ci-benchmark-analyzer

name

ci-benchmark-analyzer

description

Analyze CI benchmark workflow runs from GitHub Actions for the tt-xla project. Produces a markdown report covering failed jobs (with root-cause error extraction via logs and Glean), successful model performance metrics (samples/sec, TTFT, device perf), perf regressions/improvements vs previous nightly, and the full dependency commit chain (tt-xla, tt-mlir, tt-metal). Use this skill whenever the user wants to analyze a CI run, review nightly benchmark results, investigate CI failures, check benchmark performance from a workflow run, or asks about "latest nightly" results. Also trigger when the user pastes a GitHub Actions run URL or mentions a run ID in the context of performance analysis, or asks about perf regressions.

CI Benchmark Analyzer

Analyzes GitHub Actions benchmark workflow runs for the tenstorrent/tt-xla repository. Produces a structured markdown report with an actionable summary, failure diagnosis, performance comparison against the previous nightly, and detailed per-model metrics.

Input Handling

The user specifies a run in one of these ways:

Input	How to resolve
GitHub Actions URL	Extract run ID from actions/runs/<ID> in the URL
Numeric run ID	Use directly
latest nightly	Query the latest completed run of the "On nightly" workflow (ID 184422752)
latest benchmark	Query the latest completed run of "Performance Benchmark" workflow (ID 184422748)
latest experimental	Query the latest completed run of "On nightly Experimental" workflow (ID 187864533)

To resolve "latest" variants:

gh api "repos/tenstorrent/tt-xla/actions/workflows/<WORKFLOW_ID>/runs?per_page=1&status=completed" --jq '.workflow_runs[0].id'

If the run is still in progress, note that in the report header and analyze only the completed jobs.

Report Storage

Save reports to nightly-reports/ directory (relative to cwd) so that previous results accumulate and are accessible for future comparison. Naming convention:

nightly-reports/
  nightly_<RUN_ID>_<YYYY-MM-DD>.md

Always produce a fresh report file named after the current run ID — each run gets its own new file. The filename already encodes the run date, so there's no need for an "updated on" or "last updated" line in the report body.

Before generating, check nightly-reports/ for the most recent prior report and use it as comparison context (read-only). If no prior report exists, fetch the previous nightly run from GitHub (see step 4) to establish a baseline.

Workflow

1. Gather run metadata

gh api repos/tenstorrent/tt-xla/actions/runs/<RUN_ID> \
  --jq '{id, name, head_sha, head_branch, event, status, conclusion, created_at, updated_at, html_url}'

2. Resolve the dependency commit chain

This is important context for anyone reading the report — it tells them exactly which versions of the three core repos were used.

tt-xla commit: head_sha from step 1.

tt-mlir commit: Read third_party/CMakeLists.txt at that commit and extract TT_MLIR_VERSION:

gh api repos/tenstorrent/tt-xla/contents/third_party/CMakeLists.txt?ref=<HEAD_SHA> \
  --jq '.content' | base64 -d | grep 'set(TT_MLIR_VERSION'

Parse the SHA from the set(TT_MLIR_VERSION "...") line.

tt-metal commit: Read tt-mlir's third_party/CMakeLists.txt at the tt-mlir commit:

gh api repos/tenstorrent/tt-mlir/contents/third_party/CMakeLists.txt?ref=<TT_MLIR_SHA> \
  --jq '.content' | base64 -d | grep 'set(TT_METAL_VERSION'

3. List all jobs and classify them

gh api "repos/tenstorrent/tt-xla/actions/runs/<RUN_ID>/jobs?per_page=100" --paginate

The response may span multiple pages. Collect all jobs.

Focus exclusively on perf benchmark jobs (name contains perf-benchmark / run-perf-benchmarks / perf). Ignore all other job categories (build, test, forge, release, notify, etc.) entirely — they are not relevant to this report.

Separate failed perf jobs into two categories:

• Model failures: the benchmark itself failed (compilation error, runtime crash, device error)
• Perf regression failures: the job failed because the Check perf regression step detected >5% regression — these are NOT errors, they are perf signals

You can distinguish them by checking the job logs: regression failures have "Performance regression > 5% detected!" in the log, while model failures have stack traces, TT_FATAL, AssertionError, etc.

4. Get previous nightly for comparison

Always run the perf comparison yourself — the CI Check perf regression step is unreliable and may fail silently or use stale baselines. The comparison script is the source of truth for regressions and improvements in this report.

Always compare against the immediately preceding nightly from GitHub — never use local report files as a substitute. Nightlies run daily and a local report may be days old, making the diff misleading.

Find the nightly run that ran just before the current one:

gh api "repos/tenstorrent/tt-xla/actions/workflows/184422752/runs?per_page=5&status=completed" \
  --jq '.workflow_runs[] | {id, created_at, head_sha}'

Pick the run whose created_at is the most recent date earlier than the current run's created_at. Then run the comparison script with a 5% threshold so both summary-relevant directions are captured:

python <skill-path>/scripts/compare_nightlies.py <CURRENT_RUN_ID> <PREVIOUS_RUN_ID> \
  --output-dir /tmp/perf_compare_<RUN_ID> --threshold 5

The script outputs comparison.json with per-model diffs categorized as regression, improvement, stable, new, or missing. Use diff_percent to apply the report's asymmetric thresholds:

• Summary regressions: diff_percent <= -10
• Summary improvements: diff_percent >= +5

Local reports in nightly-reports/ are read-only context (e.g. to check if a failure is recurring) — they are not the baseline for perf diffs.

Perf diff convention (used consistently everywhere in the report):

• Positive % = improvement (model got faster, more samples/sec)
• Negative % = regression (model got slower, fewer samples/sec)

Example: "llama_3_2_1b: +15.3%" means it's 15.3% faster than the previous nightly.

5. Analyze failed perf benchmark jobs

For each failed perf benchmark job (model failures only, not regression check failures):

1. Fetch the job log:

gh api repos/tenstorrent/tt-xla/actions/jobs/<JOB_DATABASE_ID>/logs

2. Extract the root error by scanning the log for these patterns (in priority order):

   • TT_FATAL or TT_THROW — device/runtime fatal error. Extract the error message and walk up the stack trace for the nearest ttnn:: or tt::tt_metal:: reference.
   • AssertionError: — Python assertion failures. Extract the full assertion message.
   • error: lines in MLIR output — compilation failures.
   • FAILED in pytest output — test failure. Look for the short test summary.
   • RuntimeError: — Python runtime errors.
   • Performance regression > 5% detected! — this is a perf signal, NOT a job failure to report under "Failures".
   • Generic: last ##[error] line before job end.

3. Classify with a short, universal label. The reader scans these — they need to recognize the failure mode at a glance, not read prose. Don't editorialize ("marginally", "extremely", "very low"), don't speculate about meaning ("logits diverged from reference", "optimizer assigned L1-resident layout"), and don't restate the obvious. Just name the failure mode and, if useful, append the bare numbers from the log. Examples:

   Log signal	Label
   AssertionError: First decode PCC failed. PCC=X, Required=Y	First decode PCC failed. PCC=X, Required=Y
   AssertionError: <other> PCC failed. PCC=X, Required=Y	<other> PCC failed. PCC=X, Required=Y
   TT_FATAL: Out of Memory: ... L1 buffer ...	L1 OOM: <size> B across <N> banks
   TT_THROW: ... circular buffers ... clash with L1 buffers ...	CB/L1 clash: <core range>
   error: from MLIR output	Compilation error: <op or pass name>
   Other RuntimeError / TT_FATAL	Runtime error: <one-clause excerpt>

   PCC labels: use the AssertionError message verbatim (after AssertionError: ) as the label — do NOT strip the step qualifier ("First decode", "Prefill", etc.). The step where PCC failed is essential diagnostic context.

   The same label is used in the Summary bullet AND in the Failed Jobs subsection's **Error** line — they must match.

4. For device timeout / hang failures, the hanging op is critical context. After identifying the TIMEOUT: device timeout in fetch queue wait error, scan the job log for the op being executed at the time of hang. Look for lines like:

   • BinaryNg, UnaryWithParam, MatmulMultiCore, or other kernel/op names appearing shortly before the timeout
   • tt-triage output lines that list the op name and its arguments
   • Patterns like Running op: <op_name> or Dispatching kernel: <name>

   Include the hanging op in the Context subsection under a "Hang details" note, e.g.: Hang details: timed out during BinaryNg op (from job log). This makes it immediately actionable for the tt-metal/tt-mlir owners debugging the hang.

5. Use Glean to find prior context — but stay factual. Search for the error and surface what's already documented:

   mcp__glean_default__search: "<core error message> tt-xla" or "<op name> failure"
   mcp__glean_default__chat: "What is known about this error in tt-xla/tt-mlir: <error message>"
   

   Include only what the linked sources say: issue numbers, filer, file date, related PRs, Slack threads, owner. Do not draw conclusions Glean didn't state — no "no fix has merged yet", no "PCC=0.41 indicates the logits are nearly random", no characterizations of severity. If the issue itself says a fix is in flight, you can quote that; if it doesn't, leave it out. If Glean returns nothing, note that explicitly.

6. Analyze successful perf benchmark jobs

For each successful perf benchmark job, download and parse the perf report artifact using the bundled script:

python <skill-path>/scripts/fetch_perf_reports.py <RUN_ID> --output-dir /tmp/perf_reports_<RUN_ID>

This produces summary.json with extracted metrics for all jobs. Key metrics per model:

• Samples/sec = samples_per_sec measurement if present, else total_samples / total_time
• TTFT (ms) = ttft value (LLM models only; absent for vision/encoder models)
• Device FW duration (s) = device_fw_duration (only when device perf ran)
• Device type: from device_info.device_type

7. Generate the report

Write to nightly-reports/nightly_<RUN_ID>_<YYYY-MM-DD>.md. Create the directory if needed.

Report Template

The summary is the most important section — it's what people read first and sometimes the only thing they read. Keep it short, factual, and scannable. Three sections only: Failures, Perf Regressions, Perf Improvements. No prose preambles, no "Note:" callouts about commits being identical or measurement variance — the dependency chain at the bottom already shows the SHAs, the reader can compare.

# CI Benchmark Report — Run <RUN_ID>

## Summary

**Perf benchmarks**: X/Y passed

### Failures
- [model_a (n150)](#model_a-n150) — <short universal label>
- [model_b (n300-llmbox)](#model_b-n300-llmbox) — <short universal label>

[If none, write "None".]
[Anchor format: model_display_name-device_type, e.g. #llama_3_2_1b-n150]

### Perf Regressions
Models with `diff_percent <= -10` in `comparison.json` (Samples/sec dropped >10% vs previous nightly).
- model_c (n150): -15.3%

[If none, write "None". Always derive from the comparison script — never trust the CI "Check perf regression" step.]

### Perf Improvements
Models with `diff_percent >= +5` in `comparison.json` (Samples/sec improved >5% vs previous nightly).
- model_d (n150): +12.1%

[If none, write "None".]

The Run Info and Dependency Chain sections go at the END of the report (see "Run Info & Dependency Chain" below).

Failed Jobs

For each failed model, create a subsection with an anchor matching the summary link:

### <a id="model_a-n150"></a>model_a (n150)
- **Job**: [link to job](<job_url>)
- **Error**: <short universal label — same wording as the Summary bullet>

<relevant error snippet, 3-5 lines max>

- **Context**: <factual references only — issue numbers, filer + file date, related PRs, owners, Slack threads. State whether it's a known issue and link it. Do not write "no fix has merged yet", do not interpret PCC values, do not speculate about cause unless the linked issue states it explicitly.>

All issue and PR references must be hyperlinked — never write bare tt-xla#NNNN text. Use full markdown links:

• tt-xla issues/PRs: [tt-xla#NNNN](https://github.com/tenstorrent/tt-xla/issues/NNNN) (use /pull/NNNN for PRs)
• tt-mlir issues/PRs: [tt-mlir#NNNN](https://github.com/tenstorrent/tt-mlir/issues/NNNN)
• tt-metal issues/PRs: [tt-metal#NNNN](https://github.com/tenstorrent/tt-metal/issues/NNNN)

Good Context examples (terse, factual, link-driven):

• Tracked in [tt-xla#4394](https://github.com/tenstorrent/tt-xla/issues/4394), [tt-mlir#8044](https://github.com/tenstorrent/tt-mlir/issues/8044) (filed by pdeviTT, 2026-04-21, open). Owner: Vladan Kovacevic, pdeviTT.
• Known issue from [tt-xla#4318](https://github.com/tenstorrent/tt-xla/pull/4318) (new decode PCC check, merged 2026-04-24). Active investigation in #tt-forge-models-code-changes Slack thread "Qwen PCC Drop Debugging". Owner: pdeviTT.

Avoid:

• "No fix has merged yet" (unstated assumption — leave it out, the issue link tells the reader)
• "PCC=0.41 indicates the logits are nearly random" (interpretation Glean didn't state)
• "Root cause: ..." unless the linked issue explicitly identifies it

Performance Results

Group by device type. Tables show Samples/sec and the diff vs previous nightly.

TTFT is intentionally omitted for now — it will be reintroduced once we're tracking it properly. Don't add a TTFT column, don't include TTFT diffs, and don't add explanatory notes about TTFT/measurement variance.

Filter: Only include models with ≥5% change in Samples/sec vs the previous nightly. Models with smaller changes are stable and should be omitted from the tables. Always include models marked new (not present in previous nightly) and missing rows where a diff can't be computed. At the end of each device section, add: _N stable models omitted (<5% change in Samples/sec)._

### n150 Models

| Model | Samples/sec | Diff | Device FW (s) | Job |
|-------|------------|------|---------------|-----|
| model_a | 36.5 | +15.3% | 1.89 | [link](...) |
| model_b | 12.3 | -5.2% | — | [link](...) |
| model_c | 27.9 | **-23.5%** | 1.45 | [link](...) |

[Bold diffs ≤ -10% — these are the regressions surfaced in the Summary]
[Use "—" for metrics not present (Device FW only when device perf step ran successfully)]
[Use "new" in Diff column for models not in the previous nightly]

Adjust the template based on what's actually in the run — if there are no failures, write "None" under Failures.

Run Info & Dependency Chain (report footer)

Place the run metadata and dependency chain at the END of the report, after Performance Results. Use bulleted form, omit the event/Status fields, and show both nightly URLs as full URLs (no [report](...) link):

## Run Info

- **Workflow**: <workflow name>
- **Branch**: <head_branch>
- **URL**: <html_url>
- **Previous nightly**: <prev_html_url>

## Dependency Chain

| Repo | Commit | Link |
|------|--------|------|
| tt-xla | `<short_sha>` | [full SHA](https://github.com/tenstorrent/tt-xla/commit/<sha>) |
| tt-mlir | `<short_sha>` | [full SHA](https://github.com/tenstorrent/tt-mlir/commit/<sha>) |
| tt-metal | `<short_sha>` | [full SHA](https://github.com/tenstorrent/tt-metal/commit/<sha>) |

The filename already encodes the run date — no need for an "updated on" line in the body.

Scripts

Two helper scripts are bundled:

scripts/fetch_perf_reports.py

Batch-downloads all perf report artifacts for a run and extracts metrics.

python <skill-path>/scripts/fetch_perf_reports.py <RUN_ID> --output-dir /tmp/perf_reports_<RUN_ID>

Output: summary.json with per-model metrics.

scripts/compare_nightlies.py

Compares perf between two runs and categorizes models as regression/improvement/stable.

python <skill-path>/scripts/compare_nightlies.py <CURRENT_RUN_ID> <PREVIOUS_RUN_ID> \
  --output-dir /tmp/perf_compare_<RUN_ID> --threshold 5

Output: comparison.json with per-model diffs. Convention: positive % = faster (improvement), negative % = slower (regression). Use --threshold 5 so the report can apply asymmetric thresholds (regressions ≤ -10%, improvements ≥ +5%) at filter time.

Notes

• The gh CLI must be authenticated with access to tenstorrent/tt-xla (and tenstorrent/tt-mlir for commit chain resolution).
• Artifact downloads require appropriate permissions. If an artifact has expired (default 90 days), note it in the report.
• For very large runs (nightly has 50+ jobs), use the bundled scripts to batch-process artifacts rather than downloading one by one.
• Device perf measurements (device_fw_duration) are only present when the workflow ran with skip-device-perf: false AND the device perf step succeeded. Their absence is not an error.
• When Glean returns no results for a failure, note that explicitly in the report — a novel error with no prior context is itself important.
• The CI Check perf regression job is unreliable; always run compare_nightlies.py to derive regressions and improvements yourself. Summary thresholds are asymmetric: regressions surface at diff_percent <= -10, improvements at diff_percent >= +5. Detail tables show all |diff_percent| >= 5.