---
pretty_name: Model Operator Workloads
license: cc-by-4.0
language:
- en
size_categories:
- 10K<n<100K
tags:
- benchmarking
- gpu
- operator-workloads
- gemm
- model-architecture
configs:
- config_name: gemm_runtime
  default: true
  data_files:
  - split: train
    path: data/gemm_runtime/train-00000-of-00001.parquet
- config_name: gemm_weights
  data_files:
  - split: train
    path: data/gemm_weights/train-00000-of-00001.parquet
- config_name: gemm_benchmark
  data_files:
  - split: train
    path: data/gemm_benchmark/train-00000-of-00001.parquet
- config_name: convolution
  data_files:
  - split: train
    path: data/convolution/train-00000-of-00001.parquet
- config_name: convolution_lowerings
  data_files:
  - split: train
    path: data/convolution_lowerings/train-00000-of-00001.parquet
- config_name: attention
  data_files:
  - split: train
    path: data/attention/train-00000-of-00001.parquet
- config_name: embedding
  data_files:
  - split: train
    path: data/embedding/train-00000-of-00001.parquet
- config_name: moe
  data_files:
  - split: train
    path: data/moe/train-00000-of-00001.parquet
- config_name: normalization
  data_files:
  - split: train
    path: data/normalization/train-00000-of-00001.parquet
- config_name: pooling
  data_files:
  - split: train
    path: data/pooling/train-00000-of-00001.parquet
- config_name: reduction
  data_files:
  - split: train
    path: data/reduction/train-00000-of-00001.parquet
- config_name: transform
  data_files:
  - split: train
    path: data/transform/train-00000-of-00001.parquet
---

# Model Operator Workloads

Unique neural-network operator shapes and selected reference workloads for kernel
benchmarking, workload selection, and architecture coverage analysis.
Created by [hlky](https://github.com/hlky).

## What is included

The nine primary operator configurations contain **43,150 rows**.
Three supplemental configurations expose static GEMM weight shapes, synthetic GEMM
benchmark shapes, and symbolic convolution lowerings. All 12 configurations total
**60,690 rows**. This sum is across different views and is
not a claim of global uniqueness across operators or views. Each configuration is
unique on its documented workload identity, excluding ranks and provenance counts.
Module instances, expanded checkpoint instances, and symbolic module inventories
are excluded. The duplicate unranked GEMM weight list is also excluded.

| Configuration | Unique rows | Evidence/workload category |
| --- | ---: | --- |
| `gemm_runtime` | 9,418 | reference_workloads |
| `gemm_weights` | 1,961 | architecture_shapes |
| `gemm_benchmark` | 13,727 | synthetic_benchmark |
| `convolution` | 2,920 | architecture_shapes |
| `convolution_lowerings` | 1,852 | symbolic_lowerings |
| `attention` | 3,415 | reference_workloads |
| `embedding` | 8,182 | reference_workloads |
| `moe` | 4,626 | reference_workloads |
| `normalization` | 4,416 | reference_workloads |
| `pooling` | 342 | reference_workloads |
| `reduction` | 8,843 | reference_workloads |
| `transform` | 988 | reference_workloads |

Each configuration has a single `train` split for loading convenience. This is an
inventory, not a training corpus; no independent train/test partition is implied.

## Loading

```python
from datasets import load_dataset

repo_id = "hlky/model-operator-workloads"
gemm = load_dataset(repo_id, "gemm_runtime", split="train")
attention = load_dataset(repo_id, "attention", split="train")
```

## Workload meaning

- `gemm_runtime`: unique M,N,K tuples from selected linear and per-head attention
  reference workloads. A tuple can occur in multiple `execution_forms`. Keep those
  forms and `attention_batch_groups` when interpreting it; batch-head groups are
  not folded into M. The aggregates do not preserve every pairing between a
  workload, execution form, and batch-head count.
- `gemm_weights`: oriented architecture K,N pairs. Header evidence can assume
  ordinary PyTorch N,K weight storage; custom/packed layouts need interpretation.
- `gemm_benchmark`: synthetic Cartesian expansion over selected M buckets
  (1, 16, 64, 256, 1024, 4096, 16384). These are benchmark choices, not traced model shapes.
- `convolution`: unique Conv/ConvTranspose 1D–3D module geometries, with groups,
  stride, padding, dilation, bias, and symbolic lowering geometry explicit.
  Ordinary lowering uses M=B*output_positions, N=Cout/groups,
  K=(Cin/groups)*kernel_volume. Transposed lowering uses M=B*input_positions,
  N=(Cout/groups)*kernel_volume, K=Cin/groups.
- `convolution_lowerings`: unique per-group lowering signatures. Summed checkpoint
  and family counts may count a source repeatedly across different convolution shapes.
- `attention`: B,heads,Q,K,head_dim with KV grouping, masks, dtype, prefill/decode,
  and cross-attention behavior retained.
- `embedding`: lookup/gather and vocabulary projection, with table geometry,
  token counts, tied-weight state, dtype, and output scope.
- `moe`: expert GEMM dimensions, selected routed-token profiles, expert grouping,
  routing assumptions, capacity policy and dtype. Routing distributions are selected
  estimates, not captured routing traffic.
- `normalization`: LayerNorm/RMSNorm/GroupNorm/BatchNorm row and reduction geometry,
  epsilon, affine state, groups, and dtype.
- `pooling`: full selected tensor geometry, pooling parameters and semantics.
- `reduction`: outer rows and reduction width with mask, top-k, dtype, execution
  mode and fused context. Some vocabulary operations are consumer-selected.
- `transform`: tensor transforms/resampling with selected input/output geometry,
  operation parameters, layout and dtype.

## Schema and missing values

See [SCHEMA.md](SCHEMA.md) and [schema.json](schema.json) for every column, type,
null count and identity key. Source column names and row order are retained except
for the audited MoE consolidation below. Unambiguous integers become int64;
true/false columns become boolean. Epsilon remains a string because numeric values
coexist with `not_exposed`. Empty cells become null. Explicit markers such as `not_applicable`,
`not_configured`, `not_observed`, `runtime_selected`, and literal `null` strings
remain strings. Mixed numeric/marker columns remain strings. Geometry strings and
JSON parameters retain their original encoding. Semicolon-delimited examples and
aggregates remain source strings: examples can be truncated and must not be treated
as exhaustive membership lists. Unknown family markers are preserved.

## Limitations

Counts and rankings represent this source snapshot's coverage, not deployment
frequency, market share, hardware performance, or complete production layer counts.
Source families can overlap. Source counting conventions and treatment of missing
checkpoint IDs can vary by operator. No new frequency estimates are introduced.
Reference batch/sequence/spatial sizes, routing distributions and FLOPs are selected
or derived rather than observed execution. Unsupported or unresolved workloads in
the wider inventories are absent from these unique resolved lists. Configured
compute dtype and observed weight dtype are distinct; unpinned dtype stays explicit.
Different dtype, mask, grouping, layout or operation semantics remain distinct even
when numerical dimensions coincide. Do not concatenate configurations and deduplicate
solely by dimensions. Validate inferred shapes and assumptions for the target model
and backend before using them as kernel correctness fixtures.

## License and attribution

Copyright 2026 hlky. The dataset compilation and original contributions are licensed
under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
Credit **hlky**, link to the dataset and license, and indicate changes when applicable.
Upstream model identifiers and architecture metadata retain their respective origins;
this license does not relicense upstream model weights, code, or documents.

Suggested attribution: “Model Operator Workloads by hlky
(https://github.com/hlky), CC BY 4.0.” Include the dataset repository URL when citing
the uploaded dataset. See [ATTRIBUTION.md](ATTRIBUTION.md).