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@@ -69,7 +69,7 @@ approval, then upload reviewed accepted baseline records.
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| `model_id` | Yes | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. |
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| `gpu_type` | Yes | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. Baselines are GPU-specific. |
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| `source_results` | Yes | One or more local paths or Buildkite artifact URLs for accepted shifted performance JSONs. Prefer normalized `normalized_perf_*.json` artifacts emitted by `compare_baseline.py`. Accept `source_result` as an alias only for a single JSON. |
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| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `PERF_MAX_REGRESSION` if set, otherwise `0.05` (5%). |
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| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `0.05` (5%). |
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| `intent_rationale` | Yes | One-line explanation for why the baseline shift is legitimate. This is written into provenance and should be reused in the PR. |
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Hardcoded defaults:
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@@ -148,8 +148,7 @@ For each metric with at least two non-null source values:
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4. Stop if any source record regresses against the batch median by more than
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`max_intra_batch_regression`.
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Default `max_intra_batch_regression` to `PERF_MAX_REGRESSION` when set,
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otherwise `0.05`. Print a table with per-source values, batch median, and
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Default `max_intra_batch_regression` to `0.05`. Print a table with per-source values, batch median, and
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worst intra-batch regression.
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This check prevents uploading a mixed batch where one JSON is materially
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@@ -183,7 +182,7 @@ present, that run is not a valid source for baseline reseeding.
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### 2. Sync and back up existing HF records under /tmp
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Use `fastvideo/tests/performance/hf_store.py` helpers directly. Do **not** use
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Use `fastvideo/performance/hf_store.py` helpers directly. Do **not** use
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`compare_baseline.py` as a sync shortcut; on full main runs it can persist
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records, while this step must only fetch and back up existing history.
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@@ -192,7 +191,7 @@ The sync command pattern is:
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```bash
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export PERFORMANCE_TRACKING_ROOT="${PERFORMANCE_TRACKING_ROOT:-/tmp/perf-tracking}"
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export HF_REPO_ID="${HF_REPO_ID:-FastVideo/performance-tracking}"
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PYTHONPATH=fastvideo/tests/performance python -c 'from hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
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python -c 'from fastvideo.performance.hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
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```
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Then back up only the sanitized model directory under `/tmp`:
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@@ -200,8 +199,8 @@ Then back up only the sanitized model directory under `/tmp`:
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```bash
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SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
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TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
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MODEL_SAFE=$(PYTHONPATH=fastvideo/tests/performance python - <<'PY'
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from hf_store import sanitize
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MODEL_SAFE=$(python - <<'PY'
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from fastvideo.performance.hf_store import sanitize
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print(sanitize("<model_id>"))
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PY
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)
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@@ -235,7 +234,7 @@ first baseline seed. Continue, but report that baseline history was empty.
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Load the last 5 successful records for the target:
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```python
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from hf_store import load_records_for_model
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from fastvideo.performance.hf_store import load_records_for_model
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records = load_records_for_model(
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"/tmp/perf-tracking",
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@@ -372,7 +371,7 @@ prepared records plus backup on disk.
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Use the shared storage helper so the path and repo type match CI:
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```python
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from hf_store import upload_record
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from fastvideo.performance.hf_store import upload_record
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upload_record("<local_record_path>", record, strict=True)
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```
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@@ -460,7 +459,7 @@ directories created for this reseed. Never remove unrelated `/tmp` contents.
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intentional baseline replacement.
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- `fastvideo/tests/performance/compare_baseline.py` — normalization, rolling
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median comparison, and persistence rules.
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- `fastvideo/tests/performance/hf_store.py` — HF sync, record loading,
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- `fastvideo/performance/hf_store.py` — HF sync, record loading,
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`sanitize()`, and `upload_record()`.
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- `fastvideo/tests/performance/test_inference_performance.py` — source result
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JSON schema.
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