o i@sddlZddlZddlZddlmZddlmZddlmZdej de ej de ej dffd d Z dd ed ede fddZde efddZdejjfddZdS)N)symbolic_trace)Node)legalize_graphresultinputsreturn.cCs8t|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. rcSg|]}|jdqSr)shape).0xr _/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py z(split_result_tensors..) isinstancetorchfxProxylensplit)rrsplitsr r rsplit_result_tensors s rab search_depthcCsP||krdSt|jdkrdS|dkrdS|jD] }t|||dr%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. TrF)rall_input_nodes may_depend_on)rrrinpr r rr$s rnodescCs4t|dD]\}}t||st||rdSqdS)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)r!ijr r rare_nodes_independentIs  r'in_modc st|i}i}jjD]9}|jdks|jtjurq |j\}}|jdkr'|jn|}|jdkr1|jn|}||g |||g |q | D]s\}}t |dkrUqJt |sZqJdd|D}fdd|D}t |truj|n|}jtj|fi}jtj||fi} jt| |fifddtt |D} t|| D]\} } | | j| qtqJjS)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_attrr"cSrr )args)r mmr r rrrz merge_matmul..cs&g|]}t|trj|n|qSr )rstrgraphr*)r l)gmr rrs&cs"g|] }jtj|fiqSr )r.r)operatorgetitem)r outr0merge_mm_splitr rrs)rr.r!optargetrmatmulr+ setdefaultappenditemsrr'rr-r*r)catrrangezipreplace_all_uses_with erase_noder recompilelint) r( rhs_users lhs_usersnodelhsrhsmmslhs_vals merge_mm_catmerge_mm merge_mm_resoldnewr r4r merge_matmul[sP          rO)r)r#r1rtorch.fx._symbolic_tracer torch.fx.nodertorch.fx.passes.tools_commonrTensorlisttuplerintrr'nnModulerOr r r rs      %