o h@sUddlZddlZddlZddlZddlZddlmZmZddlm Z m Z m Z m Z m Z mZddlZddlmZmZmZddlmZmZmZddlmZdd lmZe rYdd lmZgd Zee Z!ee"e#e$e%e&ej'ej(ej)ej*ej+ejj,ej-ej.ej/fZ0e0j1Z2ee d e fe"fZ3e ee4d edee"dfe5e6de0fZ7e de7dZ8e9:gdZ;ejj?ej>j@gZAeBe eCd<ejDejEejFjEjGejFjHjIejFjJjIejFjKjIejFjLjIejMjNejMjOejMjPejQjRjIejSh eTeABZUeTe eCd<eVejQdreUWejQjXjIeddde de fddZYde d e fde"fddZZde[de"fd d!Z\d"e d e fde"fd#d$Z]e$d%fd&e[d'e$de"fd(d)Z^ed*dGd+ddeZ_ed*dd,e8de e_ge7fde8fd-d.Z`ed*dd,e8de e7ge7fde8fd/d0ZadS)1N)MappingSequence)AnyCallableOptional TYPE_CHECKINGTypeVarUnion)_fx_map_aggregate _fx_map_arg _NodeBase)ArgsKwargsPairnormalize_functionnormalize_module)ops) compatibility)Graph)Nodemap_arg map_aggregatehas_side_effect.Argument.rr ArgumentT)bound) placeholder call_method call_module call_functionget_attroutputroot1_side_effectful_need_to_be_preserved_pre_dispatch_side_effectful_functionsresize_storage_bytes_Fis_backward_compatiblefnreturncCst||SN)r%add)r)r-a/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/fx/node.pyrhs r orig_methodcCsR|j}|j}|dur |SttjjfD]}t||d|ur!|jSqtd|)Nzcannot find module for )__name__ __module__torchnn functionalgetattr RuntimeError)r/namemoduleguessr-r-r._find_module_of_methodos r:objcCs^t|trt|tjs|jdkr|jS|jd|jS|dur"dSt|tjr+|jSt|S)a:Return the repr() of an object, special-casing types (internal helper). If obj is a type, we return a shorter version than the default type.__repr__, based on the module and qualified name, which is typically enough to uniquely identify a type. For everything else, we fall back on repr(obj). builtins..z...) isinstancetypetypes GenericAliasr1 __qualname__ FunctionTyper0repr)r;r-r-r. _type_repr|s  rEfuncc Cstt|jd|ur |jSt|tjtjfr%|ttj|jdur%d|jS|j}|dkrLzt | dd }Wnt yK}ztd|d}~wwt|}|dd}|dkrb|d krbd |}|d |S) Nz torch.Tensor.z=rzUnable to represent lambdaz torch._opsz torch.opsr2segment_reduce_r=)r5r<r0r>r@MethodDescriptorTypeWrapperDescriptorTyper2Tensorinspect getsourcesplitstrip Exceptionr6r:replace)rFr7er8r-r-r._get_qualified_names,   rTinfarg max_list_lencs*t|dr |St|tr6dfddt|D}t|dkr&dndt|d}d ||dSt|trodfd dt|D}t|dkrSdndt|d}t|dkrcd nd}d |||d St|trddd| D}d|dSt|t rdt |St |S)N_custom_fx_repr_fnz, c3$|] \}}|krt|VqdSr+ _format_arg.0idxarWr-r. z_format_arg..rz, ...[total_len=][c3rYr+rZr\r`r-r.rarb,()css&|]\}}|dt|VqdS): NrZ)r]kvr-r-r.ras${}%) hasattrrXr>listjoin enumeratelentupledictitemsrstr)rVrWrv maybe_len maybe_comma items_strr-r`r.r[s.  " "    r[TcseZdZUdZeded<eedfed<ded<eed<eed <d ed <ed ed <ed ed<ee ed<e ed<ee dgefed<eee fed<e dd dpddded ed d deddeedfdee ddffdd Z deee ffddZ deee fddfdd Zedqd!d"Zedqd#d$Ze dddrd&d'Zd(ddefd)d*Zd(ddefd+d,Zd(ddefd-d.Zd(ddefd/d0Ze dddrd1d2Zdsd3d4Zedeed5ffd6d7Zejd8eed5fddfd9d7Zedeeeffd:d;Zejdd?Ze ddd@e dAeddfdBdCZ!e ddd@e dAeddfdDdEZ"e dddFedAeddfdGdHZ#edeefdIdJZ$e$jdKeeddfdLdJZ$defdMdNZ%e&d e'defdOdPZ(e dd  dtdQeeedReeedeefdSdTZ)e dddUdVfdWdXdYddZe dgefd[ededfd\d]Z*e dWddefd^d_Z+e dWd   Wdud`e,j-j.daeee dbeeee fdcedee/f dddeZ0e dddvdhdiZ1djeddfdkdlZ2dedme ddfdndoZ3Z4S)wrao ``Node`` is the data structure that represents individual operations within a ``Graph``. For the most part, Nodes represent callsites to various entities, such as operators, methods, and Modules (some exceptions include nodes that specify function inputs and outputs). Each ``Node`` has a function specified by its ``op`` property. The ``Node`` semantics for each value of ``op`` are as follows: - ``placeholder`` represents a function input. The ``name`` attribute specifies the name this value will take on. ``target`` is similarly the name of the argument. ``args`` holds either: 1) nothing, or 2) a single argument denoting the default parameter of the function input. ``kwargs`` is don't-care. Placeholders correspond to the function parameters (e.g. ``x``) in the graph printout. - ``get_attr`` retrieves a parameter from the module hierarchy. ``name`` is similarly the name the result of the fetch is assigned to. ``target`` is the fully-qualified name of the parameter's position in the module hierarchy. ``args`` and ``kwargs`` are don't-care - ``call_function`` applies a free function to some values. ``name`` is similarly the name of the value to assign to. ``target`` is the function to be applied. ``args`` and ``kwargs`` represent the arguments to the function, following the Python calling convention - ``call_module`` applies a module in the module hierarchy's ``forward()`` method to given arguments. ``name`` is as previous. ``target`` is the fully-qualified name of the module in the module hierarchy to call. ``args`` and ``kwargs`` represent the arguments to invoke the module on, *excluding the self argument*. - ``call_method`` calls a method on a value. ``name`` is as similar. ``target`` is the string name of the method to apply to the ``self`` argument. ``args`` and ``kwargs`` represent the arguments to invoke the module on, *including the self argument* - ``output`` contains the output of the traced function in its ``args[0]`` attribute. This corresponds to the "return" statement in the Graph printout. r_argsr_kwargsrgraphr7opTargettarget)rN _input_nodesusersr? _sort_key_repr_fnmetaTr'Nargskwargs return_typer*c s|dkrt|std|d|d|dt|d n|tvs#Jt|tsrwsuper__init___update_args_kwargs)selfr}r7r~rrrr __class__r-r.r s%"  "z Node.__init__cCsLi|j|j|j|j|j|j|j|j|j|j|j |j |j |j |j |jdS)N)r}r7r~rr?rr{r|_erased_prev_nextrrrr)__dict__r}r7r~rrr{r|rrrrrrrrr-r-r. __getstate__?s&zNode.__getstate__statecCs"|D] \}}t|||qdSr+)rvsetattr)rrrjrkr-r-r. __setstate__SszNode.__setstate__cC|jS)z Returns the next ``Node`` in the linked list of Nodes. Returns: The next ``Node`` in the linked list of Nodes. )rrr-r-r.nextW z Node.nextcCr)z Returns the previous ``Node`` in the linked list of Nodes. Returns: The previous ``Node`` in the linked list of Nodes. )rrr-r-r.prevbrz Node.prevxcCs|j|jks Jd||krtddS||j}|||_|_|||_|_|jj}|jj}t|t|krS|dt|d^}}g||dR|_dSt|t|krt|dt|d^}}g||dR|_dSg|dR|_dS)aD Insert x before this node in the list of nodes in the graph. 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Assignment to this property is allowed. All accounting of uses and users is updated automatically on assignment. )r{rr-r-r.r z Node.argsr_cCs|||jdS)z Set the tuple of arguments to this Node. The interpretation of arguments depends on the node's opcode. See the ``fx.Graph`` docstring for more information. N)rr|)rr_r-r-r.r cCr)aG The dict of keyword arguments to this ``Node``. The interpretation of arguments depends on the node's opcode. See the :class:`Node` docstring for more information. Assignment to this property is allowed. All accounting of uses and users is updated automatically on assignment. )r|rr-r-r.rrz Node.kwargsrjcCs||j|dS)z Set the dict of kwargs to this Node. The interpretation of arguments depends on the node's opcode. See the ``fx.Graph`` docstring for more information. N)rr{)rrjr-r-r.rrcCst|jS)aE Return all Nodes that are inputs to this Node. This is equivalent to iterating over ``args`` and ``kwargs`` and only collecting the values that are Nodes. Returns: List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this ``Node``, in that order. )rprkeysrr-r-r.all_input_nodes zNode.all_input_nodesr^rVcCs t|j}|||<t||_dS)a3 Update an existing positional argument to contain the new value ``arg``. After calling, ``self.args[idx] == arg``. Args: idx (int): The index into ``self.args`` of the element to update arg (Argument): The new argument value to write into ``args`` N)rprrt)rr^rVrr-r-r. update_args zNode.update_argcCsd|krt|jksJdJd|jd|}|j|d}||f||_i}t||j|D]}||jvrJ|j||j|q7dS)a  Insert an positional argument to the argument list with given index. Args: idx (int): The index of the element in ``self.args`` to be inserted before. arg (Argument): The new argument value to insert into ``args`` rz6insert_args index must be between 0 and len(self.args)N)rsrr{r setdefaultrrr)rr^rV args_left args_right_new_input_nodesnew_user-r-r. insert_args       zNode.insert_argkeycCsi|j||i|_dS)a2 Update an existing keyword argument to contain the new value ``arg``. After calling, ``self.kwargs[key] == arg``. Args: key (str): The key in ``self.kwargs`` of the element to update arg (Argument): The new argument value to write into ``kwargs`` N)r)rrrVr-r-r. update_kwargs zNode.update_kwargcCs|jddS)a Return the Python stack trace that was recorded during tracing, if any. When traced with fx.Tracer, this property is usually populated by `Tracer.create_proxy`. To record stack traces during tracing for debug purposes, set `record_stack_traces = True` on the `Tracer` instance. When traced with dynamo, this property will be populated by default by `OutputGraph.create_proxy`. stack_trace would have the innermost frame at the end of the string. stack_traceN)rgetrr-r-r.rrzNode.stack_tracetracecCs||jd<dS)Nr)r)rrr-r-r.r-scCs|jr||S|jSr+)rr7rr-r-r.__repr__1s z Node.__repr__cCsdt|tr|St|dr.t|dd}|durt|S|jdkr$d|S|jdkr.d|St|S)z Make target printouts more user-friendly. 1) builtins will be printed as `builtins.xyz` 2) operators will be printed as `operator.xyz` 3) other callables will be printed with qualified name, e.g. torch.add r1r0Nr<z builtins. _operatorz operator.)r>rwror5rTr1)rr7r-r-r._pretty_print_target6s       zNode._pretty_print_targetplaceholder_namesmaybe_return_typenamecCs|jdkrat|jts J|j}||jr|dt|jnd7}|r*||dS|jr5t|jdnd}|jrEdt|jddnd}d |jd |d t |j d |jd |jd| S|jdkr|jdurst|jdnd}d |jd |d t |j d |jd | |jd S|jdkr|jr|rdt|j|d<d|jdS|jdurt|jdnd}d |jd |d t |j d |jd | |jdt |jdt |j dS)a Return a descriptive string representation of ``self``. This method can be used with no arguments as a debugging utility. This function is also used internally in the ``__str__`` method of ``Graph``. Together, the strings in ``placeholder_names`` and ``maybe_return_typename`` make up the signature of the autogenerated ``forward`` function in this Graph's surrounding GraphModule. ``placeholder_names`` and ``maybe_return_typename`` should not be used otherwise. Args: placeholder_names: A list that will store formatted strings representing the placeholders in the generated ``forward`` function. Internal use only. maybe_return_typename: A single-element list that will store a formatted string representing the output of the generated ``forward`` function. Internal use only. Returns: str: If 1) we're using ``format_node`` as an internal helper in the ``__str__`` method of ``Graph``, and 2) ``self`` is a placeholder Node, return ``None``. Otherwise, return a descriptive string representation of the current Node. rrircN z (default=rrhrnz : z [num_users=z] = z[target=rdr!r"z -> zreturn z ](args = z , kwargs = )r~r>rrwr?rErrr7rsrrr[r)rrrarg_strmaybe_typename default_valr-r-r. format_nodeOsF ""  4     zNode.format_nodecCsdS)NTr-)userr-r-r.sz Node.F)propagate_meta replace_withdelete_user_cbrc s|rtjdks JdjD] \}}|j|<qtj}gjj}|D]L}||s5|q)dtdtffdd } t |ddrU|j D] } | j |d qJt |j | } t |j| } t| tshJt| tsoJ|| | q)tjtdksJfd d |DS) a Replace all uses of ``self`` in the Graph with the Node ``replace_with``. Args: replace_with (Node): The node to replace all uses of ``self`` with. delete_user_cb (Callable): Callback that is called to determine whether a given user of the self node should be removed. propagate_meta (bool): Whether or not to copy all properties on the .meta field of the original node onto the replacement node. For safety, this is only valid to do if the replacement node doesn't already have an existing .meta field. Returns: The list of Nodes on which this change was made. rzmCalled node.replace_all_uses_with(replace_with, propagate_meta=True), but replace_with already has .meta keysrr*c|krS|Sr+r-r)rrr-r.maybe_replace_nodesz6Node.replace_all_uses_with..maybe_replace_node_replace_hooksNoldnewrcsg|]}|vr|qSr-r-)r]r)skippedr-r. sz.Node.replace_all_uses_with..)rsrrvrprr} owning_modulerrr5rr7r rrr>rtrur) rrrrrjrk to_processmuse_noder replace_hooknew_args new_kwargsr-)rrrr.replace_all_uses_withs0       zNode.replace_all_uses_withcCs|jdvrdS|jdkr*t|jdd}|dur|jrdSt|jddr%dS|jtvS|jdkrS|jjdus9Jd |jj|j}|dusMJd |jt|d dSdS) z Returns whether this op is impure, i.e. if its op is a placeholder or output, or if a call_function or call_module which is impure. Returns: bool: If the op is impure or not. >r"rTr _schemaN_nondeterministic_seededFrz1self.graph.owning_module not set for purity checkz'Did not find expected submodule target _is_impure)r~r5r is_mutabler%r}r get_submodule)rschema target_modr-r-r. is_impures&       zNode.is_impurer# arg_types kwarg_typesnormalize_to_only_use_kwargscCs`|jdkrt|js Jt|j|j|j||S|jdkr.t|jts$Jt||j|j|jSdS)a Returns normalized arguments to Python targets. This means that `args/kwargs` will be matched up to the module/functional's signature and return exclusively kwargs in positional order if `normalize_to_only_use_kwargs` is true. Also populates default values. Does not support positional-only parameters or varargs parameters. Supports module calls. May require `arg_types` and `kwarg_types` in order to disambiguate overloads. Args: root (torch.nn.Module): Module upon which to resolve module targets. arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs. Returns: Returns NamedTuple ArgsKwargsPair, or `None` if not successful. r rN) r~rrrrrr>rwr)rr#rrrr-r-r.normalized_argumentss  zNode.normalized_arguments old_input new_inputcsdtdtffdd }|jj}t|ddr$|jD] }|j|dqt|j|}t|j|}t |t s7Jt |t s>J| ||dS)a Loop through input nodes of ``self``, and replace all instances of ``old_input`` with ``new_input``. Args: old_input (Node): The old input node to be replaced. new_input (Node): The new input node to replace ``old_input``. rr*crr+r-rrrr-r.r+rz3Node.replace_input_with..maybe_replace_noderNr) rr}rr5rr7r rrr>rtrur)rrrrrrrrr-rr.replace_input_withs     zNode.replace_input_with candidatecCs8||jkrdS|jj|d}||_|jj||dSr+)r7r}_graph_namespace create_name_rename_object)rrr7r-r-r._rename9s z Node._renamevaluecCs|dkr-t|dr-|jj}t|ddr-t|tsJ|jD]}|jD] }||||dq"qd}t||rIt|jdrI||jjvrId}|jj |t ||||r[|jj |dSdS)Nr7rrF_find_nodes_lookup_tableT) ror}rr5r>rwrrrremoveobject __setattr__insert)rr7rrrrupdater-r-r.r@s(     zNode.__setattr__r+)r*r)rrr*N)r*N)NN)NNF)rrrrr*N)5r0r1rB__doc__rt__annotations__rurwrrrrrrrpropertyrrrboolrrrrrrrrsetterrrprintrrrrr staticmethodrrrrrr2r3Moduler rrrr __classcell__r-r-rr.rs        4   $            A  :%  , r_cCst|sJdt||S)z Apply fn recursively to each Node appearing in arg. arg may be a list, tuple, slice, or dict with string keys: the return value will have the same type and structure. z.torch.fx.map_arg(a, fn): fn must be a callable)rr r_r)r-r-r.rUs rcCs t||S)z Apply fn recursively to each object appearing in arg. arg may be a list, tuple, slice, or dict with string keys: the return value will have the same type and structure. )r r r-r-r.ras r)br<rMloggingoperatorr@collections.abcrrtypingrrrrrr r2torch._Cr r r torch.fx.operator_schemasr rr_opsr_compatibilityrr}r__all__ getLoggerr0rrwrfloatrcomplexdtyperLdevice memory_formatlayout OpOverloadSymIntSymBoolSymFloatBaseArgumentTypes__args__ base_typesrrtslicerangerrrufromkeysr_C_set_grad_enabledamp_enter_autocast_exit_autocastr$rpr_assert _assert_asyncatenmsg_assert_scalardefault_assert_tensor_metadatasym_constrain_rangesym_constrain_range_for_sizeprofiler_record_function_enter_record_function_enter_new_record_function_exitinductoraccumulate_grad_setitemsetr%ror,r&rr:rrErTr[rrrr-r-r-r.s            " &