U dJ@sddlZddlmZddlmZddlmZddlZddlZddl m Z m Z ej e ej e ej dddZ deeed d d Ze ed ddZejjdddZdS)N)Node)symbolic_trace)legalize_graph)DictList)resultinputsreturncCs8t|tjjrdgt|}ndd|D}t||S)a A free function for use in the merge_matmul graph transformation below that splits the output from a merged matmul into the individual results for each input tensor. Arguments: result: The merged matmul result tensor. inputs: The list of inputs that were merged into one for the matmul. Returns: List of matmul results for each input tensor. rcSsg|]}|jdqSr)shape).0xrF/tmp/pip-unpacked-wheel-ua33x9lu/torch/fx/experimental/merge_matmul.py sz(split_result_tensors..) isinstancetorchZfxZProxylensplit)rrsplitsrrrsplit_result_tensors sr)ab search_depthcCsP||kr dSt|jdkrdS|dkr*dS|jD]}t|||dr0dSq0dS)a^ Determine if one node depends on another in a torch.fx.Graph. Arguments: a: The node that may have a dependency on b. b: The node that a may have a dependency on. search_depth: In the case of an indirect dependency, this function searches upto this many nodes away in search of a data dependency. If none is found, the function makes the conservative assumption that there is a dependency. Returns: True if a may depend on b, False if it definitely does not. TrF)rZall_input_nodes may_depend_on)rrrinprrrr#s r)nodescCs4t|dD]"\}}t||s(t||r dSq dS)z Check if all of the given nodes are pairwise-data independent. Arguments: nodes: The nodes to check for data dependencies. Returns: True if any pair in nodes has a data dependency. FT) itertools combinationsr)rijrrrare_nodes_independentHs r$)in_modc st|i}i}jjD]r}|jdks|jtjk r4q|j\}}|jdkrN|jn|}|jdkrb|jn|}||g |||g |q| D]\}}t |dkrqt |sqdd|D}fdd|D}t |trj|n|}jtj|fi}jtj||fi} jt| |fifddtt |D} t|| D] \} } | | j| qRtqjS)a A graph transformation that merges matrix multiplication operations that share the same right-hand side operand into one large matrix multiplication. ____ _________ _________ ---- | | | | M| A * C | M| A | T| B | * K| C | = |---------| ---- , | | | | T| B * C | K ---- --------- --------- K R R call_functionget_attrrcSsg|]}|jdqSr )args)r mmrrrrsz merge_matmul..cs&g|]}t|trj|n|qSr)rstrgraphr')r l)gmrrrscs"g|]}jtj|fiqSr)r+r&operatorgetitem)r outr-Zmerge_mm_splitrrrs)rr+roptargetrmatmulr( setdefaultappenditemsrr$rr*r'r&catrrangezipZreplace_all_uses_withZ erase_noderZ recompileZlint) r%Z rhs_usersZ lhs_usersnodelhsrhsmmsZlhs_valsZ merge_mm_catZmerge_mmZ merge_mm_resoldnewrr1r merge_matmulZsH         rA)r)rZ torch.fx.noderZtorch.fx._symbolic_tracerZtorch.fx.passes.tools_commonrr r.typingrrZTensorrintrr$nnModulerArrrrs    %