id string | dialect string | source_benchmark string | source_id string | nl string | canonical_fn_name string | canonical_signature string | inputs list | result_type string | expected_output float64 | expected_stdout_regex string | memref_inputs list | memref_print list | scalar_inputs list | iree_inputs list | expected_output_pattern string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
F01_arith_add | arith+func | mlir_spec_150 | 001_add-two-ints | Write a function `add` that takes two i32 values and returns their sum. | add | (i32, i32) -> i32 | [
{
"type": "i32",
"value": 7
},
{
"type": "i32",
"value": 3
}
] | i32 | 10 | ^10\b | null | null | null | null | null |
F02_arith_mul_i64 | arith+func | mlir_spec_150 | 003_multiply-ints | Write a function `mul` that multiplies two i64 values and returns their product. | mul | (i64, i64) -> i64 | [
{
"type": "i64",
"value": 6
},
{
"type": "i64",
"value": 7
}
] | i64 | 42 | ^42\b | null | null | null | null | null |
F03_arith_addf_f32 | arith+func | mlir_spec_150 | 004_add-floats | Write a function `addf` that takes two f32 values and returns their sum. | addf | (f32, f32) -> f32 | [
{
"type": "f32",
"value": 1.5
},
{
"type": "f32",
"value": 2.25
}
] | f32 | 3.75 | ^3\.75\b | null | null | null | null | null |
F04_arith_const42 | arith+func | mlir_spec_150 | 005_integer-constant | Write a function that returns the constant integer 42 as an i32. | const42 | () -> i32 | [] | i32 | 42 | ^42\b | null | null | null | null | null |
F05_arith_chain_addmul | arith+func | mlir_spec_150 | 008_chain-add-then-multiply | Write a function that computes `(a + b) * c` on i32 inputs and returns the i32 result. | fma_like | (i32, i32, i32) -> i32 | [
{
"type": "i32",
"value": 2
},
{
"type": "i32",
"value": 3
},
{
"type": "i32",
"value": 4
}
] | i32 | 20 | ^20\b | null | null | null | null | null |
F06_arith_band | arith+func | mlir_spec_150 | 016_bitwise-and | Write a function that computes the bitwise AND of two i32 values. | band | (i32, i32) -> i32 | [
{
"type": "i32",
"value": 12
},
{
"type": "i32",
"value": 10
}
] | i32 | 8 | ^8\b | null | null | null | null | null |
F07_arith_inc | arith+func | mlir_spec_150 | 033_increment-by-one | Write a function that takes an i32 and returns it incremented by 1. | inc | (i32) -> i32 | [
{
"type": "i32",
"value": 41
}
] | i32 | 42 | ^42\b | null | null | null | null | null |
F08_arith_double_f32 | arith+func | mlir_spec_150 | 034_double-value | Write a function that doubles an f32 value. | dbl | (f32) -> f32 | [
{
"type": "f32",
"value": 3.5
}
] | f32 | 7 | ^7(\.0+)?\b | null | null | null | null | null |
F09_arith_square_i32 | arith+func | mlir_spec_150 | 040_square | Write a function that computes x squared for an i32 x. | square | (i32) -> i32 | [
{
"type": "i32",
"value": 7
}
] | i32 | 49 | ^49\b | null | null | null | null | null |
F10_arith_cube_f32 | arith+func | mlir_spec_150 | 041_cube | Write a function that computes x * x * x for an f32 x. | cube | (f32) -> f32 | [
{
"type": "f32",
"value": 2
}
] | f32 | 8 | ^8(\.0+)?\b | null | null | null | null | null |
F11_linalg_fill_zero_1d | linalg+memref | linalg_spec_30 | 06_fill-zero-1d | Write a function that fills a 1-D f32 memref with zeros. | f0 | (memref<?xf32>) -> () | null | null | null | data\s*=\s*\[\s*0(\.0+)?\s*,\s*0(\.0+)?\s*,\s*0(\.0+)?\s*,\s*0(\.0+)?\s*\] | [
{
"name": "m",
"shape": [
4
],
"dtype": "f32",
"init": "alloc_only"
}
] | [
{
"name": "m",
"shape": [
4
],
"dtype": "f32"
}
] | null | null | null |
F12_linalg_fill_value_2d | linalg+memref | linalg_spec_30 | 07_fill-value-param | Write a function that fills a 2-D f32 memref with a given f32 value passed as a parameter. | fp | (f32, memref<?x?xf32>) -> () | null | null | null | (5(\.0+)?[^\d]+){4} | [
{
"name": "m",
"shape": [
2,
2
],
"dtype": "f32",
"init": "alloc_only"
}
] | [
{
"name": "m",
"shape": [
2,
2
],
"dtype": "f32"
}
] | [
{
"type": "f32",
"value": 5
}
] | null | null |
F13_linalg_fill_i32_const | linalg+memref | linalg_spec_30 | 08_fill-i32 | Write a function that fills a 1-D i32 memref with the integer constant 7. | f7 | (memref<?xi32>) -> () | null | null | null | (7[^\d]+){4} | [
{
"name": "m",
"shape": [
4
],
"dtype": "i32",
"init": "alloc_only"
}
] | [
{
"name": "m",
"shape": [
4
],
"dtype": "i32"
}
] | null | null | null |
F14_linalg_copy_1d | linalg+memref | linalg_spec_30 | 09_copy-1d | Write a function that copies a 1-D f32 memref into another 1-D f32 memref of the same shape. | c1 | (memref<?xf32>, memref<?xf32>) -> () | null | null | null | (3\.5[^\d]+){4} | [
{
"name": "s",
"shape": [
4
],
"dtype": "f32",
"init": "fill",
"fill_value": 3.5
},
{
"name": "d",
"shape": [
4
],
"dtype": "f32",
"init": "alloc_only"
}
] | [
{
"name": "d",
"shape": [
4
],
"dtype": "f32"
}
] | null | null | null |
F15_linalg_copy_2d_static | linalg+memref | linalg_spec_30 | 10_copy-2d-static | Write a function that copies a 4x4xf32 static memref into another 4x4xf32 memref. | c2 | (memref<4x4xf32>, memref<4x4xf32>) -> () | null | null | null | (1\.25[^\d]+){4} | [
{
"name": "s",
"shape": [
4,
4
],
"dtype": "f32",
"init": "fill",
"fill_value": 1.25,
"static": true
},
{
"name": "d",
"shape": [
4,
4
],
"dtype": "f32",
"init": "alloc_only",
"static": true
}
] | [
{
"name": "d",
"shape": [
4,
4
],
"dtype": "f32"
}
] | null | null | null |
F16_linalg_add_elemwise | linalg+memref | linalg_spec_30 | 20_add-elemwise | linalg.add elementwise on two 1-D f32 memrefs | ae | (memref<?xf32>, memref<?xf32>, memref<?xf32>) -> () | null | null | null | (5(\.0+)?[^\d]+){4} | [
{
"name": "a",
"shape": [
4
],
"dtype": "f32",
"init": "fill",
"fill_value": 2
},
{
"name": "b",
"shape": [
4
],
"dtype": "f32",
"init": "fill",
"fill_value": 3
},
{
"name": "c",
"shape": [
4
],
"dtype": "f32",
"init": "al... | [
{
"name": "c",
"shape": [
4
],
"dtype": "f32"
}
] | null | null | null |
F17_linalg_mul_elemwise_2d | linalg+memref | linalg_spec_30 | 22_mul-elemwise-2d | linalg.mul elementwise on two 2-D f32 memrefs (2x2) | me | (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>) -> () | null | null | null | (12(\.0+)?[^\d]+){4} | [
{
"name": "a",
"shape": [
2,
2
],
"dtype": "f32",
"init": "fill",
"fill_value": 3
},
{
"name": "b",
"shape": [
2,
2
],
"dtype": "f32",
"init": "fill",
"fill_value": 4
},
{
"name": "c",
"shape": [
2,
2
],
"dty... | [
{
"name": "c",
"shape": [
2,
2
],
"dtype": "f32"
}
] | null | null | null |
F18_linalg_matmul_2x2 | linalg+memref | linalg_spec_30 | 02_matmul-static-2x2 | Write a function that performs matrix multiplication of two 2x2 f32 memrefs and writes the result into the output 2x2 f32 memref. | mm22 | (memref<2x2xf32>, memref<2x2xf32>, memref<2x2xf32>) -> () | null | null | null | (2(\.0+)?[^\d]+){4} | [
{
"name": "A",
"shape": [
2,
2
],
"dtype": "f32",
"init": "fill",
"fill_value": 1,
"static": true
},
{
"name": "B",
"shape": [
2,
2
],
"dtype": "f32",
"init": "fill",
"fill_value": 1,
"static": true
},
{
"name": "C",
"sh... | [
{
"name": "C",
"shape": [
2,
2
],
"dtype": "f32"
}
] | null | null | null |
F19_linalg_exp_elemwise | linalg+memref | linalg_spec_30 | 24_exp-elemwise | linalg.exp elementwise on a 1-D f32 memref | ee | (memref<?xf32>, memref<?xf32>) -> () | null | null | null | (1(\.0+)?[^\d]+){4} | [
{
"name": "x",
"shape": [
4
],
"dtype": "f32",
"init": "fill",
"fill_value": 0
},
{
"name": "y",
"shape": [
4
],
"dtype": "f32",
"init": "alloc_only"
}
] | [
{
"name": "y",
"shape": [
4
],
"dtype": "f32"
}
] | null | null | null |
F20_linalg_abs_elemwise | linalg+memref | linalg_spec_30 | 26_abs-elemwise | linalg.abs elementwise on a 1-D f32 memref | ab | (memref<?xf32>, memref<?xf32>) -> () | null | null | null | (2\.5[^\d]+){4} | [
{
"name": "x",
"shape": [
4
],
"dtype": "f32",
"init": "fill",
"fill_value": -2.5
},
{
"name": "y",
"shape": [
4
],
"dtype": "f32",
"init": "alloc_only"
}
] | [
{
"name": "y",
"shape": [
4
],
"dtype": "f32"
}
] | null | null | null |
F21_stablehlo_add_1d_f32 | stablehlo | stablehlo_spec_30 | 01_add-1d | Write a function that adds two 1-D f32 tensors of 16 elements using stablehlo.add. | a | (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32> | null | null | null | null | null | null | null | [
"16xf32=1.0",
"16xf32=2.0"
] | 16xf32=3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 |
F22_stablehlo_multiply_2d | stablehlo | stablehlo_spec_30 | 04_multiply-2d | Write a function that multiplies two 4x4 f32 tensors elementwise using stablehlo.multiply. | mul | (tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4xf32> | null | null | null | null | null | null | null | [
"4x4xf32=3.0",
"4x4xf32=4.0"
] | 4x4xf32=\[12 12 12 12\]\[12 12 12 12\]\[12 12 12 12\]\[12 12 12 12\] |
F23_stablehlo_subtract_1d_i32 | stablehlo | stablehlo_spec_30 | 03_subtract-1d-i32 | Write a function that subtracts two 1-D i32 tensors of 16 elements using stablehlo.subtract. | s | (tensor<16xi32>, tensor<16xi32>) -> tensor<16xi32> | null | null | null | null | null | null | null | [
"16xi32=10",
"16xi32=4"
] | 16xi32=6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 |
F24_stablehlo_abs_f32 | stablehlo | stablehlo_spec_30 | 06_abs-f32 | Write a function that computes the elementwise absolute value of a 1-D f32 tensor. | ab | (tensor<16xf32>) -> tensor<16xf32> | null | null | null | null | null | null | null | [
"16xf32=-2.5"
] | 16xf32=2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 |
F25_stablehlo_exp_1d | stablehlo | stablehlo_spec_30 | 07_exp-1d | Write a function that computes the elementwise exponential of a 1-D f32 tensor. | ex | (tensor<16xf32>) -> tensor<16xf32> | null | null | null | null | null | null | null | [
"16xf32=0.0"
] | 16xf32=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 |
F26_stablehlo_transpose_2d | stablehlo | stablehlo_spec_30 | 09_transpose-2d | Write a function that transposes a 4x8 f32 tensor producing an 8x4 tensor. | t | (tensor<4x8xf32>) -> tensor<8x4xf32> | null | null | null | null | null | null | null | [
"4x8xf32=1.5"
] | 8x4xf32= |
F27_stablehlo_add_2d_dyn | stablehlo | stablehlo_spec_30 | 02_add-2d-dynamic | Write a function that adds two 2x4 f32 tensors using stablehlo.add. | a2 | (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32> | null | null | null | null | null | null | null | [
"2x4xf32=2.0",
"2x4xf32=2.5"
] | 2x4xf32=\[4.5 4.5 4.5 4.5\]\[4.5 4.5 4.5 4.5\] |
F28_stablehlo_divide_f64 | stablehlo | stablehlo_spec_30 | 05_divide-f64 | Write a function that divides two 1-D f64 tensors of 8 elements using stablehlo.divide. | d | (tensor<8xf64>, tensor<8xf64>) -> tensor<8xf64> | null | null | null | null | null | null | null | [
"8xf64=12.0",
"8xf64=4.0"
] | 8xf64=3 3 3 3 3 3 3 3 |
F29_stablehlo_dot_matmul | stablehlo | stablehlo_spec_30 | 17_dot_general-matmul | Write a function that performs a matmul of two f32 tensors via stablehlo.dot_general (lhs 4x8, rhs 8x4). | dm | (tensor<4x8xf32>, tensor<8x4xf32>) -> tensor<4x4xf32> | null | null | null | null | null | null | null | [
"4x8xf32=1.0",
"8x4xf32=1.0"
] | 4x4xf32=\[8 8 8 8\]\[8 8 8 8\]\[8 8 8 8\]\[8 8 8 8\] |
F30_stablehlo_transpose_3d | stablehlo | stablehlo_spec_30 | 10_transpose-3d | Write a function that transposes a 2x3x4 f32 tensor with permutation [2, 0, 1] producing a 4x2x3 tensor. | t3 | (tensor<2x3x4xf32>) -> tensor<4x2x3xf32> | null | null | null | null | null | null | null | [
"2x3x4xf32=2.5"
] | 4x2x3xf32= |
MLIR-Functional-Reference-30
Hand-authored functional-correctness reference set for arith, linalg+memref, and stablehlo (n=30, 10 per dialect).
This dataset is one of six NL→MLIR benchmarks released alongside the NeurIPS
2026 Evaluations & Datasets track paper Cross-Dialect Generalization Without
Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding
for MLIR (anonymous submission). The full suite — MLIR-Spec-150,
Linalg-Spec-30, StableHLO-Spec-30, StableHLO-Held-Out-200,
StableHLO-OutOfGrammar-25, and MLIR-Functional-Reference-30 — totals 465
instances across three MLIR dialects.
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.pyfor 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
For paper reproducibility, individual per-record JSON files (the
examples/*.json layout used by the companion code repository) and the
MLCommons Croissant 1.0 metadata (croissant.json) ship together with the
release. The JSONL file at data/test.jsonl is the canonical HuggingFace
interface; it is generated 1-to-1 from the source records.
Datasheet
A full Gebru-style datasheet covering motivation, collection, preprocessing,
uses, distribution, and maintenance is included in the companion
reproducibility archive (docs/datasheets/datasheet.md). Key points:
- All reference MLIR programs are verifier-clean at the time of release.
- Hand-authored single-author (no crowdsourcing, no LLM-authored references).
- Test-only — fine-tuning on these benchmarks contaminates future evaluation and is explicitly out of scope.
Companion artifacts
- Reproducibility archive (code + scripts):
submission_artifact.tar.gzin the OpenReview attachment / Zenodo mirror. - Companion code repository: .
Citation
@inproceedings{anonymous2026crossdialect,
title = {Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR},
author = {Anonymous},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},
year = {2026},
note = {Anonymous submission under review.}
}
License
Apache-2.0. See LICENSE.
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