| ---
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| pretty_name: Model Operator Workloads
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| license: cc-by-4.0
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| language:
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| - en
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| size_categories:
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| - 10K<n<100K
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| tags:
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| - benchmarking
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| - gpu
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| - operator-workloads
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| - gemm
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| - model-architecture
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| configs:
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| - config_name: gemm_runtime
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| default: true
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| data_files:
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| - split: train
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| path: data/gemm_runtime/train-00000-of-00001.parquet
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| - config_name: gemm_weights
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| data_files:
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| - split: train
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| path: data/gemm_weights/train-00000-of-00001.parquet
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| - config_name: gemm_benchmark
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| data_files:
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| - split: train
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| path: data/gemm_benchmark/train-00000-of-00001.parquet
|
| - config_name: convolution
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| data_files:
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| - split: train
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| path: data/convolution/train-00000-of-00001.parquet
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| - config_name: convolution_lowerings
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| data_files:
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| - split: train
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| path: data/convolution_lowerings/train-00000-of-00001.parquet
|
| - config_name: attention
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| data_files:
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| - split: train
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| path: data/attention/train-00000-of-00001.parquet
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| - config_name: embedding
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| data_files:
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| - split: train
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| path: data/embedding/train-00000-of-00001.parquet
|
| - config_name: moe
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| data_files:
|
| - split: train
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| path: data/moe/train-00000-of-00001.parquet
|
| - config_name: normalization
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| data_files:
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| - split: train
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| path: data/normalization/train-00000-of-00001.parquet
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| - config_name: pooling
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| data_files:
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| - split: train
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| path: data/pooling/train-00000-of-00001.parquet
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| - config_name: reduction
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| data_files:
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| - split: train
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| path: data/reduction/train-00000-of-00001.parquet
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| - config_name: transform
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| data_files:
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| - split: train
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| path: data/transform/train-00000-of-00001.parquet
|
| ---
|
|
|
| # Model Operator Workloads
|
|
|
| Unique neural-network operator shapes and selected reference workloads for kernel
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| 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**.
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| Three supplemental configurations expose static GEMM weight shapes, synthetic GEMM
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| benchmark shapes, and symbolic convolution lowerings. All 12 configurations total
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| **60,690 rows**. This sum is across different views and is
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| not a claim of global uniqueness across operators or views. Each configuration is
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| unique on its documented workload identity, excluding ranks and provenance counts.
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| Module instances, expanded checkpoint instances, and symbolic module inventories
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| 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 |
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| | `moe` | 4,626 | reference_workloads |
|
| | `normalization` | 4,416 | reference_workloads |
|
| | `pooling` | 342 | reference_workloads |
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| | `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
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| from datasets import load_dataset
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|
|
| repo_id = "hlky/model-operator-workloads"
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| gemm = load_dataset(repo_id, "gemm_runtime", split="train")
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| attention = load_dataset(repo_id, "attention", split="train")
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| ```
|
|
|
| ## 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
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| workload, execution form, and batch-head count.
|
| - `gemm_weights`: oriented architecture K,N pairs. Header evidence can assume
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| ordinary PyTorch N,K weight storage; custom/packed layouts need interpretation.
|
| - `gemm_benchmark`: synthetic Cartesian expansion over selected M buckets
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| (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,
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| stride, padding, dilation, bias, and symbolic lowering geometry explicit.
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| Ordinary lowering uses M=B*output_positions, N=Cout/groups,
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| K=(Cin/groups)*kernel_volume. Transposed lowering uses M=B*input_positions,
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| N=(Cout/groups)*kernel_volume, K=Cin/groups.
|
| - `convolution_lowerings`: unique per-group lowering signatures. Summed checkpoint
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| 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,
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| and cross-attention behavior retained.
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| - `embedding`: lookup/gather and vocabulary projection, with table geometry,
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| token counts, tied-weight state, dtype, and output scope.
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| - `moe`: expert GEMM dimensions, selected routed-token profiles, expert grouping,
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| routing assumptions, capacity policy and dtype. Routing distributions are selected
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| estimates, not captured routing traffic.
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| - `normalization`: LayerNorm/RMSNorm/GroupNorm/BatchNorm row and reduction geometry,
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| epsilon, affine state, groups, and dtype.
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| - `pooling`: full selected tensor geometry, pooling parameters and semantics.
|
| - `reduction`: outer rows and reduction width with mask, top-k, dtype, execution
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| mode and fused context. Some vocabulary operations are consumer-selected.
|
| - `transform`: tensor transforms/resampling with selected input/output geometry,
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| operation parameters, layout and dtype.
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|
|
| ## Schema and missing values
|
|
|
| See [SCHEMA.md](SCHEMA.md) and [schema.json](schema.json) for every column, type,
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| null count and identity key. Source column names and row order are retained except
|
| for the audited MoE consolidation below. Unambiguous integers become int64;
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| true/false columns become boolean. Epsilon remains a string because numeric values
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| coexist with `not_exposed`. Empty cells become null. Explicit markers such as `not_applicable`,
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| `not_configured`, `not_observed`, `runtime_selected`, and literal `null` strings
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| remain strings. Mixed numeric/marker columns remain strings. Geometry strings and
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| JSON parameters retain their original encoding. Semicolon-delimited examples and
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| aggregates remain source strings: examples can be truncated and must not be treated
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| as exhaustive membership lists. Unknown family markers are preserved.
|
|
|
| ## Limitations
|
|
|
| Counts and rankings represent this source snapshot's coverage, not deployment
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| frequency, market share, hardware performance, or complete production layer counts.
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| Source families can overlap. Source counting conventions and treatment of missing
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| checkpoint IDs can vary by operator. No new frequency estimates are introduced.
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| Reference batch/sequence/spatial sizes, routing distributions and FLOPs are selected
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| 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.
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| 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
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| under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
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| Credit **hlky**, link to the dataset and license, and indicate changes when applicable.
|
| Upstream model identifiers and architecture metadata retain their respective origins;
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| 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).
|
| |
