[ModelSuite] Add model loading infrastructure #177
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This was referenced Oct 2, 2025
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Here we introduce model suite (model.py). The idea here to start and codify the ideas from jiannanWang/BackendBenchExamples. Specifically this PR deals with model registration and loading. ### Model Registration This PR creates a way of adding models to the suite and automatically validates them through CI. It also loads the models as well. The way these models are added is detailed in this readme. The tl;dir is we use a format similar to kernelbench and SakanaAI/robust-kbench where we pair model code with a config. Importantly the configs contain initialization code, forward pass arguments (both in a similar format to torchbench), and a list of ops in the forward and backwards passes. These ops are fairly important as they are what we want to point out to the researcher when they are optimizing a model. There is a README.md to help folks setup proper model code / configs. We also further verify these registrations are correct through CI. Specifically we run test/test_model_ops_configs.py to ensure the configs are formatted correctly. ### Added models I added 2 models here as examples. SmokeTestModel - This is simple model that uses aten.ops.mm as we can implement a correct version of this op ToyCoreOpsModel - This is a model which explicitly calls the backwards passes which are both in torchbench + core. ### Small Things - Added a --model-filter to the CLI as it will be needed to support filtering in model suite as it chooses things to test based on the model not set of ops ### Testing New tests are added so pytest resolves things here ### Future work with Model Suite #181
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Here we introduce model suite (model.py). The idea here to start and codify the ideas from jiannanWang/BackendBenchExamples. Specifically this PR deals with model registration and loading.
Model Registration
This PR creates a way of adding models to the suite and automatically validates them through CI. It also loads the models as well. The way these models are added is detailed in this readme. The tl;dir is we use a format similar to kernelbench and SakanaAI/robust-kbench where we pair model code with a config. Importantly the configs contain initialization code, forward pass arguments (both in a similar format to torchbench), and a list of ops in the forward and backwards passes. These ops are fairly important as they are what we want to point out to the researcher when they are optimizing a model. There is a README.md to help folks setup proper model code / configs.
We also further verify these registrations are correct through CI. Specifically we run test/test_model_ops_configs.py to ensure the configs are formatted correctly.
Added models
I added 2 models here as examples.
SmokeTestModel - This is simple model that uses aten.ops.mm as we can implement a correct version of this op
ToyCoreOpsModel - This is a model which explicitly calls the backwards passes which are both in torchbench + core.
Small Things
Testing
New tests are added so pytest resolves things here
Future work with Model Suite
#181
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