pytorch

f6f35135 - suggest constraints to specify for export based on generated shape guards (#98463)

suggest constraints to specify for export based on generated shape guards (#98463) The design of export API expects constraints to be specified on dynamic dimensions, while assuming all other dimensions are static by default. However a user who wishes to export a model may not be fully familiar with the code to plan what to specify. This diff provides support for discovering constraints to specify. The basic idea is to take the set of generated shape guards and convert them into appropriate constraints. However, we usually generate a LOT of shape guards, and there is often a LOT of redundancy in them. Thus, we also need to simplify the guards so that our suggested constraints are concise yet capture the information content in the guards. The algorithm for simplification uses `sympy` under the hood, but very surgically to avoid any risk of blowing up. See comments inline for a full description. Briefly, 1. We consider only univariate inequalities, and among them, solve for equalities first. 2. We substitute these exact solutions to convert multivariate inequalities progressively into univariate. 3. Remaining univariate inequalities are solved using `sympy.solvers.inequalities.reduce_inequalities`. 4. As pre-processing, we also eliminate all `//` and `%` operations to generate a set of linear congruence guards, and solve these using `sympy.ntheory.modular.solve_congruence`. The results are quite dramatic. For example, an internal model produced several hundreds of guards with `dynamic_shapes=True`, which were pretty much inscrutable for humans. The summary contains around 30 dimensions that were specialized and 3 constraints on dynamic dimensions. The output format looks like this: ``` The following dimensions have been specialized and CANNOT be dynamic. NOTE: Specializations will happen by default with `assume_static_by_default=True`. L['foo']['bar'].size()[0] == 4 ... L['baz']['qux'].size()[3] == 96 The following dimensions CAN be dynamic. You can use the following code to specify the constraints they must satisfy: constraints=[ dynamic_dim(L['blah']['bleh'], 1) == dynamic_dim(L['blah']['bloh'], 1), ..., 2 <= dynamic_dim(L['blah']['bloh'], 1), ] ``` Differential Revision: [D44731747](https://our.internmc.facebook.com/intern/diff/D44731747/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/98463 Approved by: https://github.com/voznesenskym, https://github.com/ezyang