o H h+@s\ddlZddlZddlmZddlmZddlmZmZm Z ddl Z ddl Z ddl m Z ddlmZgdZe ee jee jfZe e jefZee jjZee jjZeeZhdZe d d d d Ze d d d eee jj fde jjdefddZ!e d d de jjde"fddZ#e d d GdddZ$e d d de jj%de jj%fddZ&dS)N)Mapping) dataclass)AnyOptionalUnion) compatibility)_get_qualified_name)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)kmodulerP/var/www/vscode/kcb/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)rr submod submod_typer&rrrr +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_rrrr Rsr c@sxeZdZdZdejjdefddZe GdddZ dd d d e ee fd e efd dZdeejjeffddZdS)r z 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)rlistgraphnodesr1)selfrr1rrr__init__es zFxNetAccFusionsFinder.__init__c@s6eZdZUeed<eed<eed<eed<ddZdS)!FxNetAccFusionsFinder.FusionGroup top_node_idxr5inputsnodes_need_processcsR|jvrdSj|j|j|jfdd|jDdS)z5 Add a node to fusion group. Ncs$h|]}|jtvr|jvr|qSr)r#r$r5).0nr6rr s z=FxNetAccFusionsFinder.FusionGroup.add_node..)r5r;addr:discardupdateall_input_nodes)r6r rr>radd_nodexs     z*FxNetAccFusionsFinder.FusionGroup.add_nodeN)rr __qualname__int__annotations__NodeSetrDrrrr FusionGroupjs  rIN fusion_groupr8r:visitedcCsz|D]8}|dur||vr q|||jtvrq|j||jkr"q||jvr*dS|||j|r:||dSqdS)z Start from inputs and going reverse topological order. If any upstream node is in the fusion group, add all the nodes in this path to fusion group. NTF) r@r#r$r5indexr9recursive_add_noderCrD)r6rJr:rKargrrrrMs      z(FxNetAccFusionsFinder.recursive_add_noder!cCsi}t|j}|D]}||vrq |jtvrq d|jvrq ||jvr"q |j|j||ht|j |hd}|j r|j }|j ||j tdd|jvrl|jD]}|jtvrVqN||jvr\qN|||j ||j tdqN|j D].}|jtvrwqod|jvr}qo||jvrqo||t|j|j||_|j ||j tdqo|j s7t|j|jks|j|j8_q |jD]}|j||<qq |S)N tensor_meta)r9r5r:r;)rK)r3r1r#r$r+rIr5rLsetrCr;poprMr:usersrDminr9)r6resultr1r rJuserrNr=rrr__call__sv                *  zFxNetAccFusionsFinder.__call__r2)rrrE__doc__r.fx GraphModulerHr7rrIrNodeListrrMdictNoderVrrrrr ^s#  &r gmcstjtjtjtjtjtjtjtjtj tj tj tj t jjjjt jjjjt jjjjt jjjjt jjjjg}t|jjdt j}|jjD]}|jD] }|d7<qIqDt}|jjD] }|dkrj| |q]it!|dkr|"}|#|fdd|<|jD]%}|d8<|dkr|j$dkr|j%|vr|&|q| |qt!|dksst!|jt!|jjkrt'dfddD|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 rcs|Sr2r)x)envrr/sz legalize_graph..rz&Input graph has cycles, unable to add csg|] }|dkr|qS)rr)r<r )indegrr ;sz"legalize_graph..))operatorr@mulsubfloordivtruedivmodleltgegteqner.opsatensym_constrain_rangedefaultsym_constrain_range_for_size _assert_asyncmsg scalar_tensor_assert_scalarr[fromkeysr4r5rXGraphrR collectionsdequeappendlenpopleft node_copyr#r& appendleft RuntimeError_codegen)r]PRIORITIZED_OPS new_graphr rUqueuecurr)r`rbrr s`                   r )'r{rdcollections.abcr dataclassesrtypingrrrr.torch.fxtorch.fx._compatibilityr torch.fx.noder__all__tupler/r3TensorsTensorOrTensorsrXr\rZrPrHrNamesr$r nnModuler boolr r rYr rrrrsB         &