refactor common used toy model #2729
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Integrates common used toy model and refactor across TorchAO (ao/test/benchmark/tutorial) - fix: pytorch#2078 Test Plan: CI
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@namgyu-youn thanks for taking up this effort |
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| self.linear1 = torch.nn.Linear(k, n, bias=False).to(dtype) | ||
| self.linear1 = torch.nn.Linear(m, n, bias=False) | ||
| self.linear2 = torch.nn.Linear(n, k, bias=False) | ||
| self.linear3 = torch.nn.Linear(k, 1, bias=False) |
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Please create a separate model for two linear layers. This model for single linear layer is used in benchmarking run on CI.
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@jainapurva I prefer to define ToySingleLinearModel and ToyMultiLinearModel for a future update as you mentioned, but how about reverting benchmark_aq.py?
Unit tests (e.g., test_quant_api.py, test_awq.py) are using single/multiple layers in a mixed manner, and using only multiple layers might be an update. If this makes sense, benchmark_aq.py would be the only case using single layers. Let me know which one aligns better.
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ToySingleLinearModel and ToyMultiLinearModel sounds good. Please ensure all the tests are running smoothly for it.
For benchmark_aq.py you can add the bias parameter as the last arg in init and set it to False by default. In addition to this, ToySingleLinearModel is used in running .github/workflows/run_microbenchmarks.yml. It uses the create_model_and_input_data, please ensure that method is running smoothly, and is updated as per the new toy models.
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Sorry for opening the PR without checking it. And I will move into your suggestion; thanks for your leading.
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@namgyu-youn Please feel free to divide this into multiple PRs if it's too many changes. |
Integrate commonly used single/multi-linear toy models and refactor them across the codebase (src/test/benchmark/tutorial). - fix: pytorch#2078 Test Plan: CI
Summary:
Integrate commonly used single/multi-linear toy models and refactor them across the codebase (src/test/benchmark/tutorial).
Test Plan: CI