validate-dataset

Testing & Quality

Validate the correctness and completeness of a FlashInfer Trace dataset. Use when checking dataset quality, verifying definitions/workloads/solutions/traces, debugging data issues, or preparing a dataset for release.

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Validate Dataset

Run structural, schema, and coverage checks on a FlashInfer Trace dataset. Produces a JSON report and optional text summary.

Usage

# Full validation (including GPU benchmark)
/validate-dataset

# Skip GPU benchmark
/validate-dataset --disable-gpu

# Validate specific op_type or definition
/validate-dataset --op-types mla_paged --disable-gpu
/validate-dataset --definitions rmsnorm_h4096 --disable-gpu

Parameters

--dataset: Dataset root (default: FIB_DATASET_PATH env var)
--op-types: Filter by op_type (union with --definitions)
--definitions: Filter by definition name
--checks: Comma-separated subset of: layout,definition,workload,solution,trace,baseline,benchmark
--disable-gpu: Skip benchmark checks (default: GPU enabled)
--outputs: Comma-separated output targets: stdout,json,text (default: all three)
--output-folder: Report output directory (default: <dataset>/reports/)

Implementation

Files

File	Purpose
`flashinfer_bench/data/validate.py`	Core validation logic: `validate_dataset()`
`flashinfer_bench/data/validate_render.py`	Report models and rendering: `render_report()`
`flashinfer_bench/cli/main.py`	CLI entry: `flashinfer-bench validate`, `flashinfer-bench validate-render`

Steps

Run validation via CLI:

flashinfer-bench validate --dataset tmp/flashinfer-trace --disable-gpu

Or call Python API directly:

from flashinfer_bench.data.validate import validate_dataset
report = validate_dataset(dataset="tmp/flashinfer-trace", disable_gpu=True)

Re-render an existing report:

flashinfer-bench validate-render reports/report-20260413-120000.json

Check Categories

Category	What it checks
`layout`	Duplicate definition names, directory structure, path-field consistency, blob file existence
`definition`	Pydantic schema, reference Python syntax, axis references, descriptions, build reference (GPU)
`workload`	Schema, axes > 0, shape inference, safetensors blobs, no solution/evaluation
`solution`	Pydantic schema, entry_point format, sources, path-field consistency
`trace`	Schema, must have solution+evaluation, solution existence, workload coverage
`baseline`	Baseline solution/trace existence, build baseline solutions (GPU), PASSED status, workload coverage
`benchmark`	GPU: run baseline + reference via `Benchmark.run_all()` (lightweight config)

Report Output

JSON and text reports are timestamped (report-YYYYMMDD-HHMMSS.json/.txt) to prevent overwriting
Reports go to <dataset>/reports/ by default
See docs/flashinfer-trace/validate.mdx for full report structure and examples