o ÚÜÓh]ã@s¸ddlZddlmZddlmZddlmZddlZddlZddl m Z m Z m Z dej de ej de ej d ffd d „Zdd ededefdd„Zde efdd„Zdejjfdd„ZdS)éN)ÚNode)Úsymbolic_trace)Úlegalize_graph)ÚDictÚListÚTupleÚresultÚinputsÚreturn.cCs8t|tjjƒrdgt|ƒ}ndd„|Dƒ}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. rcSóg|]}|jd‘qS©r)Úshape)Ú.0Úx©rúX/var/www/html/ai/venv/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.pyÚ óz(split_result_tensors..)Ú isinstanceÚtorchÚfxÚProxyÚlenÚsplit)rr ÚsplitsrrrÚsplit_result_tensors s réÚaÚbÚ search_depthcCsP||krdSt|jƒdkrdS|dkrdS|jD] }t|||dƒr%dSqdS)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. TrFé)rÚall_input_nodesÚ may_depend_on)rrrÚinprrrr"%s ÿr"ÚnodescCs4t |d¡D]\}}t||ƒst||ƒrdSqdS)zØ 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. éFT)Ú itertoolsÚ combinationsr")r$ÚiÚjrrrÚare_nodes_independentJs  ÿr*Úin_modc s’t|ƒ‰i}i}ˆjjD]9}|jdks|jtjurq |j\}}|jdkr'|jn|}|jdkr1|jn|}| |g¡  |¡| |g¡  |¡q |  ¡D]s\}}t |ƒdkrUqJt |ƒsZqJdd„|Dƒ}‡fdd„|Dƒ}t |tƒruˆj |¡n|}ˆj tj|fi¡}ˆj tj||fi¡} ˆj t| |fi¡‰‡‡fdd„tt |ƒƒDƒ} t|| ƒD]\} } |  | ¡ˆj | ¡q©tˆƒqJˆ ¡ˆj ¡ˆS)aÚ A graph transformation that merges matrix multiplication operations that share the same right-hand side operand into one large matrix multiplication. ____ _________ _________ ---- | | | | M| A * C | M| A | T| B | * K| C | = |---------| ---- , | | | | T| B * C | K ---- --------- --------- K R R Ú call_functionÚget_attrr%cSr r )Úargs)rÚmmrrrr‡rz merge_matmul..cs&g|]}t|tƒrˆj |¡n|‘qSr)rÚstrÚgraphr-)rÚl)Úgmrrr‹s&cs"g|] }ˆj tjˆ|fi¡‘qSr)r1r,ÚoperatorÚgetitem)rÚout©r3Úmerge_mm_splitrrr›sÿÿ)rr1r$ÚopÚtargetrÚmatmulr.Ú setdefaultÚappendÚitemsrr*rr0r-r,ÚcatrÚrangeÚzipÚreplace_all_uses_withÚ erase_noderÚ recompileÚlint) r+Ú rhs_usersÚ lhs_usersÚnodeÚlhsÚrhsÚmmsÚlhs_valsÚ merge_mm_catÚmerge_mmÚ merge_mm_resÚoldÚnewrr7rÚ merge_matmul\sD     ÿ  þ   rR)r)rÚ torch.fx.noderÚtorch.fx._symbolic_tracerÚtorch.fx.passes.tools_commonrr&r4ÚtypingrrrÚTensorrÚintr"r*ÚnnÚModulerRrrrrÚs"   ÿÿ  þ%