U ‰d1 ã@sdddlmZddlmZddlmZmZdd„Zdd„Zdd „Z d d „Z d d „Z dd„Z dd„Z dS)é)ÚRefine)Ú TensorType)ÚVarÚunifycCs*t|ƒ}| ¡t|jƒ}t|j|ƒdS)z- Calls our symbolic inferencer once. N)rÚrefineÚunify_eqÚ constraintsÚsubstitute_all_typesÚgraph©ZtracedÚrZmgu©r úK/tmp/pip-unpacked-wheel-ua33x9lu/torch/fx/experimental/unify_refinements.pyÚ infer_symbolic_types_single_passs rcCsXt|ƒ}| ¡t|jƒ}t|j|ƒt|ƒ}| ¡t|jƒ}t|j|ƒ| ¡dS)z¥ Calls our symbolic inferencer twice. This is useful when one pass is not enough to infer all the information such as the case for braodcasting. N)rrrrr r Zsymbolic_relationsr r r rÚinfer_symbolic_typess    rcCs:g}g}|D]}| |j¡| |j¡q t|ƒt|ƒfS)za Convert equality constraints in the right format to be used by unification library. )ÚappendÚlhsÚrhsÚtuple)Ú list_of_eqrrÚeqr r rÚ convert_eq"s  rcCst|ƒ\}}t||ƒS)z@ Apply unification to a set of equality constraints )rr)rrrr r rr/s rcCsÔt|tƒr$|| ¡kr||S|Sn¬t|tƒrpg}|jD]*}|| ¡krX| ||¡q8| |¡q8tt|ƒƒSt|tƒrœg}|D]}| t||ƒ¡q‚|St|tƒrÌg}|D]}| t||ƒ¡q®t|ƒS|SdS)z2 Apply the most general unifier to a type N) Ú isinstancerÚkeysrÚ__args__rrÚlistÚsubstitute_solution_one_type)ÚmappingÚtÚnew_typeÚtypr r rr8s,         rcCsrd}|rTd}|D]@}||}||| ¡kr@||}||||<|||krd}qq|jD]}t||jƒ|_qZdS)zž Apply the most general unifier to all types in a graph till reaching a fixed point. If the input and output graph are the same, we converge. TFN)rÚnodesrÚtype)r rÚflagÚkZold_mapping_valZnew_keyÚnr r rr [s   r cCs.t|j|jƒD]\}}|j|jkrdSqdS)zv A check equality to be used in fixed points. We do not use graph equality but instead type equality. FT)Úzipr!r")Zg1Zg2r%Úmr r rÚcheck_for_type_equalityos r(N)Z/torch.fx.experimental.graph_gradual_typecheckerrZtorch.fx.tensor_typerZ!torch.fx.experimental.unificationrrrrrrrr r(r r r rÚs     #