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15b61d6c1a
Currently whenever the sizes or strides are modified for a `TensorImpl` we eagerly recompute the numel and memory format flags. This is fine for static shapes as it's all fast C++ code, but for symbolic shapes it runs slow python code. This instead changes the `SymbolicShapeMeta` object to compute the derived quantities lazily at the first request. This has the added benefit that we can now pass assumptions in `empty_tensor_restride` which remove the need to compute some contiguity flags at all. Pull Request resolved: https://github.com/pytorch/pytorch/pull/112785 Approved by: https://github.com/ezyang ghstack dependencies: #112689, #112890 |
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| BUILD.bazel | ||
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