Vhw5 dZddlZddlZddlZddlZddlZddlmZmZddl m Z ddl m Z m Z mZmZmZddlZddlZddlmZddlmZedZerd d lmZej6j8ZeeZeeZe e!e!e"e!e!ejFe!e!e!e!f Z$ejJe&Z'ejPjSe&d Z*d e+d dfdZ,dejZd e e egefe e dfffdZ.Gdde/Z0GddejbZ2de d e fdZ3ded e e e+dfe ee dfffdZ4d e$fdZ5GddZ6GddZ7deed e8ed!e eege!fd dfd"Z9y)#a This module provides functionality for tracking and managing regions in computational graphs. It supports graph optimization by identifying and grouping similar regions based on their structure and behavior. The module implements algorithms for: 1. Tracking nodes and their relationships in the computational graph 2. Identifying identical or similar regions across the graph 3. Managing graph regions for optimization purposes 4. Supporting deduplication and other graph transformation passes The core functionality revolves around the GraphRegionTracker class which maintains mappings between nodes and their duplicates, enabling efficient graph analysis and optimization operations. N) defaultdictdeque)fields)AnyCallableOptional TYPE_CHECKINGTypeVar) FakeTensor) tree_flattenT)InstructionTranslatorBasegraph_region_expansionmsgreturnc0tj|g|yN)graph_expansion_logdebug)rargss R/home/dcms/DCMS/lib/python3.12/site-packages/torch/_dynamo/graph_region_tracker.py debug_logr1sc)D)x.cddlm}m}g}||}t|D]'}|j t ||j )|t|fS)Nr)_ident%extract_tensor_metadata_for_cache_key)torch._inductor.codecacherrrappendgetattrnametuple)rrroutmetadatafields r&_extract_tensor_metadata_for_node_hashr'5sSX C4Q7H!2 78UZZ012 E#J rc eZdZy)NodeHashExceptionN)__name__ __module__ __qualname__rrr)r)Bsrr)c0eZdZdfd ZdedefdZxZS) InputPicklerrc xddlmtj}||_t ||tjj|_|jjtttjfdtjfdtj fdid|_y)Nr)rc t|ffSrstrrrs rz'InputPickler.__init__..Qs#a&(;rc t|ffSrr2r4s rr5z'InputPickler.__init__..Rs&3q6))<rc t|ffSrr2r4s rr5z'InputPickler.__init__..Ss6CF9*=rT)rrioBytesIO_streamsuper__init__copyregdispatch_tablecopyupdater r'torchSymIntSymBoolSymFloatfast)selfstreamr __class__s @rr<zInputPickler.__init__Gs4   %4499; ""B ; < =    robjcx |j||jj|jjd|jj dS#t t f$r }t|d}~wwxYw#|jjd|jj dwxYw)z< Pickle an object and return a byte string. rN)dumpr:getvalueseektruncate TypeErrorAttributeErrorr))rFrIes rdumpszInputPickler.dumpsXs % IIcN<<((* LL  a LL ! !! $ >* +# * + LL  a LL ! !! $s#*A##A>2A99A>>B8B9rN)r*r+r,r<rbytesrR __classcell__)rHs@rr/r/Fs" % % %rr/argcZt|tr|jjdSy)N example_value) isinstanceNodemetaget)rVs r_extract_tensor_argr]fs"#txx||O,,rnodec$t|j\}}t|jj d}t t|jj }t|\}}||z}|t d|DfS)Nc |dS)Nrr-)rs rr5z!_normalize_args..qs adrkeyc32K|]}t|ywr)r]).0rVs r z"_normalize_args..usLC237Ls)r rsortedkwargsitemsr#keys)r^ flat_args_ sorted_kwargs sorted_keys flat_kwargsall_argss r_normalize_argsrpmsy  *LIq4;;,,.NCMt{{//123K!-0NK;&H L8LL MMrc tjtjtjtjj tjj tjtjtjjtjtjjjf Sr) rAis_grad_enabledis_inference_mode_enabledget_num_threads_C2_get_cublas_allow_fp16_reduced_precision_reduction2_get_cublas_allow_bf16_reduced_precision_reductionget_default_dtype$are_deterministic_algorithms_enabled_get_cublas_allow_tf32-is_deterministic_algorithms_warn_only_enabled _autograd_saved_tensors_hooks_is_enabledr-rrget_global_state_keyr~xs  '')  CCE CCE ! 224 '') ;;= ::<  rczeZdZdeddfdZededdfdZdeefdZdeefdZ deddfd Z d eddfd Z y) BackwardBfsArgIteroriginrNc0||_t|_yr)_curr_queue)rFrs rr<zBackwardBfsArgIter.__init__s$* -2W rc>t|}|j||Sr)r add_children)rits rcreatezBackwardBfsArgIter.creates  '  rc|j}|js d|_|S|jj|_|Sr)rrpopleft)rFrets rnextzBackwardBfsArgIter.nexts;ii{{DI  ++-DI rc|jSr)rrFs rpeekzBackwardBfsArgIter.peeks yyrr^ct|j\}}|D]$}t|ts|j |&t|j \}}|D]$}t|ts|j |&yr)r rrYrZ_appendrg)rFr^rjrkrVrnkwargs rrzBackwardBfsArgIter.add_childrensr#DII. 1 "C#t$ S! "&dkk2 Q  $E%& U# $rrVcb|j||_y|jj|yr)rrr )rFrVs rrzBackwardBfsArgIter._appends% 99 DI KK  s #r) r*r+r,rZr< staticmethodrrrrrrr-rrrrs~5t55t 4 htnhtn $ $$ $$4$D$rrc eZdZdZddZdededeededef d Z d ed ede fd Z d ddeddfdZ de jjdeeefdZdefdZy)GraphRegionTrackera GraphRegionTracker tracks each node added to the output graph and generates a key based on the source location, instruction pointer, input shapes, and global state at the time the node is inserted into the graph. Nodes with the same key are grouped together in a list of identical nodes (the value of node_to_duplicates). hash_to_duplicates: Dict[str, IdenticalNodes] - A dictionary mapping the key to a list of identical nodes node_to_duplicates: Dict[Node, IdenticalNodes] - A dictionary mapping a node to the list of identical nodes it belongs to input_pickler: InputPickler - An instance of InputPickler used to generate a node hash rNcXtt|_i|_t |_yr)rlisthash_to_duplicatesnode_to_duplicatesr/ input_picklerrs rr<zGraphRegionTracker.__init__s!=H=N>@)^rfilenamelinenoinstruction_pointerr^cddlm}t|||t|f}||jj |S)Nr) sha256_hash)rrr~rprrR)rFrrrr^rrbs r _hash_nodezGraphRegionTracker._hash_nodesE : ! "    D !  4--33C899rn0n1c||jvxr4||jvxr$|j||j|uxr||uSr)r)rFrrs r _is_identicalz GraphRegionTracker._is_identicalsY $)) ) d--- ''+t/F/Fr/JJ "  rtxrc2 |j|j|jj|j|j |}|j |||j|<y#t$r!}tjd||Yd}~yd}~wwxYw)z The main entry point for tracking a node. This function will hash the node argument and group nodes with the same hash together. It updates the hash_to_duplicates and node_to_duplicates dictionaries to track the new node. z(Unable to hash node %s with exception %sN) rrf_code co_filenamerrr rr)logr)rFrr^ duplicatesrQs r track_nodezGraphRegionTracker.track_nodes K00II))299b6L6LdJ   d #,6D # #D )  K II@$ J J KsA)A,, B5BBgraphc t|jDcic]\}}|| c}} g}|jjD]r}t |dkDsg}t j }|D](}| vst| |}|j|g*t |dkDs`|j||ft|jd|D cgc]\}} | } }} t} | D]5}t|| |j|D]} | j fd7| Dcgc]}t |ddkDs|c}Scc}}wcc} }wcc}w)a This function is responsible for extracting the largest regions of identical nodes from the given graph. **Note**: This function assumes the nodes that have been tracked with track_node are in the provided graph argument. The algorithm proceeds as follows: The nodes tracked via track_node above are organized into region groups. The initial region groups look like this: [[IdenticalNode1], [IdenticalNode2], [IdenticalNode3]] and each sublist is called a region. For each region group (starting at the topologically latest region group), the inner regions are gradually expanded one node at time from the flattened args and kwargs of the node in each region provided that for all regions in the group, the nodes being added are also identical (ie have the same key computed by track_node). This is checked by verifying that the two nodes have the same identical node list in node_to_duplicates. rc|d S)Nrr-)rgs rr5z:GraphRegionTracker.get_identical_regions..s RUFrrac|Srr-)ntopological_rankings rr5z:GraphRegionTracker.get_identical_regions..s *=a*@rr) enumeratenodesrvalueslenmathinfminr sortsetfully_expand_region_groupr)rFrir^region_groups_with_rankgroup region_groupmin_rankrrk region_groups seen_nodesregionrs @rget_identical_regionsz(GraphRegionTracker.get_identical_regionssy7@ 6LM71dtQwM"$,,335 ME5zA~! 88!4D22#&x1DT1J#K$++TF3 4 |$q(+22L(3KL M $$):$;)@AAA A !$ ) BL %lJ@R@R S& B @ A B B.; )c,q/>RUV>VL  GN.B s E  E+EEc<d|jd|jdS)Nz&GraphRegionTracker(hash_to_duplicates=z, node_to_duplicates=))rrrs r__str__zGraphRegionTracker.__str__"s.78O8O7PPefjf}f}e~~A ArrS)r*r+r,__doc__r<r3intrrZrboolrrrAfxGraphrRegionrrr-rrrrs, : :%( :?G} :TX :  :  $ 4 K8KKK"2 588>>2 d4<>P2 hAArrregionsris_identical_fnc tdtd|td|DsJg}|D]*}|\}|jtj |,g}|D]'}|j }|sJ|j |)|dj } | J| rd} |j|j| t|} |ddD]}|j }tdtd|| td ||vtd ||r_td ||| | ||vxr || vxr|jd k7xr ||| z} |j|| j|nd } td| rt|||D]}\}}}|j|td|td|jt|jj|j ||j||dj } | r|D]}|j!td|tdy)Nz2--------------------------------------------------zexpanding new region group: %sc38K|]}t|dk(yw)rN)r)rdrs rrez,fully_expand_region_group../s6Fs6{a6srTrz--------------------z$considering adding: %s, cur_node: %szpreviously claimed nodes: %sz%szis_identical: %s placeholderFzadding %s's childrenz%s %szend expand new region group: %s)rallr rrrrclearropaddziprrrgrhreverse) rrr region_itersrr nodes_to_add region_itr^ current_nodeadd_nodenodes_to_add_sets rrr&s BC .8 6g6 66 6L? .55f=>? 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