rh[dZddlZddlZddlZddlZddlZddlmZmZm Z ddl m Z m Z ddl mZmZddlmZmZmZddlmZddlmZmZdd lmZmZmZmZmZddlZ dd l!m"Z"d d l#m$Z$d dl%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,iZ-e$j\e/Z0e*r ddl1Z1d dl2m3Z3Gdde4Z5dZ6dZ7dZ8dZ9dZ:dZ;dZdZ?dZ@dZAdZBdZCd ZDd!ZEd"ZFd#ZGd$ZHd%ZId&ZJd'ZKGd(d)eZLe*rddlMm#cmNZOd*eLd+ePeQed,ffd-ZR ded.e ed/d,d+eLfd0ZSe"je&e"jd1k\r8eOjeLeReeSeL2eLjd3eLj^4neOjeLeReeSeL2Gd5d6eXeZYGd7d8eYZZGd9d:eYZ[Gd;d<Z\d=Z]d>Z^dfd?e d@eXdAeXfdBZ_edgdDe`fdEZadedFZbdGZcdedHZddIZedJZfdKZgdLZhdMZidedNeeQfdOZjGdPdQedCRZkdSeleXefd+e`fdTZmdUeXd+e`fdVZndWdXdYeXdZefd[ZodWdXdYeXfd\Zpd]Zqee,d^_Gd`daZrdbZsGdcdde Zty)hz Generic utilities N) OrderedDictUserDict defaultdict)IterableMutableMapping) ExitStackcontextmanager) dataclassfields is_dataclass)Enum)partialwraps)AnyCallableContextManagerOptional TypedDict)version)logging)get_torch_versionis_flax_availableis_mlx_availableis_tf_availableis_torch_availableis_torch_fx_proxyrequires)model_addition_debugger_contextceZdZdZddZy)cached_propertyz Descriptor that mimics @property but caches output in member variable. 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This decorator ensures that only the keyword arguments that match the function's signature, or are specified in the `extra` list, are passed to the function. Any additional keyword arguments are filtered out and a warning is issued. Parameters: extra (`Optional[list]`, *optional*): A list of extra keyword argument names that are allowed even if they are not in the function's signature. Returns: Callable: A decorator that wraps the function and filters out invalid keyword arguments. Example usage: ```python @filter_out_non_signature_kwargs(extra=["allowed_extra_arg"]) def my_function(arg1, arg2, **kwargs): print(arg1, arg2, kwargs) my_function(arg1=1, arg2=2, allowed_extra_arg=3, invalid_arg=4) # This will print: 1 2 {"allowed_extra_arg": 3} # And issue a warning: "The following named arguments are not valid for `my_function` and were ignored: 'invalid_arg'" ``` ctj}t|jj }|j d|vd|vd_tfd}|S)Nr*rTci}i}|jD]\}}| vr|||<|||<|r|Dcgc]}d|d }}dj|} r|djjdz}n r|djdz}nd}t j d|jd|t d |i|Scc}w) N'z, rrrNz1The following named arguments are not valid for `z` and were ignored: r) stacklevel)rjoinrr'warningswarn UserWarning) rr valid_kwargsinvalid_kwargsrrinvalid_kwargs_names cls_prefixfuncis_class_methodis_instance_methodvalid_kwargs_to_passs r/wrapperzCfilter_out_non_signature_kwargs..decorator..wrapper3sLN  *1,,&'LO()N1%  * :H'IQ!A3a'I$'I'+yy1E'F$&!%a!2!2!;!;c!AJ$!%a!1!1C!7J!#J G |TXTaTaSbc**>)?A .. .%(Js B?)r6r7setr;runion _filter_out_non_signature_kwargsr)rsigfunction_named_argsrrrrextra_params_to_passs` @@@r/ decoratorz2filter_out_non_signature_kwargs..decorator's}%!#.."5"5"782889MN$'::#6615- t /  />r1)r)rrrs @r/filter_out_non_signature_kwargsr s&6 KREu:,\ r1ceZdZUdZeded<eeed<eeed<eeed<eded<eded <eeed <eeed <y ) TransformersKwargsav Keyword arguments to be passed to the loss function Attributes: num_items_in_batch (`Optional[torch.Tensor]`, *optional*): Number of items in the batch. It is recommended to pass it when you are doing gradient accumulation. output_hidden_states (`Optional[bool]`, *optional*): Most of the models support outputing all hidden states computed during the forward pass. output_attentions (`Optional[bool]`, *optional*): Turn this on to return the intermediary attention scores. output_router_logits (`Optional[bool]`, *optional*): For MoE models, this allows returning the router logits to compute the loss. cumulative_seqlens_q (`torch.LongTensor`, *optional*) Gets cumulative sequence length for query state. cumulative_seqlens_k (`torch.LongTensor`, *optional*) Gets cumulative sequence length for key state. max_length_q (`int`, *optional*): Maximum sequence length for query state. max_length_k (`int`, *optional*): Maximum sequence length for key state. z torch.Tensornum_items_in_batchoutput_hidden_statesoutput_attentionsoutput_router_logitsztorch.LongTensorcumulative_seqlens_qcumulative_seqlens_k max_length_q max_length_kN)r'r3r4r5r__annotations__boolrr6r1r/rrXs^.!00"4.(~%"4.("#566"#5663-3-r1r)total config_dictc d|vS)z3Checks whether a config dict is a timm config dict.pretrained_cfgr6)rs r/is_timm_config_dictrzs { **r1pretrained_model_pathc|yt|}tjj|}tjj |}|rE|j dr4t |5}tj|}dddt|S|rtjjtjj|drRt tjj|d5}tj|}dddt|Sy#1swYtSxYw#1swYtSxYw)zA Checks whether a checkpoint is a timm model checkpoint. NFz.jsonz config.json) rNospathisfileisdirendswithopenjsonloadrexistsr)ris_fileis_dirr?rs r/is_timm_local_checkpointrs$ 56ggnn23G WW]]0 1F(11': ' ( 'A))A,K '";//"''...C]!ST "'',,4mD E '))A,K '";//  '";// '";//s+D&<D<&D9<Eruztorch.nn.Modulerrcbt||||jD]}t|||y)z5 Set a value to a module and all submodules. N)r)childrenset_attribute_for_modules)rurr submodules r/rrs2 FC__&9 !)S%89r1cvt||r t|||jD]}t||y)z: Delete a value from a module and all submodules. N)r{delattrrdel_attribute_from_modules)rurrs r/rrs8 vs__&3 "9c23r1c.tfd}|S)z Decorator to wrap model method, to call output.to_tuple() if return_dict=False passed as a kwarg or use_return_dict=False is set in the config. Note: output.to_tuple() convert output to tuple skipping all `None` values. ct|dr|jjnd}|jd|}||}|g|i|}|s t |t s|j }|S)NconfigT return_dict)r{rrrrdrr)r*rrrreturn_dict_passedrrs r/rz!can_return_tuple..wrappersj18x1Hdkk--d #ZZ {C  ),Kd,T,V,:fe#<__&F r1rrrs` r/can_return_tuplers" 4[ Nr1)ro)backendscXeZdZUdZded<dZeeed<dZee ed<dZ ee ed<y) OutputRecordera 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. ztype[torch.nn.Module] target_classrindexN layer_name class_name) r'r3r4r5rrrrrrNrr6r1r/rrs9*)E8C= $J $ $J $r1rc.tfd}|S)z Decorator to intercept specific layer outputs without using hooks. Compatible with torch.compile (Dynamo tracing). c|jd}|t|jdd}|jdd}|t|jdd}t|ddr%|jr|rt j dd}||d<|j}d|vr |djD] \}}|||< tjt|ji}|Dcic]X}d||jd|t|jd||jd t|jd dZ} }ttg} fd } t| jrlg} |jD]\} }| jd| dst!|t"s|g}|D]c}t!|t$s>d | vrd nd }t!|tsdn|}t!|ts|nd}t%|||}| j'| |fe|j)D]\}}| D]\} }|j*t!||j*s)|j,5|j/|j,sQ|j0|j0|vrl|j2}| ||| |j4|_| j'||f|g|i|}| D] \}}||_D]} | d k(r]| dd| <t7|dr| xx|j8fz cc<n$t7|dr| xx|j:fz cc<| || <e| dk(rMt!|| t"r1t=|| dk(r | d dd|| <| d dd|d| z<| || <| || <|dur|j?}|Scc}w)N use_cacheFrTgradient_checkpointingzX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.routput_rc>tfd}|S)Ncdk(r"tdk(rxx|dfz cc<|jddr>t|jdd|jd5|i|}dddn|i|}ttsxx|fz cc<|S| vr |f<|Sxx|fz cc<|S#1swYPxYw)N hidden_statesrdebug_ioF debug_io_dirz ~/model_debug prune_layers)rgetr rdr)rrrcollected_outputsrrru orig_forwards r/wrapped_forwardzZcheck_model_inputs..wrapper..make_capture_wrapper..wrapped_forward s/)c2CC2H.IQ.N%c*tAwj8*::j%08 >? KVZZXfMg?".t!>v!>?? *4:6:F!&%0%c*vi7*  E]."3328-1A)#. *#.6%=2BB. ??s ' CCr)rurrrrrs```` r/make_capture_wrapperzAcheck_model_inputs..wrapper..make_capture_wrappers" <  ! &# "r1rrr)rrrvision_hidden_stateslast_hidden_state attentionsrcross_) rr(rrtraininglogger warning_oncecopyr_CAN_RECORD_REGISTRYrNrrranyrbrdrrrZ named_modulesrrrrr9rr{rrrr)r*rrrrall_argsrr capture_flagsrecordable_keysmonkey_patched_layersr capture_tasksr layer_specsspecsrrrrruoriginal_forwardoutputsrrs @r/rz#check_model_inputs..wrappersTJJ{+   [%@Ijj5  !$++}dCK 415 9dmmPY   j I'{;;= x  *002 1  -00T^^1DbI #  aSM8<<! KKaSMLL!4gdkkK^`e6fg     (. 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F&E$ A$I& <# ;"J4T3+T3n//:k:eDIG^<^6_: 9 9(9  9w}}&()]W]]5-AA* **  ! + E$/$:$:#;1[=Q=QL^ % D+T#s(^++ CD89&79c9#9 3'8 3s 32  :%% %"zz&!~&!r1