o h`%@sXddlmZmZmZmZmZmZmZddlZddl m Z ddl Z ddl Z ddl mZddlmZgdZeee jee jfZee jefZee jjZee jjZeeZhdZedd d d Zedd d eee jjfd e jjdefddZedd d e jjde fddZ!edd GdddZ"edd de jj#de jj#fddZ$dS))ListTupleUnionDictAnySetMappingN) dataclass)_get_qualified_name) compatibility)get_acc_ops_nameget_node_targetis_node_output_tensorFxNetAccFusionsFinderlegalize_graph> call_method call_module call_functionF)is_backward_compatiblecCsPt|tr|S|jrd|jvrd|jS|jdd}|r |ndd|jS)Nacc_opsacc_ops.z torch._opsz torch.ops.) isinstancestr __module____name__replace)kmoduler R/var/www/html/ai/venv/lib/python3.10/site-packages/torch/fx/passes/tools_common.pyr s  r submodulesnodereturncCs|jtvsJddtd|j|jdkr2t|jts!J||j}t|dt|}t|S|jdkrN|j}|j durJd|j vrJd |j St |St|jtsVJ|jS) a, Given a `node` returns its target typename. For "call_method" node, return node.target which is the name of that method being called. This could potential lead to conflict but should be okay because normally it's on a tensor. For "call_function" node, return typename of node.target. For "call_module" node, return typename of the module that node.target point to. If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by "torch". e.g. _VariableFunctionsClass.relu would become torch.relu. zExpect op types of z, z , but found r_base_class_originrNrr) opCALLABLE_NODE_OPSjoinrtargetrgetattrtyper rrr )r"r#submod submod_typer)r r r!r s"     r cCs"|jdd}|duot|tjS)aChecks if the node output produces a Tensor or not. NOTE: This requires to run `ShapeProp` on the containing fx graph before calling this function. This is because it works by checking the `type` metadata on the node. This metadata is produced by the `ShapeProp`. r+N)metaget issubclasstorchTensor)r#type_r r r!rCsrc@sleZdZdZdejjdefddZe GdddZ dd d e ee ffd d Z d eejjeffddZdS)rz Finds groups of connected ACC nodes that pass non-tensor data between each other. Such groups are called fusion groups. r acc_nodescCs||_t|jj|_||_dSN)rlistgraphnodesr4)selfrr4r r r!__init__Us zFxNetAccFusionsFinder.__init__c@s6eZdZUeed<eed<eed<eed<ddZdS)!FxNetAccFusionsFinder.FusionGroup top_node_idxr8inputsnodes_need_processcsR|jvrdSj|j|j|jfdd|jDdS)z5 Add a node to fusion group. Ncs$h|]}|jtvr|jvr|qSr )r&r'r8).0nr9r r! ss z=FxNetAccFusionsFinder.FusionGroup.add_node..)r8r>addr=discardupdateall_input_nodes)r9r#r rAr!add_nodehs     z*FxNetAccFusionsFinder.FusionGroup.add_nodeN)rr __qualname__int__annotations__NodeSetrGr r r r! FusionGroupZs  rL fusion_groupr;r=cCs\|D])}|jtvr q|j||jkrq||jvrdS|||jr+||dSqdS)z Start from inputs and going reverse topological order. If any upstream node is in the fusion group, add all the nodes in this path to fusion group. TF)r&r'r8indexr<recursive_add_noderFrG)r9rMr=argr r r!rOzs   z(FxNetAccFusionsFinder.recursive_add_noder$cCsni}t|j}|D]}||vrq |jtvrq d|jvrq ||jvr"q |j|j||ht|j |hd}|j r|j }| ||j d|jvrf|jD]}|jtvrSqK||jvrYqK||| ||j qK|j D]+}|jtvrqqid|jvrwqi||jvr}qi||t|j|j||_| ||j qi|j s7t|j|jks|j|j8_q |jD]}|j||<qq |S)N tensor_meta)r<r8r=r>)r6r4r&r'r.rLr8rNsetrFr>poprOr=usersrGminr<)r9resultr4r#rMuserrPr@r r r!__call__s^                  zFxNetAccFusionsFinder.__call__N)rrrH__doc__r1fx GraphModulerKr:r rLrNodeListrOrNoderXr r r r!rNs  rgmcs&dd|jjDtj}|jjD]}|jD] }|d7<qqt}|jjD] }|dkr8||q+it |dkrp| }| |fdd|<|jD]}|d8<|dkri||qTt |dksAt |jt |jjkrt dfdd D|jj |_ ||_|S) a Replace the graph of the given GraphModule with one that contains the same nodes as the original, but in topologically sorted order. This is used by the merge_matmul transformation below, which disturbs the topologically sorted order of its input GraphModule, so that this order is restored before further transformation. Arguments: gm: The graph module to topologically sort. It is modified in-place. Returns: The graph module in-place sorted cSsi|]}|dqSrr r?r#r r r! sz"legalize_graph..rcs|Sr5r )x)envr r!sz legalize_graph..z&Input graph has cycles, unable to add csg|] }|dkr|qSr_r r`)indegr r! sz"legalize_graph..)r7r8r1rZGraphrT collectionsdequeappendlenpopleft node_copy RuntimeError_codegen)r^ new_graphr#rWqueuecurr )rdrfr!rs4             r)%typingrrrrrrrri dataclassesr r1torch.fx torch.fx.noder torch.fx._compatibilityr __all__r2TensorsTensorOrTensorsrZr]r\rKrNamesr'r nnModuler boolrrr[rr r r r!s2$       (#