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Summary: This PR modifies `benchmarks/tensorexpr`. It follows up[ https://github.com/pytorch/pytorch/issues/44101](https://github.com/pytorch/pytorch/pull/44101) and further supports characterizing fusers with dynamic shape benchmarks. Dynamic shape condition models the use case when the input tensor shape changes in each call to the graph. Changes include: Added an auxiliary class `DynamicShape `that provides a simple API for enabling dynamic shapes in existing test cases, example can be found with `DynamicSimpleElementBench` Created new bench_cls: `DynamicSimpleElementBench`, `DynamicReduce2DInnerBench`, `DynamicReduce2DOuterBench`, and `DynamicLSTM`. They are all dynamic shaped versions of existing benchmarks and examples of enabling dynamic shape with `DynamicShape`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/46107 Reviewed By: glaringlee Differential Revision: D24229400 Pulled By: bertmaher fbshipit-source-id: 889fece5ea87d0f6f6374d31dbe11b1cd1380683 |
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| .. | ||
| __main__.py | ||
| attention.py | ||
| benchmark.py | ||
| broadcast.py | ||
| conv.py | ||
| elementwise.py | ||
| HowToRun.md | ||
| matmul.py | ||
| normalization.py | ||
| pooling.py | ||
| pt_engine.py | ||
| reduction.py | ||
| rnn_eltwise.py | ||
| softmax.py | ||
| swish.py | ||
| tensor_engine.py | ||