---
slug: contributing/performance_benchmarks
title: Performance Benchmarks
---
<a id="performance-benchmarks"></a>

FastVideo's performance benchmark suite measures end-to-end inference latency,
throughput, peak GPU memory, and component-level pipeline timings for
representative pipeline configurations. It tracks those metrics over time
against a rolling baseline stored on the Hugging Face Hub.

It serves three audiences:

* **CI** — gates pull requests against a per-GPU static threshold and a
  rolling-median regression check.
* **Maintainers** — surfaces regressions in a Markdown summary on every
  performance build and a long-form Plotly dashboard.
* **Local developers** — lets you run the same benchmark on your own machine,
  then compare against the historical baseline for the same comparable
  identity.

## Quick start (local) {#quick-start-local}

```bash
# Run all measured benchmarks; writes raw perf_*.json under
# fastvideo/tests/performance/results/
pytest fastvideo/tests/performance/test_inference_performance.py -vs

pytest tests/local_tests/performance/ -vs

# Optional: compare against the rolling HF baseline.
# PERF_REPORTS_DIR defaults to /root/data/perf_reports in a CI container, so
# override it for a local run.
PERF_REPORTS_DIR=/tmp/fastvideo_perf_reports \
python fastvideo/tests/performance/compare_baseline.py

# Optional: explicitly upload a passing local/manual run.
HF_TOKEN=hf_... \
PERF_RUN_SOURCE=local \
PERF_UPLOAD_POLICY=pass \
PERF_REPORTS_DIR=/tmp/fastvideo_perf_reports \
python fastvideo/tests/performance/compare_baseline.py

# Optional: build the Plotly dashboard locally.
PERF_REPORTS_DIR=/tmp/fastvideo_perf_reports \
python fastvideo/tests/performance/dashboard.py
```

The pytest run never uploads anything. `compare_baseline.py` uploads only when
`PERF_UPLOAD_POLICY` is set. Local uploads are explicit opt-in and require HF
credentials. PR performance runs read baselines strictly and retain comparison
reports as Buildkite artifacts without uploading records. Scheduled-main runs
upload both pass and fail records using an operator-provisioned CI credential. The
report directory default is container-oriented; set `PERF_REPORTS_DIR` to a
writable local path when generating dashboards or when you want local
Markdown/normalized-result artifacts from the comparator.
`compare_baseline.py` reads every `perf_*.json` currently present in
`fastvideo/tests/performance/results/`; remove stale result files if you only
want to compare the latest local run.

## Local live dashboard {#local-live-dashboard}

For an app-style local dashboard backed by the same HF performance-tracking
records, see `performance_dashboard/README.md`. The dashboard provides a
FastAPI API plus a React UI and can be exposed with `ngrok` after building the
frontend.

## DGX Spark (GB10) local benchmarking {#dgx-spark-gb10-local-benchmarking}

The NVIDIA DGX Spark (GB10) is not available on Modal, so its coverage is
**local/manual** rather than automated CI. The GB10 benchmark
`wan-t2v-1.3b-1gpu-gb10` is gated to the GB10 via `run_config.gpu_types`
(matched as substrings of the CUDA device name), so the shared H100/L40S
performance lanes discover it and skip it, while a GB10 owner runs it locally.

Run just the GB10 benchmark on a DGX Spark:

```bash
pytest 'fastvideo/tests/performance/test_inference_performance.py::test_inference_performance[wan-t2v-1.3b-1gpu-gb10]' -vs
```

To check run-to-run stability (latency, peak memory, throughput), run it a few
times from a clean results directory, then normalize:

```bash
rm -f fastvideo/tests/performance/results/perf_*.json
for i in 1 2 3 4 5; do
  pytest 'fastvideo/tests/performance/test_inference_performance.py::test_inference_performance[wan-t2v-1.3b-1gpu-gb10]' -vs
done
PERF_REPORTS_DIR=/tmp/fastvideo_perf_reports \
  python fastvideo/tests/performance/compare_baseline.py
```

`compare_baseline.py` reports `CALIBRATION_NEEDED` until a baseline exists for
the GB10 identity, and writes one `normalized_perf_*.json` per run. Reference
figures on a GB10 (torch 2.12.0+cu130, transformers 5.14.0): generation ~39.3 s,
peak ~8.4 GB, throughput ~1.15 fps, stable to ~0.4% across five runs.

### Seeding the GB10 baseline (follow-up) {#seeding-the-gb10-baseline-follow-up}

Seeding a baseline-eligible record for the GB10 identity is intentionally **not**
done from a local run: `seed_baseline.py` accepts only `scheduled_main`
full-suite source artifacts, so ordinary local/manual uploads stay
`baseline_eligible=false` (dashboard-visible, but they do not move the rolling
baseline). Establishing the GB10 baseline requires either:

* a scheduled-main performance run on a GB10 CI runner once one is available, or
* a carefully scoped, maintainer-approved manual calibration path that preserves
  the existing exact-identity, batch-consistency, provenance, and
  explicit-approval safeguards — it must **not** make arbitrary local uploads
  baseline-eligible.

The reviewed five-run GB10 artifacts are the stability evidence for that first
baseline. Tracked in #1632.

## Architecture {#architecture}

```
.buildkite/performance-benchmarks/tests/*.json
    └── per-benchmark configs: model, gen kwargs, per-GPU thresholds

fastvideo/tests/performance/
    ├── test_inference_performance.py
    │       └── pytest test that runs each config, writes perf_*.json
    │           with latency, memory, throughput, and component timings
    ├── compare_baseline.py
    │       └── normalizes raw results, compares against HF rolling baseline,
    │           writes Markdown summary + (optionally) uploads new records
    ├── dashboard.py
    │       └── builds time-series Plotly HTML from HF history

tests/local_tests/performance/
    └── local-only behavior tests for config, policy, identity, dashboards,
        and worker-log capture

fastvideo/performance/
    ├── hf_store.py               # shared HF I/O + DataFrame helpers
    └── metric_policy.py          # shared rolling-baseline threshold policy
```

The HF dataset (`FastVideo/performance-tracking` by default) holds one
normalized JSON per run. For v2 records, the rolling baseline is the median of
the last 5 successful, baseline-eligible records in the same comparison cohort:
`workload_id`, `variant_id`, `benchmark_version`, `recipe_fingerprint`,
`hardware_profile_id`, and `software_profile_id`. PR and local records are
visible in the dashboard but are not baseline eligible.

## Planned Coverage {#planned-coverage}

The current rollout tracks a small set of representative inference workloads.
Broader coverage is planned for additional models, GPU types, attention
backends, workload shapes, and inference recipes. As that coverage lands, the
performance tracking system will also add environment-specific considerations
so comparisons remain meaningful across hardware, runtime, attention backend,
and recipe changes instead of treating all records for a model as equivalent.

## Metrics {#metrics}

Each benchmark records six metrics. The rolling-baseline comparator also has a
per-metric policy with direction, percent threshold, absolute threshold, and a
`gated` flag.

| Metric | Raw key | Normalized key | Direction | Default rolling policy |
|---|---|---|---|---|
| End-to-end generation latency | `avg_generation_time_s` | `latency` | Lower is better | 8% and 0.5 s |
| Video throughput | `throughput_fps` | `throughput` | Higher is better | 8% and 0.05 FPS |
| Peak GPU memory | `max_peak_memory_mb` | `memory` | Lower is better | 5% and 256 MB |
| Text encoder time | `text_encoder_time_s` | `text_encoder_time_s` | Lower is better | 5% and 0.25 s |
| DiT denoising time | `dit_time_s` | `dit_time_s` | Lower is better | 5% and 0.25 s |
| VAE decode time | `vae_decode_time_s` | `vae_decode_time_s` | Lower is better | 5% and 0.25 s |

`test_inference_performance.py` temporarily sets `FASTVIDEO_STAGE_LOGGING=1`
while it runs so pipeline stage execution times are available in
`generate_video(...).logging_info`. Stage logs use pipeline-unique keys such as
`prompt_encoding_stage` so duplicate stage classes do not collide. For
`PipelineStage` entries, shared component stage bases emit a stable
`component_metric`: text encoding stages map to `text_encoder_time_s`,
denoising stages and subclasses map to `dit_time_s`, and decoding stages map to
`vae_decode_time_s`. The extractor falls back to known `stage_class` names for
older logs that do not include `component_metric` or that used the class name as
the stage key. Generator-side timings such as `PostDecodeFrameProcessStage`,
`VideoSaveStage`, and `AudioMuxStage` are intentionally ignored. If a pipeline
does not report one of the mapped stages, that component metric is stored as
`null` and is skipped by the static threshold and rolling baseline checks.

## The two gates {#the-two-gates}

There are **two independent regression gates** — they protect against
different failure modes and are not redundant.

A config can additionally restrict which hardware it runs on via
`run_config.gpu_types` (substring match against the CUDA device name); a
non-matching device skips the benchmark entirely. That is a run gate, not a
regression gate.

### Static thresholds (per-GPU) {#static-thresholds-per-gpu}

Defined in `.buildkite/performance-benchmarks/tests/<benchmark>.json` under
`thresholds`. Example:

```json
"thresholds": {
  "L40S": {
    "max_generation_time_s": 34.0,
    "max_peak_memory_mb": 11000.0,
    "max_text_encoder_time_s": 5.0,
    "max_dit_time_s": 10.0,
    "max_vae_decode_time_s": 10.0
  },
  "default": { "max_generation_time_s": 120.0, "max_peak_memory_mb": 30000.0 }
}
```

Selection: `_get_thresholds(cfg)` matches the current GPU name (substring
match) against keys; falls back to `default` if no GPU matches.

`max_generation_time_s` and `max_peak_memory_mb` are required for every
selected threshold block. Component limits are optional: if
`max_text_encoder_time_s`, `max_dit_time_s`, or `max_vae_decode_time_s` is
absent, the pytest static-threshold gate skips that component.

These are **fail-safes** — they catch order-of-magnitude regressions,
unrealistic memory growth, and optionally large component-specific slowdowns
even when the rolling baseline is empty. They are hand-set with generous
headroom and almost never need touching.

### Rolling baseline (per comparison cohort) {#rolling-baseline-per-comparison-cohort}

`compare_baseline.py` loads the last 5 successful, baseline-eligible records
for the same comparison cohort from the HF dataset, computes the median for
each available metric, and evaluates the current run with the metric's rolling
regression policy. For v2 records, that cohort is `workload_id`, `variant_id`,
`benchmark_version`, `recipe_fingerprint`, `hardware_profile_id`, and
`software_profile_id`. For latency, memory, and component times, higher values
are regressions. For throughput, lower values are regressions.

A metric exceeds its rolling threshold when both of these are true:

```text
percent_delta > threshold_percent
absolute_delta > threshold_absolute
```

Gated metrics fail CI when that threshold crossing happens. Set `gated: false`
for metrics that should remain visible in reports and the dashboard without
failing CI. Dashboard/API payloads expose `threshold_exceeded` separately from
`regressed`, where `regressed` means a gated CI failure. Missing or `null`
metrics are skipped.

This is the **drift detector** — it catches sub-threshold regressions that
slowly add up. Only scheduled-main successful records are baseline eligible.
Local uploads are explicit opt-in and do not update future gating baselines.
PR runs retain their normalized records and reports as Buildkite artifacts.

Comparator summaries and normalized artifacts include an explicit
`comparison_status`:

| Status | Meaning | CI behavior |
|---|---|---|
| `PASS` | Comparable baseline exists and no gated metric regressed. Legacy records with no baseline also keep the historical initialization behavior. | Passes |
| `REGRESSION` | The record exceeds one of its static thresholds or at least one gated metric regressed against a comparable baseline. | Fails |
| `CALIBRATION_NEEDED` | A v2 record has no exact comparable baseline. | Passes, may upload when `PERF_UPLOAD_POLICY=pass`, never seeds a baseline |
| `RECIPE_MISMATCH` | The same workload, variant, and benchmark version has trusted successful records under another recipe fingerprint, including records from other hardware or software profiles. | Fails |
| `INFRA_ERROR` | The comparator cannot safely classify the record, such as a v2 record missing required identity fields. | Fails |

`QUALITY_BLOCKED` is reserved for a future variant-promotion workflow and is
not emitted by normal rolling-baseline comparison.

For `RECIPE_MISMATCH`, trusted records are scheduled-main records or records
already marked `baseline_eligible`. PR and local calibration uploads remain
visible but do not authorize future CI-gating recipe mismatch failures.

When the baseline shifts for a legitimate reason (torch upgrade, kernel
change, etc.) and CI starts failing, use the
[`reseed-performance-baseline`](https://github.com/hao-ai-lab/FastVideo/blob/main/.agents/skills/reseed-performance-baseline/SKILL.md)
agent skill to advance the rolling median. To approve the first baseline for
a new v2 exact identity, follow that skill with a reviewed scheduled-main
full-suite `CALIBRATION_NEEDED` normalized artifact. Its prepare step uses
`fastvideo/tests/performance/seed_baseline.py`; after a separate human gate,
the skill rechecks the current HF revision and conditionally uploads the whole
seed batch in one commit.

## Schemas {#schemas}

### Benchmark config (`.buildkite/performance-benchmarks/tests/*.json`) {#benchmark-config-buildkiteperformance-benchmarkstestsjson}

Benchmark configs without `config_schema_version` are treated as legacy v1
configs and remain loadable. New or migrated configs should use
`config_schema_version: 2` and include explicit comparable identity fields:

```jsonc
{
  "benchmark_id": "wan-t2v-1.3b-2gpu",
  "config_schema_version": 2,
  "workload_id": "wan-t2v",
  "variant_id": "1.3b-sp2",
  "benchmark_version": 3
}
```

`benchmark_id` is still required because raw artifact names, generated-video
directories, normalized record paths, and legacy storage directories depend on
it. The v2 comparator does not use it as part of the comparison cohort. The v2
identity fields make the measured workload explicit:

| Field | Purpose |
|---|---|
| `workload_id` | Stable benchmark family, such as `wan-t2v`. |
| `variant_id` | Intentional recipe family, including model size and parallelism config, such as `1.3b-sp2`. |
| `benchmark_version` | Version of the measurement protocol and comparison policy. |

If a config declares `config_schema_version: 2`, loading fails clearly when any
required v2 identity field is missing. If v2 identity or metadata fields are
added without `config_schema_version: 2`, loading also fails so partial
migrations do not silently run as v1 configs. Optional v2 `quality_metadata`
and the v1/v2 `regression_thresholds` policy must be JSON objects when present.
(`recipe` is emitted by the harness and is not config-declarable.)

`run_config.gpu_types` is an optional list of non-empty strings. Each entry is
substring-matched against the CUDA device name; if no entry matches, the
benchmark is skipped on that device, so a hardware-specific config does not run
on shared lanes. Discovery fails when an entry has no matching `thresholds` key
or when `thresholds` has no `default` block, which prevents a gated config from
silently falling back to `default`.

V2 records compare only within their exact identity cohort. A record that opens
a new cohort is marked `baseline_status: "initialized_new_cohort"` and
`comparison_status: "CALIBRATION_NEEDED"`; it remains ineligible until a
reviewed scheduled-main artifact is seeded explicitly. Legacy v1 configs still
run and are normalized for reporting, but their records skip rolling-baseline
comparison entirely (`baseline_status: "skipped_missing_identity"`, never
baseline eligible); only static thresholds gate them. Metric-specific threshold
policies are active. `QUALITY_BLOCKED` remains reserved for future variant
promotion policy.

The shipped Wan benchmark uses `benchmark_version: 3` because recipe schema 2
changed the recipe fingerprint by removing the legacy `benchmark_id` display
name. This intentionally opens a new comparison cohort: after deployment, a
reviewed scheduled-main full-suite `CALIBRATION_NEEDED` artifact must be seeded
once before rolling regression gating resumes for that exact identity. Static
thresholds remain active during calibration.

### Raw record (`results/perf_*.json`) {#raw-record-resultsperf_json}

Written by `test_inference_performance.py`. One file per benchmark run.

```jsonc
{
  "benchmark_id": "wan-t2v-1.3b-2gpu",
  "result_schema_version": 2,
  "workload_id": "wan-t2v",
  "variant_id": "1.3b-sp2",
  "benchmark_version": 3,
  "model_short_name": "Wan2.1-T2V-1.3B-Diffusers",
  "device": "NVIDIA L40S",
  "num_gpus": 2,
  "num_warmup_runs": 1,
  "num_measurement_runs": 3,
  "avg_generation_time_s": 28.4,
  "individual_times_s": [28.5, 28.3, 28.4],
  "throughput_fps": 1.58,
  "max_peak_memory_mb": 10840.0,
  "individual_peak_memories_mb": [10840.0, 10822.0, 10833.0],
  "thresholds": {
    "max_generation_time_s": 34.0,
    "max_peak_memory_mb": 11000.0,
    "max_text_encoder_time_s": 5.0,
    "max_dit_time_s": 10.0,
    "max_vae_decode_time_s": 10.0
  },
  "worker_log_path": "<container-local path to the captured worker log, or null>",
  "regression_thresholds": {
    "latency": {
      "threshold_percent": 0.10,
      "threshold_absolute": 1.0,
      "gated": true
    }
  },
  "commit": "<full sha>",
  "run_source": "pr",
  "branch": "feature/perf-change",
  "pr_number": "1234",
  "test_scope": "direct",
  "build_url": "https://buildkite.example/build",
  "build_id": "<buildkite-build-id>",
  "job_id": "<buildkite-job-id>",
  "timestamp": "2026-05-08T22:00:00+00:00",
  "quality_metadata": { "quality_status": "canonical" },
  "text_encoder_time_s": 2.141,
  "dit_time_s": 8.437,
  "vae_decode_time_s": 3.208,
  "recipe": {
    "recipe_schema_version": 2,
    "benchmark": {
      "benchmark_id": "wan-t2v-1.3b-2gpu",
      "workload_id": "wan-t2v",
      "variant_id": "1.3b-sp2",
      "benchmark_version": 3
    },
    "model": { "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" },
    "init_kwargs": { "num_gpus": 2, "sp_size": 2, "tp_size": 1 },
    "generation_kwargs": { "height": 480, "width": 832, "num_frames": 45 },
    "inputs": { "prompt_count": 1, "prompt_sha256": ["<measured-prompt-sha256>"] },
    "attention": { "requested_backend": "FLASH_ATTN", "resolved_backend": "FLASH_ATTN" }
  },
  "recipe_fingerprint": "<sha256>",
  "hardware_profile": {
    "device_type": "cuda",
    "gpu_count": 2,
    "gpus": [{ "name": "NVIDIA L40S", "memory_gb": 48, "compute_capability": "8.9" }],
    "interconnect": "none_or_partial"
  },
  "hardware_profile_id": "hw-<sha256-prefix>",
  "software_profile": {
    "python": "3.12",
    "pytorch": "2.12",
    "cuda": "13.0",
    "attention_backend": "FLASH_ATTN",
    "flash_attention_4_enabled": true,
    "container_image_version": "py3.12-cuda13.0.0",
    "packages": {
      "fastvideo_kernel": "0.3.2",
      "flashinfer": "0.2.11",
      "nvidia_cutlass_dsl": "4.5.0",
      "triton": "3.4.1"
    }
  },
  "software_profile_id": "sw-<sha256-prefix>",
  "environment_metadata": {
    "env": {
      "IMAGE_VERSION": "py3.12-cuda13.0.0",
      "FASTVIDEO_CONTAINER_IMAGE_REF": "ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-cuda13.0.0@sha256:<digest>"
    }
  },
  "environment_fingerprint": "env-<sha256-prefix>"
}
```

### Normalized record (HF dataset, also dumped as `normalized_perf_*.json`) {#normalized-record-hf-dataset-also-dumped-as-normalized_perf_json}

Written by `compare_baseline.py:_normalize_record`. One file per benchmark
result, used as the rolling-baseline source of truth.

```jsonc
{
  "model_id": "wan-t2v-1.3b-2gpu",
  "result_schema_version": 2,
  "workload_id": "wan-t2v",
  "variant_id": "1.3b-sp2",
  "benchmark_version": 3,
  "timestamp": "2026-05-08T22:00:00+00:00",
  "commit_sha": "<full sha>",
  "gpu_type": "NVIDIA L40S",
  "latency": 28.4,
  "throughput": 1.58,
  "memory": 10840.0,
  "text_encoder_time_s": 2.141,
  "dit_time_s": 8.437,
  "vae_decode_time_s": 3.208,
  "regression_thresholds": {
    "latency": {
      "threshold_percent": 0.08,
      "threshold_absolute": 0.5,
      "gated": true
    }
  },
  "recipe_fingerprint": "<sha256>",
  "hardware_profile_id": "hw-<sha256-prefix>",
  "software_profile_id": "sw-<sha256-prefix>",
  "environment_fingerprint": "env-<sha256-prefix>",
  "run_source": "pr",
  "branch": "feature/perf-change",
  "pr_number": "1234",
  "test_scope": "direct",
  "build_url": "https://buildkite.example/build",
  "build_id": "<buildkite-build-id>",
  "job_id": "<buildkite-job-id>",
  "quality_metadata": { "quality_status": "canonical" },
  "baseline_status": "compared",
  "comparison_status": "PASS",
  "comparison_status_reason": "Comparable baseline found with no gated regressions",
  "baseline_eligible": false,
  "success": true
}
```

### Compatibility with legacy records {#compatibility-with-legacy-records}

Older records in the HF dataset may not have `result_schema_version`,
component timing fields, or v2 identity/profile fields. Records without
`result_schema_version` are treated as v1. The comparator ignores missing or
`null` metrics when computing a median, and the dashboard lists skipped plots
for metric series that have no non-null values. Records missing both
`run_source` and `baseline_eligible` are treated as legacy successful
main/full-suite uploads and remain eligible for rolling baselines.
Current `perf_*.json` artifacts that lack the v2 comparison identity are
normalized for reporting but skip rolling-baseline comparison and are not marked
baseline eligible.
Dashboard reports keep historical v1 records readable by grouping them under
the explicit `Legacy v1` label by `(model_id, gpu_type)`. Complete v2 cohorts
remain separate and use the six-field comparison identity. Records that claim
or partially use v2 identity without all required fields are labeled
`Invalid v2` and remain scoped by their display metadata and available identity
fields. Stored comparison status, comparison reason, and baseline status are
shown when present; old records remain readable when those fields are absent.

New v2 records compare only against the same `workload_id`, `variant_id`,
`benchmark_version`, `recipe_fingerprint`, `hardware_profile_id`, and
`software_profile_id` cohort, independent of the legacy `model_id` directory
and `gpu_type` display string. Historical v1 records remain readable for
reporting, but current legacy artifacts do not perform a `(model_id, gpu_type)`
rolling comparison or seed new rolling baselines.
`environment_metadata` and `environment_fingerprint` are audit data and are not
part of the comparison key.
The recipe prompt digests describe the prompts actually measured by the
benchmark run; extra configured prompts are ignored unless the benchmark runner
executes them.
Software profile package cohorts keep exact versions for relevant
attention/kernel packages, including FastVideo kernels, FlashAttention,
FlashInfer, Cutlass DSL, SageAttention, Triton, and xFormers when installed.

## Environment variable reference {#environment-variable-reference}

| Variable | Default | Used by | Purpose |
|---|---|---|---|
| `PERFORMANCE_TRACKING_ROOT` | `/tmp/perf-tracking` | `compare_baseline.py`, `dashboard.py` | Local directory the HF dataset is synced to. |
| `PERF_REPORTS_DIR` | `/root/data/perf_reports` | `compare_baseline.py`, `dashboard.py` | Where the Markdown summary and Plotly HTML get written for Buildkite to pick up. |
| `HF_REPO_ID` | `FastVideo/performance-tracking` | `fastvideo/performance/hf_store.py` | HF dataset repo holding rolling-baseline records. |
| `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, `HF_TOKEN` | unset | `fastvideo/performance/hf_store.py` | Required for upload or private dataset reads. |
| `PERF_RUN_SOURCE` | inferred | `compare_baseline.py`, `test_inference_performance.py` | Source metadata for uploaded records: `pr`, `local`, `scheduled_main`, or `unknown`. |
| `PERF_UPLOAD_POLICY` | `never` | `compare_baseline.py` | Upload policy: `never`, `pass`, or `always`. |
| `PERF_PYTEST_RC` | unset | `compare_baseline.py` | Performance pytest exit code. Measured static-threshold failures are attributed per record; otherwise a nonzero code reports an infrastructure error. |
| `TEST_SCOPE` | unset | `compare_baseline.py` | CI context used to infer scheduled-main runs together with `BUILDKITE_BRANCH=main`. |
| `BUILDKITE_BRANCH`, `BUILDKITE_COMMIT`, `BUILDKITE_PULL_REQUEST` | unset | `compare_baseline.py`, `test_inference_performance.py` | CI metadata stamped into records. |
| `DASHBOARD_DAYS` | `30` | `dashboard.py` | Lookback window for the Plotly trend pages. |
| `PERFORMANCE_TRACKING_SYNC_REUSE_TTL_SECONDS` | `3600` | `fastvideo/performance/hf_store.py` | Freshness window for reusing an existing HF sync when requested by dashboard consumers. |
| `FASTVIDEO_ATTENTION_BACKEND` | `auto` | `test_inference_performance.py` | Requested attention backend included in `software_profile_id`. |
| `FASTVIDEO_FA4` | `0` | `test_inference_performance.py` | FlashAttention-4 toggle included in `software_profile_id`. |
| `FASTVIDEO_PERFORMANCE_PROFILE_VERSION` | unset | `test_inference_performance.py` | Optional explicit software cohort/profile version included in `software_profile_id`. |
| `IMAGE_VERSION` | unset | `test_inference_performance.py` | CI container image/profile version included in `software_profile_id` when available. |
| `FASTVIDEO_CONTAINER_IMAGE_REF` | unset | Slurm runner, `test_inference_performance.py` | Pinned CI container image digest recorded in `environment_metadata` and `software_profile_id`. |
| `FASTVIDEO_STAGE_LOGGING` | set by the pytest test | `test_inference_performance.py` | Enables pipeline stage timing capture for component metrics during benchmark runs. |

## CI integration {#ci-integration}

The performance step can run on demand with `/test performance`, through a
merge gate when performance tests or benchmark policy changed, and as part of
an explicit `/test full` run (see [CI/CD Architecture](/FastVideo/contributing/ci_architecture)).
The weekly `fastvideo-performance-lane` schedule runs the same Slurm payload.
The active entry point is `.buildkite/scripts/lanes/performance.sh`; the
trusted host dispatcher relays its allowlisted reports to Buildkite after the
isolated container exits.

Each performance build runs pytest first. PR and direct runs only continue to
`compare_baseline.py` when that fixed-threshold phase passes; if pytest fails,
Markdown summaries and normalized JSON artifacts are not emitted. Scheduled
main runs set `PERF_UPLOAD_POLICY=always`, so they still run
`compare_baseline.py` (with `PERF_PYTEST_RC` set) after pytest fails. Each raw
record is checked against its own static thresholds: a measured breach reports
`REGRESSION`, while unaffected records retain their rolling-baseline status. A
nonzero pytest exit with no attributable static-threshold breach reports
`INFRA_ERROR`. Failed records have `success=false` and are excluded from future
rolling baselines. The dashboard still runs best-effort for observability.

Each benchmark run also captures worker-process logs into
`results/worker_logs/worker_<benchmark_id>_<ts>.log` (raw records point to it
via `worker_log_path`; the field is null/absent in older and HF-synced
records). On a hard regression, a 200-line tail of that log is printed in the
failure output — by the pytest assertion on PR runs and by
`compare_baseline.py` on scheduled runs. The performance lane
(`.buildkite/scripts/lanes/performance.sh`) also mirrors each captured log into
`$PERF_REPORTS_DIR` as `worker_<benchmark_id>_<ts>.md` next to the raw JSON
results, so the log is retrievable as a build artifact even when the
comparison phase never runs. The mirror uses an allowlisted extension because
the trusted host relays only `.md`, `.html`, `.json`, and `.csv` files from
that directory; the `.log` files themselves are never uploaded. Coverage
caveat: only the `fastvideo` logger is captured (no torch/NCCL or raw stderr
output), and ranks > 0 suppress `logger.info` by default, so the file contains
rank-0 INFO plus WARNING/ERROR from all ranks.
When the rolling-baseline phase runs, it emits:

* **Markdown summary** — appended to `$GITHUB_STEP_SUMMARY` when that variable
  is set, and written as `perf_<sha>_<ts>.md` for Buildkite upload. Contains a
  per-benchmark row with current vs. baseline values for latency, throughput,
  memory, text encoder time, DiT time, and VAE decode time.
* **Plotly dashboard** — `dashboard_<sha>_<ts>.html` showing time-series for
  each metric grouped by comparison cohort. Chart titles distinguish
  `Legacy v1` and incomplete `Invalid v2` records, and point hover data includes
  stored comparison and baseline status metadata.
* **Normalized records** — `normalized_perf_*.json`, one per benchmark.
  Useful as input to the
  [`reseed-performance-baseline`](https://github.com/hao-ai-lab/FastVideo/blob/main/.agents/skills/reseed-performance-baseline/SKILL.md)
  skill.

## Adding a new benchmark {#adding-a-new-benchmark}

1. Drop a new JSON config into
   `.buildkite/performance-benchmarks/tests/<name>.json`. New configs should
   use v2 identity fields:

   ```json
   {
     "benchmark_id": "<unique-id>",
     "config_schema_version": 2,
     "workload_id": "<stable-workload-id>",
     "variant_id": "<variant, e.g. 1.3b-sp2>",
     "benchmark_version": 1,
     "model": { "model_path": "...", "model_short_name": "..." },
     "init_kwargs": { "num_gpus": 1, ... },
     "generation_kwargs": { "num_frames": 45, ... },
     "test_prompts": ["..."],
     "run_config": { "required_gpus": 1,
                     "num_warmup_runs": 1, "num_measurement_runs": 3,
                     "gpu_types": ["<device-name-substring>"] },
     "thresholds": {
       "L40S": {
         "max_generation_time_s": 34.0,
         "max_peak_memory_mb": 11000.0,
         "max_text_encoder_time_s": 5.0,
         "max_dit_time_s": 10.0,
         "max_vae_decode_time_s": 10.0
       },
       "default": { "max_generation_time_s": 120.0, "max_peak_memory_mb": 30000.0 }
     },
     "regression_thresholds": {
       "latency": { "threshold_percent": 0.10, "threshold_absolute": 1.0, "gated": true }
     }
   }

   ```

   Legacy v1 configs without `config_schema_version` still load, but should not
   gain v2 identity or metadata fields until they are migrated to
   `config_schema_version: 2`. For v2 configs, `workload_id`, `variant_id`,
   and `benchmark_version` are part of the comparison key; benchmark runs
   fail if any of these identity fields are missing.

2. The pytest test auto-discovers all configs — no test code needed. CI
   picks it up on the next `/test performance` run.

3. Legacy benchmarks are gated only by their static thresholds. Their current
   records skip rolling-baseline comparison and are never baseline eligible.
   V2 benchmarks with no exact comparable baseline report
   `CALIBRATION_NEEDED`; the record remains visible but does not become
   baseline eligible until a comparable scheduled-main full-suite run is
   reviewed and seeded through the prepare, review, and conditional-upload
   steps in the `reseed-performance-baseline` skill.

4. If the benchmark targets a GPU not currently in `thresholds`, either add
   that GPU as a key or rely on the `default` block. Note that `default` is
   intended for slower fallback GPUs, so its values should be relaxed
   relative to the fastest entry.

5. Add component thresholds only when the stage timing is stable enough to be
   a useful fixed gate. The rolling baseline will still track component times
   when static component thresholds are omitted.

6. Omit `regression_thresholds` to use the default rolling-baseline policy, or
   include only benchmark-specific deviations. Tune these independently from
   the fixed thresholds when a metric is noisy or should be informational. The
   fixed `thresholds` block is an absolute pytest ceiling. The
   `regression_thresholds` block controls rolling-baseline comparisons against
   recent scheduled-main records.

## Troubleshooting {#troubleshooting}

**`CALIBRATION_NEEDED` / "No baseline found for exact comparable identity"** —
the v2 run passes, but its normalized record remains
`baseline_eligible=false`. Review a successful scheduled-main full-suite
normalized artifact, then follow the `reseed-performance-baseline` skill. The
utility only prepares a digest-protected manifest; the separately confirmed
upload rechecks remote state and commits the batch atomically.

**Persistent failure right after a torch / kernel / image upgrade** —
genuine regression *or* baseline drift. Compare the failing normalized record
with recent successful records in the HF dataset. If the shift is expected and
reviewed, use the `reseed-performance-baseline` skill.

**Dashboard reports skipped metric plots** — the loaded records do not have
non-null values for that metric. This is expected for older records or for
pipelines that did not report a mapped component stage.

**Component timing is `null`** — the generated result did not include a mapped
stage in `logging_info.stages`. Check that the pipeline emits stage logging
and that the stage emits `component_metric` or is covered by the legacy
`STAGE_METRIC_MAP` fallback in `test_inference_performance.py`.