+ QV-j1PRt^RIHt^RIt^RIHt^RIHt^RIH t ^RI H t ^RI H t Ht] 'd ^RIHt^R IHt/t]]!RR 7!R R 444t!R R4t]!R4tRRRlltRRltRRRllt]P4!4tRRltRRR/RlltR#)z Contains the logic for automatic additional output capture with our forward decorators. This mostly describe the hooks used and the logic to make capture thread/context safe. ) annotationsN) ContextVar) dataclasswraps) TYPE_CHECKING)is_torchdynamo_compilingrequires)nnPreTrainedModel)backendsc^]tRt^'t$RtR]R&^tR]R&RtR]R&RtR]R &R t R ]R &R t R#)OutputRecorderar Configuration for recording outputs from a model via hooks. Attributes: target_class (Type): The class (e.g., nn.Module) to which the hook will be attached. index (Optional[int]): If the output is a tuple/list, optionally record only at a specific index. layer_name (Optional[str]): Name of the submodule to target (if needed), e.g., "transformer.layer.3.attn". class_name (Optional[str]): Name of the class to which the hook will be attached. Could be the suffix of class name in some cases. capture_initial_hidden_state (bool): Whether to prepend the first module's input as the initial hidden state. ztype[nn.Module] target_classintindexN str | None layer_name class_nameTboolcapture_initial_hidden_state) __name__ __module__ __qualname____firstlineno____doc____annotations__rrrr__static_attributes__rt/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/utils/output_capturing.pyrr's7 "!E3N!J !!J !)- $-r rc2]tRt^>>?? "##'') )r c v\4'dWnRVnR#VPP V4#)TN)rr'r(r&set)r)values&&r!r2CompileableContextVar.setPs1 # % %#O!DN##''. .r c VP'gVfRVnRVnR#VPPV4R#)NF)r(r'r&reset)r)tokens&&r!r6CompileableContextVar.resetXs1 >>>U]"DO"DN    " "5 )r )r(r&r'N) rrrrrr+r/r2r6rrr r!r#r#<s */*r r#output_collectorTc ,V^8dQhRRRRRRRRR R /#) module nn.ModulekeystrrrrrreturnNoner)formats"r! __annotate__rCds488 88(+8KO8 8r c>aaaVVV3RlpVPV4R#)zaInstall the forward hook needed to capture the output described by `key` and `index` in `module`.c<\P4pVeSVP49dR#S'd>SR8Xd7\VS,4^8Xd VS,P V^,4\ V\ 4'gVS,P V4R#VS,e"VS,P VS,4R#R#)N hidden_states)_active_collectorr/keyslenappend isinstancetuple)r<argsoutputcollected_outputsrrr>s&&& r!output_capturing_hook;install_output_capuring_hook..output_capturing_hookis-113  $3D3I3I3K(K  'C?,BsK\]`KaGbfgGg c " ) )$q' 2&%(( c " ) )& 1 E] & c " ) )&- 8'r N)register_forward_hook)r<r>rrrPs&fff r!install_output_capuring_hookrSds 9   !67r c(V^8dQhRRRRRRRR/#) r; parent_moduler= module_namer? capture_tasksz list[tuple[str, OutputRecorder]]r@rAr)rBs"r!rCrCzs2nnn+.n?_n nr c^RIHpVP4F:wrE\WS4'g\ WQ RV 2V4K)\ WQ RV 2R7K< VFwrgVP e\WP 4'g4VPfK>VPVP4'gKaVPeVPV9dK\WVPVP4K R#)a Recursively install all output capturing hooks on all submodules of `parent_module`. Note that we need to use this recursive approach instead of simply iterating over all modules, because we want to respect the `capture_tasks` of all individual submodels (`PreTrainedModel` instances) in the graph. That is, once we reach a submodel in the graph, its children should use this submodel's `capture_tasks`, but other parts of the graph should not. r .)prefixN) modeling_utilsr named_childrenrKrecursively_install_hooks"install_all_output_capturing_hooksrrendswithrrSrr)rUrVrWr r*r<r>specss&&& r!r]r]zs1&446 &22 %f Qtf.E} U /v Qtf>U V 7$     *z-I[I[/\/\    ([-A-A%BRBR-S-S+0@0@ 0S (U[[%JlJl m$r c$V^8dQhRRRRRR/#)r;modelr rZrr@rAr)rBs"r!rCrCs">>o>z>]a>r c\P\VP44;'g/p.pVP 4FwrE\ V\ 4'gV.pVFwp\ V\4'gMRV9d^M^p\ V\4'gRMTp\ V\4'gTMRp \WVR7pVPWF34Ky K VeTMRp\WV4\VRR4R#)z Install the output recording hooks on all the modules in `model`. This will take care of correctly dispatching the `_can_record_outputs` property of each individual submodels in case of composite models. rFN)rrr!_output_capturing_hooks_installedT) _CAN_RECORD_REGISTRYr/r? __class__itemsrKlistrrJr]setattr) rbrZ capture_flagsrWr> layer_specsr`rrrs && r!r^r^s ),,S-ABHHbMM)//1+t,,&-K Ee^44,3)3E3)?)?TU ,6uc,B,Bu &LZde  # . !2)VrFe]; E6=r c V^8dQhRRRR/#)r;rbr r@rAr)rBs"r!rCrCs222T2r c\VRR4'dR#\;_uu_4\VRR4'd RRR4R#\V4RRR4R# +'giR#;i)z Check if the model already has output capturing hooks installed, and install them if it is not already the case. Note that this is thread-safe, in case 2 (or more) threads want to install them concurrently. reFN)getattr_hook_installation_lockr^)rbs&r!maybe_install_capturing_hooksrqsSu95AA  5=u E E  ! +51 ! sA A A) tie_last_hidden_statesc,aV3RlpVe V!V4#V#)a Decorator to intercept specific layer outputs through hooks. The hooks are installed only once and lazily, the first time output capture is requested with the `output_xxx` kwargs/config. The implementation is fully context/thread safe, except when using `torch.compile`, as dynamo is unable to trace through `ContextVar` methods. Args: tie_last_hidden_states (`bool`, *optional*, defaults to `True`): Whether to overwrite `out.hidden_states[-1]` with the `out.last_hidden_state`. This is true for all language models and should be toggled off only if `out.hidden_states[-1]` has to be the hidden state before last layer norm, which is needed for some vision models (e.g. CLIP, SigLIP) c4<a\S4VV3Rl4pV#)c 8<VPR\VPRR44p\P \ VP 44;'g/pVUu/uF4pRV 2VP RV 2\VPRV 2R44bK6 ppRV9d+VP R\VPRR44VR&RV9d+VP R\VPRR44VR&VP4UUu/uF"wrWV'gKVPRR 4.bK$ ppp\V4^8d \V4\PV4p S !V.VO5/VBp \PV 4VEFKp V R 8XdS 'gM\V R 4'd0W,R RW&W,PV P 4M@\V R 4'd/W,R RW&W,PV P"4\%W,4W&KV R8Xd\'WK,\(4'dV\WK,4^8Xd@\%W,R,4W&\%W,R,4V RV ,&EK"\%W,4W&EK8\%W,4W&EKN VRJdV P+4p V #uupiuuppi \PT 4i;i) return_dictToutput_Fcross_attentionsoutput_attentionsoutput_cross_attentionsmask_decoder_attentionsoutput_mask_decoder_attentionsrdrFvision_hidden_statesNlast_hidden_state attentions:Nr;:Nr;cross_)poproconfigrfr/r?rgrhreplacerIrqrGr2r6hasattrrJr}r~rLrKrito_tuple)r)rMkwargsrvcapturable_flagskrecordable_keysvrO output_tokenoutputsr>funcrrs&*, r!wrapper4capture_outputs..wrapped_fn..wrappers!**]GDKKX\4]^K 477DNN8KLRRPR *)A! vzzGA3-PWXYWZm]b9cdd)  "%55=CZZ'>QSX)Y> 9:),<QSX)YE @AKZJ_J_Ja gJa$!ef!=9b!92!=Ja  g$%)-d3,001BCL 6t5d5f5"'' 5)/)1 *@AA1B1G1L).).55g6R6RS *=>>1B1G1L).).55g6O6OP#():)?#@GLL(!"2"7>>3GWG\C]abCb',->-CD-I'J 278I8Nt8T2U3/',->-C'D #():)?#@GL)),e#!**,No!h"'' 5s:K7 K<!K<, LLr)rrrrsf r! wrapped_fn#capture_outputs..wrapped_fns! t=  = ~r r)rrrrs&d r!capture_outputsrs#AF $ r )torch)Tr.)r __future__r threading contextvarsr dataclassesr functoolsrtypingr import_utilsrr rr r[r rfrr#rGrSr]r^Lockrprqrrr r!rs #"! <0  :.. .&!*!*J**<=8,n@>:$..*2&TTr