קgL XddlmZddlmZddlmZmZdZdZdZ dZ dZ d Z d Z d S) )Refine) TensorType)Varunifyct|}|t|j}t |j|dS)z- Calls our symbolic inferencer once. N)rrefineunify_eq constraintssubstitute_all_typesgraphtracedrmgus c/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/fx/experimental/unify_refinements.py infer_symbolic_types_single_passrsC vAHHJJJ 1= ! !Cs+++++c^t|}|t|j}t |j|t|}|t|j}t |j||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)rrr r r r symbolic_relationsr s rinfer_symbolic_typesrs vAHHJJJ 1= ! !Cs+++vAHHJJJ 1= ! !Cs+++rcg}g}|D]6}||j||j7t|t|fS)za Convert equality constraints in the right format to be used by unification library. )appendlhsrhstuple) list_of_eqrreqs r convert_eqr#s_ C C 26 26 ::uSzz !!rcFt|\}}t||S)z@ Apply unification to a set of equality constraints )rr)rrrs rr r 0s# *%%HC c??rct|tr ||vr||S|St|trog}|jD]I}||vr|||4||Jtt |St|tr,g}|D]%}|t||&|St|t r9g}|D]%}|t||&t |S|S)z2 Apply the most general unifier to a type ) isinstancerkeysr__args__rrlistsubstitute_solution_one_type)mappingtnew_typetyps rr%r%9s]!S    1: H Az " ": % %Cgllnn$$ ----$$$$%//*** At    H HC OO8#FF G G G G Au   H HC OO8#FF G G G GXrcd}|rNd}|D]G}||}|||vr||}||||<|||krd}H|N|jD]}t||j|_dS)z 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)r"nodesr%type)r r&flagkold_mapping_valnew_keyns rr r \s D   A%ajOqzW\\^^++!!*$W- '!*,, [??-gqv>>??rclt|j|jD]\}}|j|jkrdSdS)zv A check equality to be used in fixed points. We do not use graph equality but instead type equality. FT)zipr+r,)g1g2r1ms rcheck_for_type_equalityr7psE BHbh''1 6QV  55  4rN)/torch.fx.experimental.graph_gradual_typecheckerrtorch.fx.tensor_typer!torch.fx.experimental.unificationrrrrrr r%r r7rrr<sBBBBBB++++++88888888,,,& " " "   F???(     r