README.md

metadata

license: apache-2.0
language:
  - en
pretty_name: MLIR-Functional-Reference-30
size_categories:
  - n<1K
task_categories:
  - text-generation
tags:
  - mlir
  - code-generation
  - compiler
  - constrained-decoding
  - arith
  - linalg
  - stablehlo
  - functional-correctness
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test.jsonl

MLIR-Functional-Reference-30

Hand-authored functional-correctness reference set for arith, linalg+memref, and stablehlo (n=30, 10 per dialect).

Composition

• Instances: 30
• Format: one JSON record per line in data/test.jsonl
• Schema: fields = canonical_fn_name, canonical_signature, dialect, expected_output, expected_output_pattern, expected_stdout_regex, id, inputs, iree_inputs, memref_inputs, memref_print, nl, result_type, scalar_inputs, source_benchmark, source_id
• Verifier: dialect-specific lowering pipelines + execution comparison; see run_functional.py for the per-dialect runners
• License: Apache-2.0 (SPDX: Apache-2.0). No third-party IP restrictions.

Loading

from datasets import load_dataset
ds = load_dataset("plawanrath/MLIR-Functional-Reference-30", split="test")
print(ds[0])

Each record is a self-contained natural-language→MLIR pair; verify-valid pass-rate under the dialect's verifier is the primary evaluation metric.

Source format

The JSONL file at data/test.jsonl is the canonical HuggingFace interface. MLCommons Croissant 1.0 metadata (croissant.json) ships alongside the release.

Datasheet

Key points (full Gebru-style datasheet ships with the dataset archive):

• All reference MLIR programs are verifier-clean at the time of release.
• Hand-authored (no crowdsourcing, no LLM-authored references).
• Test-only — fine-tuning on these benchmarks contaminates future evaluation and is explicitly out of scope.

License

Apache-2.0. See LICENSE.