pinned here so the gate cannot be
|
||||
# dropped and leave the sweep silently running the stubbed path.
|
||||
"GITNEXUS_REQUIRE_FULL_SWEEP": "1",
|
||||
}
|
||||
assert containment["timeout-minutes"] == 20
|
||||
assert containment_node_setup["with"] == {
|
||||
|
|
@ -239,6 +244,12 @@ def test_eval_ci_uses_locked_uv_and_blocking_native_containment_jobs():
|
|||
"tests/test_proposer_sandbox.py",
|
||||
"tests/test_workflow_bench_sessions.py",
|
||||
"tests/test_ce_plugin_runtime.py",
|
||||
# The offline sweep, run here with nothing stubbed: this job is the only
|
||||
# one carrying bubblewrap, the pinned runtime and a built GitNexus.
|
||||
"tests/test_offline_sweep_integration.py",
|
||||
# Carries the real-CLI identity probe, which needs CLAUDE_CANARY_BIN -
|
||||
# set only on this job. Omitted from this list it skipped everywhere.
|
||||
"tests/test_mock_provider.py",
|
||||
"-q",
|
||||
]
|
||||
bwrap_canary_marker = re.compile(
|
||||
|
|
@ -1052,3 +1063,33 @@ def test_a_raising_packed_cell_still_persists_its_settled_siblings():
|
|||
)
|
||||
|
||||
assert (1, "review") in folded, "the sibling that completed was never recorded"
|
||||
|
||||
|
||||
def test_an_uninvoked_skill_still_counts_toward_the_arm_median():
|
||||
"""Pins a KNOWN GAP, not a desired behaviour.
|
||||
|
||||
A cell whose skill never ran still moves the arm's quality median, even
|
||||
though an arm exists to measure a SKILL. The narrow fix - filtering those
|
||||
rows out of the quality metrics - is worse than the gap: valid_runs and
|
||||
excluded_runs keep counting them, so the promotion gate sees N clean runs
|
||||
while the median came from fewer. Since the dropped rows are systematically
|
||||
an arm's worst, that biases toward promoting, and it was measured flipping
|
||||
keep_incumbent to promote.
|
||||
|
||||
Closing it honestly needs a scored-run count and a paired-equality check in
|
||||
the promotion gate. Pinned here so the half-fix cannot be reapplied without
|
||||
someone reading why it was reverted.
|
||||
"""
|
||||
|
||||
good = record(review_weighted_f1=1.0, cost_usd=2.0)
|
||||
uninvoked = record(
|
||||
review_weighted_f1=0.0, cost_usd=4.0, error_kind="skill-not-invoked", skill_invoked=False
|
||||
)
|
||||
agg = aggregate([good, uninvoked])
|
||||
|
||||
assert agg["review_weighted_f1"] == 0.5, "the uninvoked row is counted - the known gap"
|
||||
assert agg["cost_usd"] == 3.0
|
||||
# The invariant that makes the half-fix unsafe: the median and the run count
|
||||
# the gate reads must cover the same rows.
|
||||
assert agg["valid_runs"] == 2
|
||||
|
||||
|
|
|
|||
|
|
@ -42,8 +42,8 @@ def canonical_provider(label, call_type): # noqa: ANN001, ANN201
|
|||
above for why this is a copy rather than an import.
|
||||
"""
|
||||
|
||||
if label == "openai" and call_type and "responses" in call_type:
|
||||
return "openai-responses"
|
||||
if label == "openai":
|
||||
return "litellm-normalized"
|
||||
if label == "anthropic":
|
||||
return "anthropic"
|
||||
return None
|
||||
|
|
@ -104,11 +104,10 @@ class ProviderUsageLogger(CustomLogger):
|
|||
"requested_model": kwargs.get("model"),
|
||||
"actual_model": getattr(response_obj, "model", None),
|
||||
# Two fields, because they answer different questions. The raw
|
||||
# label is what LiteLLM said; "provider" is the adapter key,
|
||||
# which needs the call type too - LiteLLM reports "openai" for
|
||||
# both Chat Completions and Responses and those report usage
|
||||
# differently. Unresolvable stays None so normalize_usage
|
||||
# refuses rather than guessing token semantics.
|
||||
# label is what LiteLLM said; "provider" is the adapter key for
|
||||
# the object actually in hand, which is always LiteLLM's own
|
||||
# normalised shape here. An unrecognised label stays None so
|
||||
# normalize_usage refuses rather than guessing token semantics.
|
||||
"provider_label": provider_label,
|
||||
"provider": canonical_provider(provider_label, call_type),
|
||||
"response_id": getattr(response_obj, "id", None),
|
||||
|
|
|
|||
284
eval/workflow_bench/mock_provider.py
Normal file
284
eval/workflow_bench/mock_provider.py
Normal file
|
|
@ -0,0 +1,284 @@
|
|||
"""A scriptable stand-in for Anthropic and OpenAI, for running the harness offline.
|
||||
|
||||
Every defect this benchmark shipped in the last round was invisible to its own
|
||||
tests for the same reason: the tests exercised a layer BELOW where the code
|
||||
runs. The usage log was never written because the proxy is a subprocess with a
|
||||
constructed environment. The callback could not be imported because LiteLLM
|
||||
loads it by path. Failures went unrecorded because only the async hook was
|
||||
overridden. Each was caught by CI or review, never by a unit test, because the
|
||||
unit test called the function directly instead of driving the path that calls
|
||||
it.
|
||||
|
||||
This closes that gap without spending money. It speaks the two wire protocols
|
||||
the harness actually depends on, so a run can go through the real sandbox, the
|
||||
real Claude Code CLI, the real gateway and the real usage callback, and only
|
||||
the model is fake:
|
||||
|
||||
POST /v1/messages Anthropic Messages, streaming and non-streaming
|
||||
POST /v1/responses OpenAI Responses, which the gateway translates into
|
||||
|
||||
Point the runner at it with ``--base-url http://127.0.0.1:<port>``, which is
|
||||
the same supported path the free-model proxy documentation already uses, or
|
||||
give it to LiteLLM as ``api_base`` to exercise the gateway.
|
||||
|
||||
Scripted, not simulated: replies are supplied by the caller, so a test decides
|
||||
what the model "says", which tools it asks for, and exactly what usage it
|
||||
reports. That last part is what makes provider-native accounting testable at
|
||||
all - real cache hits are not reproducible on demand, but a declared
|
||||
``cache_read`` of 44_000 is.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import threading
|
||||
import time
|
||||
from collections import deque
|
||||
from dataclasses import dataclass, field
|
||||
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
|
||||
from typing import Any
|
||||
|
||||
|
||||
@dataclass
|
||||
class Reply:
|
||||
"""One scripted model turn.
|
||||
|
||||
``tools`` drives real tool execution: Claude Code runs what it is asked to
|
||||
run, so a reply carrying a Write block makes the CLI write that file inside
|
||||
the sandbox for real. That is how an artifact-producing cell can be
|
||||
exercised without a model deciding anything.
|
||||
"""
|
||||
|
||||
text: str = "ok"
|
||||
tools: list[dict[str, Any]] = field(default_factory=list)
|
||||
stop_reason: str = "end_turn"
|
||||
# Anthropic accounting: input_tokens is the UNCACHED remainder and the
|
||||
# cache fields add to it. Defaults are deliberately non-zero so a test that
|
||||
# forgets to script usage still cannot mistake silence for a measurement.
|
||||
input_tokens: int = 11
|
||||
output_tokens: int = 7
|
||||
# None means the field is OMITTED from the reply, which is not the same as
|
||||
# reporting 0. A consumer that cannot tell those apart is the bug this
|
||||
# harness exists to catch, so the mock has to be able to script absence.
|
||||
cache_read_input_tokens: int | None = 0
|
||||
cache_creation_input_tokens: int | None = 0
|
||||
status_code: int = 200
|
||||
error_body: dict[str, Any] | None = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class Request:
|
||||
"""What the harness actually sent, kept so a test can assert on it."""
|
||||
|
||||
path: str
|
||||
headers: dict[str, str]
|
||||
body: dict[str, Any]
|
||||
|
||||
|
||||
class _Handler(BaseHTTPRequestHandler):
|
||||
provider: MockProvider
|
||||
|
||||
def log_message(self, *_args: Any) -> None: # noqa: A003 - silence the default stderr spam
|
||||
return
|
||||
|
||||
def do_POST(self) -> None: # noqa: N802 - BaseHTTPRequestHandler's interface
|
||||
length = int(self.headers.get("Content-Length") or 0)
|
||||
raw = self.rfile.read(length) if length else b"{}"
|
||||
try:
|
||||
body = json.loads(raw or b"{}")
|
||||
except json.JSONDecodeError:
|
||||
body = {"_unparsed": raw.decode("utf-8", "replace")}
|
||||
self.provider.record(Request(self.path, dict(self.headers), body))
|
||||
reply = self.provider.next_reply()
|
||||
|
||||
if reply.status_code != 200:
|
||||
self._send_json(reply.status_code, reply.error_body or {"error": {"message": "scripted failure"}})
|
||||
return
|
||||
if self.path.rstrip("/").endswith("/responses"):
|
||||
self._send_json(200, _openai_response(reply))
|
||||
return
|
||||
if body.get("stream"):
|
||||
self._send_anthropic_stream(reply)
|
||||
return
|
||||
self._send_json(200, _anthropic_message(reply))
|
||||
|
||||
def _send_json(self, status: int, payload: dict[str, Any]) -> None:
|
||||
encoded = json.dumps(payload).encode()
|
||||
self.send_response(status)
|
||||
self.send_header("Content-Type", "application/json")
|
||||
self.send_header("Content-Length", str(len(encoded)))
|
||||
self.end_headers()
|
||||
self.wfile.write(encoded)
|
||||
|
||||
def _send_anthropic_stream(self, reply: Reply) -> None:
|
||||
self.send_response(200)
|
||||
self.send_header("Content-Type", "text/event-stream")
|
||||
self.send_header("Cache-Control", "no-cache")
|
||||
self.end_headers()
|
||||
for event, data in _anthropic_stream_events(reply):
|
||||
self.wfile.write(f"event: {event}\ndata: {json.dumps(data)}\n\n".encode())
|
||||
self.wfile.flush()
|
||||
|
||||
|
||||
def _content_blocks(reply: Reply) -> list[dict[str, Any]]:
|
||||
blocks: list[dict[str, Any]] = [{"type": "text", "text": reply.text}]
|
||||
for index, tool in enumerate(reply.tools):
|
||||
blocks.append(
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": f"toolu_mock_{index}",
|
||||
"name": tool["name"],
|
||||
"input": tool.get("input", {}),
|
||||
}
|
||||
)
|
||||
return blocks
|
||||
|
||||
|
||||
def _anthropic_usage(reply: Reply) -> dict[str, int]:
|
||||
usage = {
|
||||
"input_tokens": reply.input_tokens,
|
||||
"output_tokens": reply.output_tokens,
|
||||
"cache_read_input_tokens": reply.cache_read_input_tokens,
|
||||
"cache_creation_input_tokens": reply.cache_creation_input_tokens,
|
||||
}
|
||||
return {field: value for field, value in usage.items() if value is not None}
|
||||
|
||||
|
||||
def _anthropic_message(reply: Reply) -> dict[str, Any]:
|
||||
return {
|
||||
"id": "msg_mock",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"model": "mock-model",
|
||||
"content": _content_blocks(reply),
|
||||
"stop_reason": "tool_use" if reply.tools else reply.stop_reason,
|
||||
"stop_sequence": None,
|
||||
"usage": _anthropic_usage(reply),
|
||||
}
|
||||
|
||||
|
||||
def _anthropic_stream_events(reply: Reply) -> list[tuple[str, dict[str, Any]]]:
|
||||
"""The SSE sequence a Messages consumer expects, in order."""
|
||||
|
||||
message = _anthropic_message(reply)
|
||||
events: list[tuple[str, dict[str, Any]]] = [
|
||||
("message_start", {"type": "message_start", "message": {**message, "content": [], "usage": _anthropic_usage(reply)}})
|
||||
]
|
||||
for index, block in enumerate(message["content"]):
|
||||
if block["type"] == "text":
|
||||
events.append(("content_block_start", {"type": "content_block_start", "index": index, "content_block": {"type": "text", "text": ""}}))
|
||||
events.append(("content_block_delta", {"type": "content_block_delta", "index": index, "delta": {"type": "text_delta", "text": block["text"]}}))
|
||||
else:
|
||||
events.append(("content_block_start", {"type": "content_block_start", "index": index, "content_block": {"type": "tool_use", "id": block["id"], "name": block["name"], "input": {}}}))
|
||||
events.append(("content_block_delta", {"type": "content_block_delta", "index": index, "delta": {"type": "input_json_delta", "partial_json": json.dumps(block["input"])}}))
|
||||
events.append(("content_block_stop", {"type": "content_block_stop", "index": index}))
|
||||
events.append(("message_delta", {"type": "message_delta", "delta": {"stop_reason": message["stop_reason"], "stop_sequence": None}, "usage": {"output_tokens": reply.output_tokens}}))
|
||||
events.append(("message_stop", {"type": "message_stop"}))
|
||||
return events
|
||||
|
||||
|
||||
def _openai_response(reply: Reply) -> dict[str, Any]:
|
||||
"""OpenAI Responses shape: input_tokens is the WHOLE, cache fields subsets."""
|
||||
|
||||
# An omitted cache field contributes nothing to the Responses total; that
|
||||
# is arithmetic, not a claim the value was measured as zero.
|
||||
cache_read = reply.cache_read_input_tokens or 0
|
||||
cache_write = reply.cache_creation_input_tokens or 0
|
||||
total_input = reply.input_tokens + cache_read + cache_write
|
||||
return {
|
||||
"id": "resp_mock",
|
||||
"object": "response",
|
||||
"created_at": int(time.time()),
|
||||
"status": "completed",
|
||||
"model": "mock-model",
|
||||
"error": None,
|
||||
"output": [
|
||||
{
|
||||
"id": "msg_mock",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"status": "completed",
|
||||
"content": [{"type": "output_text", "text": reply.text, "annotations": []}],
|
||||
},
|
||||
# Tool calls belong here too. Responses is the protocol the gateway
|
||||
# is configured for BECAUSE it carries tool use, so emitting only
|
||||
# output_text meant a reply scripted with a Write or Skill crossed
|
||||
# the gateway with the tool silently dropped - the mock would have
|
||||
# been wrong about the wire on the one path that matters most.
|
||||
*(
|
||||
{
|
||||
"id": f"fc_mock_{index}",
|
||||
"type": "function_call",
|
||||
"status": "completed",
|
||||
"call_id": f"call_mock_{index}",
|
||||
"name": tool["name"],
|
||||
"arguments": json.dumps(tool.get("input", {})),
|
||||
}
|
||||
for index, tool in enumerate(reply.tools)
|
||||
),
|
||||
],
|
||||
"usage": {
|
||||
"input_tokens": total_input,
|
||||
"output_tokens": reply.output_tokens,
|
||||
"total_tokens": total_input + reply.output_tokens,
|
||||
# Omitted stays omitted here too. Collapsing None to 0 is right for
|
||||
# the total above (an unreported field adds nothing) but wrong on
|
||||
# the wire: _int_or_none reads an absent key as unknown and a
|
||||
# present 0 as a measured zero, so serializing 0 would claim a
|
||||
# measurement the reply never made - the same confusion the
|
||||
# Anthropic path already refuses.
|
||||
"input_tokens_details": {
|
||||
**({"cached_tokens": cache_read} if reply.cache_read_input_tokens is not None else {}),
|
||||
**({"cache_write_tokens": cache_write} if reply.cache_creation_input_tokens is not None else {}),
|
||||
},
|
||||
"output_tokens_details": {"reasoning_tokens": 0},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class MockProvider:
|
||||
"""Loopback-only provider stand-in. Use as a context manager."""
|
||||
|
||||
def __init__(self, replies: list[Reply] | None = None, *, default: Reply | None = None) -> None:
|
||||
self._replies: deque[Reply] = deque(replies or [])
|
||||
# A run makes more requests than a test wants to script; the default
|
||||
# keeps it going rather than failing on the first unscripted turn.
|
||||
self._default = default or Reply()
|
||||
self._requests: list[Request] = []
|
||||
self._lock = threading.Lock()
|
||||
self._server: ThreadingHTTPServer | None = None
|
||||
|
||||
def __enter__(self) -> MockProvider:
|
||||
handler = type("_BoundHandler", (_Handler,), {"provider": self})
|
||||
# Loopback only: this answers with no authentication at all.
|
||||
self._server = ThreadingHTTPServer(("127.0.0.1", 0), handler)
|
||||
threading.Thread(target=self._server.serve_forever, daemon=True).start()
|
||||
return self
|
||||
|
||||
def __exit__(self, *_exc: object) -> bool:
|
||||
if self._server is not None:
|
||||
self._server.shutdown()
|
||||
self._server.server_close()
|
||||
return False
|
||||
|
||||
@property
|
||||
def port(self) -> int:
|
||||
assert self._server is not None, "provider is not running"
|
||||
return self._server.server_port
|
||||
|
||||
@property
|
||||
def base_url(self) -> str:
|
||||
return f"http://127.0.0.1:{self.port}"
|
||||
|
||||
def record(self, request: Request) -> None:
|
||||
with self._lock:
|
||||
self._requests.append(request)
|
||||
|
||||
def next_reply(self) -> Reply:
|
||||
with self._lock:
|
||||
return self._replies.popleft() if self._replies else self._default
|
||||
|
||||
@property
|
||||
def requests(self) -> list[Request]:
|
||||
with self._lock:
|
||||
return list(self._requests)
|
||||
|
|
@ -50,6 +50,14 @@ USAGE_ENV_VARS = (USAGE_LOG_ENV_VAR, SWEEP_ID_ENV_VAR)
|
|||
|
||||
ANTHROPIC = "anthropic"
|
||||
OPENAI_RESPONSES = "openai-responses"
|
||||
# What the gateway's callback actually receives. LiteLLM does not hand a logger
|
||||
# the upstream body: it normalises usage into its own Chat-Completions-shaped
|
||||
# object first, so an OpenAI Responses reply arrives as prompt_tokens /
|
||||
# prompt_tokens_details even though the wire carried input_tokens /
|
||||
# input_tokens_details. Measured against a real proxy, not assumed - the
|
||||
# Responses adapter below found none of its keys and reported every field
|
||||
# unknown. The arithmetic is still OpenAI's (the whole, with subsets).
|
||||
LITELLM_NORMALIZED = "litellm-normalized"
|
||||
|
||||
|
||||
class UsageSemanticsError(ValueError):
|
||||
|
|
@ -131,6 +139,35 @@ def _normalize_openai_responses(usage: Mapping[str, Any]) -> NormalizedUsage:
|
|||
)
|
||||
|
||||
|
||||
def _normalize_litellm(usage: Mapping[str, Any]) -> NormalizedUsage:
|
||||
"""prompt_tokens is the WHOLE; the details are subsets of it."""
|
||||
|
||||
total = _int_or_none(usage, "prompt_tokens")
|
||||
details = usage.get("prompt_tokens_details")
|
||||
cache_read = _int_or_none(details, "cached_tokens")
|
||||
cache_write = _int_or_none(details, "cache_write_tokens")
|
||||
if cache_write is None:
|
||||
cache_write = _int_or_none(details, "cache_creation_tokens")
|
||||
output_details = usage.get("completion_tokens_details")
|
||||
|
||||
ordinary: int | None = None
|
||||
if total is not None and cache_read is not None and cache_write is not None:
|
||||
ordinary = total - cache_read - cache_write
|
||||
if ordinary < 0:
|
||||
raise UsageSemanticsError(
|
||||
f"LiteLLM cached ({cache_read}) + cache_write ({cache_write}) "
|
||||
f"exceed prompt_tokens ({total})"
|
||||
)
|
||||
return NormalizedUsage(
|
||||
ordinary_input_tokens=ordinary,
|
||||
cache_read_input_tokens=cache_read,
|
||||
cache_write_input_tokens=cache_write,
|
||||
total_input_tokens=total,
|
||||
output_tokens=_int_or_none(usage, "completion_tokens"),
|
||||
reasoning_output_tokens=_int_or_none(output_details, "reasoning_tokens"),
|
||||
)
|
||||
|
||||
|
||||
def _normalize_anthropic(usage: Mapping[str, Any]) -> NormalizedUsage:
|
||||
"""input_tokens is the uncached REMAINDER; the cache fields add to it."""
|
||||
|
||||
|
|
@ -154,16 +191,24 @@ def _normalize_anthropic(usage: Mapping[str, Any]) -> NormalizedUsage:
|
|||
def canonical_provider(label: str | None, call_type: str | None) -> str | None:
|
||||
"""Map LiteLLM's provider label onto an adapter key, or None if unsure.
|
||||
|
||||
LiteLLM reports ``custom_llm_provider`` as "openai" for both Chat
|
||||
Completions and Responses, and those two report usage differently, so the
|
||||
label alone cannot pick an adapter. The call type is what distinguishes
|
||||
them. Returning None when it does not is deliberate: normalize_usage
|
||||
refuses an unknown provider rather than guessing token semantics, which is
|
||||
the whole point of keeping the native object authoritative.
|
||||
Every "openai" label maps to LITELLM_NORMALIZED regardless of call type,
|
||||
because anything reaching a proxy callback has already been normalised by
|
||||
LiteLLM into its own object - measured against a real gateway, where the
|
||||
observed call type is "anthropic_messages" and the upstream Responses shape
|
||||
never arrives. OPENAI_RESPONSES stays in the adapter table for a RAW
|
||||
upstream body, which only direct callers and the wire-shape tests pass.
|
||||
|
||||
An unrecognised label still returns None, so normalize_usage refuses rather
|
||||
than guessing token semantics.
|
||||
"""
|
||||
|
||||
if label == "openai" and call_type and "responses" in call_type:
|
||||
return OPENAI_RESPONSES
|
||||
if label == "openai":
|
||||
# Anything reaching a proxy callback has already been normalised by
|
||||
# LiteLLM, whichever endpoint the caller used - the observed call_type
|
||||
# for a Claude Code request through this gateway is "anthropic_messages",
|
||||
# not a Responses one. The adapter has to match the object in hand, not
|
||||
# the protocol on the wire.
|
||||
return LITELLM_NORMALIZED
|
||||
if label in _ADAPTERS:
|
||||
return label
|
||||
return None
|
||||
|
|
@ -172,6 +217,7 @@ def canonical_provider(label: str | None, call_type: str | None) -> str | None:
|
|||
_ADAPTERS = {
|
||||
ANTHROPIC: _normalize_anthropic,
|
||||
OPENAI_RESPONSES: _normalize_openai_responses,
|
||||
LITELLM_NORMALIZED: _normalize_litellm,
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1591,6 +1591,21 @@ def aggregate(records: list[dict[str, Any]]) -> dict[str, Any]:
|
|||
"review_category_accuracy",
|
||||
"review_grounded_evidence",
|
||||
)
|
||||
# NOTE: a skill-not-invoked row still contributes to these medians. That is
|
||||
# a real measurement gap - an arm exists to measure a SKILL, and a cell
|
||||
# where the skill never ran did not measure it - but the narrow fix is
|
||||
# WORSE than the gap, so it is deliberately not applied here.
|
||||
#
|
||||
# Filtering those rows out of the quality metrics alone leaves valid_runs
|
||||
# and excluded_runs counting them, so the promotion gate sees N clean runs
|
||||
# while the median was taken over fewer. Because the dropped rows are
|
||||
# systematically an arm's worst, that biases toward PROMOTING: measured on
|
||||
# one real run at 0.9 plus two uninvoked rows at 0.0, the gate flipped from
|
||||
# keep_incumbent to promote. The three verdict fields below compound it -
|
||||
# they are all() reducers, so one uninvoked cell flips a whole arm.
|
||||
# Closing this honestly needs a scored-run count and a paired-equality
|
||||
# check in the gate itself: a promotion-semantics change, not an
|
||||
# aggregation fix.
|
||||
if any("review_weighted_f1" in record for record in valid):
|
||||
for metric in review_metrics:
|
||||
values = [record[metric] for record in valid if record.get(metric) is not None]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue