[Benchmark] Add ModelConfig Sweep Support and Pre-Probe to Remaining Benchmarks#1195
Merged
Tcc0403 merged 2 commits intolinkedin:mainfrom Apr 18, 2026
Merged
[Benchmark] Add ModelConfig Sweep Support and Pre-Probe to Remaining Benchmarks#1195Tcc0403 merged 2 commits intolinkedin:mainfrom
Tcc0403 merged 2 commits intolinkedin:mainfrom
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- Add Qwen 2.5 models (7B / 14B / 72B) and DeepSeek models (V2 Lite / V3) to MODEL_REGISTRY - Add model_config sweep support to all 33 benchmark scripts, enabling benchmarks to sweep across different model architectures at a fixed sequence length - Refactor benchmark scripts by extracting helper functions: - _setup_* - _resolve_model_config_* to improve code reuse and keep implementations cleaner across sweep modes - Add grouped bar chart visualization in benchmarks_visualizer for model_config sweep results
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Summary
model_configsweep support to training and Rope op benchmarks, enabling evaluation across different model architectures at a fixed sequence lengthbenchmark_attn_res.py
benchmark_embedding.py
benchmark_fused_neighborhood_attention.py (retains all kernel configs for
model_configsweep instead of sweeping model architectures)benchmark_mhc.py
benchmark_mhc_lm.py
benchmark_multi_token_attention.py
benchmark_relu_squared.py
benchmark_softmax.py
benchmark_sparsemax.py
benchmark_sparse_multi_token_attention.py
benchmark_tiled_mlp.py
make testto ensure correctnessmake checkstyleto ensure code stylemake test-convergenceto ensure convergence