Ei_nSSKJr SSKrSSKJrJrJr SSKrSSKJ r SSKJ r SSK J r \(a SSK JrJrJr SSjrSSjrSS jrSS jrSS jrS S!S jjrS"S jrS S#SjjrS$S%SjjrS&SjrS'Sjr"SS\ R85r"SS\ R85rS S(SjjrS S)Sjjr S*Sjr!S+Sjr"S,S-Sjjr#S,S.Sjjr$g)/) annotationsN)AnyNoReturn TYPE_CHECKING)Tensor)NamedMemberAccessor)CallableIterableSequencec[S5e)Na$make_functional(module): we don't yet support models that do parameter tying (also sometimes known as weight sharing). Please try to rewrite your model by replacing all instances of the tied parameter with another and/or comment your support in https://github.com/pytorch/functorch/issues/446) RuntimeError]/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/_functorch/make_functional.pyraise_parameter_tying_errorrs  : rc[U5n[U5n[UR55n[UR55nURU5(d[ SUSU35e0nUR 5H upxU/4Xh'M UR 5H/upxX;a[ SUS35eXhSR U5 M1 [UR55$)aC named_params is a dictionary of tensors: {'A': A, 'B': B} tied_named_params is another dictionary of tensors {'A': A, 'B': B, 'B_tied': B} with potentially tied (or 'duplicated') tensors This function creates a mapping from the names in named_params to the names in tied_named_params: {'A': ['A'], 'B': ['B', 'B_tied']}. ztensors_dict_keys z+ is not a subset of tied_tensors_dict_keys ztensor for key 'z ' not found in tensor_to_mapping)dictsetkeysissubsetAssertionErroritemsappendvalues) named_paramstied_named_paramsnamed_params_dicttied_named_params_dicttensors_dict_keystied_tensors_dict_keystensor_to_mappingkeytensors rcreate_names_mapr%$s \*!"34-2245 !7!?  % %&< = = !2 34&&<%= ?  >@(..0 %("I!1-335   * "3%'GH  !!$++C0 6 !((* ++rc6[U"SS95n[U"SS95n[XC5n0n[U5nUH<upX;aU"[R"U SS95Xi'Xin UR X5 M> [ U5S:XaSupO [U6upXU4$)NF)remove_duplicateTmeta)devicerrr)tupler%rtorch empty_like set_tensorlenzip) mod named_memberssubclassall_named_membersunique_named_members names_mapmemoaccessornamep replacementnamesparamss r_extract_membersr>Gs mUCD !EF !5II D"3'H$ =u//&ABDGg D. %   A% v12  ) ##rcJ[XR[R5$)a> This function removes all the Parameters from the model and return them as a tuple as well as their original attribute names. The weights must be re-loaded with `load_weights` before the model can be used again. Note that this function modifies the model in place and after this call, mod.parameters() will be empty. )r>named_parametersnn Parameterr1s rextract_weightsrDas C!5!5r|| DDrc0[XRS5$)NcU$Nr)xs r!extract_buffers..rsar)r> named_buffersrCs rextract_buffersrLos C!2!2K @@rc[U5nU(a&UVs/sHn[R"U5PM nnURX5 gs snf)z Reload a set of weights so that `mod` can be used again to perform a forward pass. Note that the `params` are regular Tensors (that can have history) and so are left as Tensors. This means that mod.parameters() will still be empty after this call. N)rrArB set_tensors)r1r<r= as_paramsr8r:s r load_weightsrPus@#3'H+126a",,q/62 '3s A c/n[U5n[UR5U5HUuupVn[U5H>upUS:Xa"UR UR X55 M-UR X5 M@ MW U$)Nr)rr0r enumerater swap_tensorr.) r1r6elemsresultr8_ attr_nameselemi attr_names r _swap_stater[svF"3'H!$Y__%6!>%j1LAAv h229CD##I4 2"? Mrc<[U5nURX5 grG)rrN)r1r<buffersrOr8s r load_buffersr^s #3'H (rc@[U5[U5:wa#[S[U5S[U535e[XU5 [U5S:aG[U5[U5:wa#[S[U5S[U535e[XU5 U$)zload_state(model, weights, weight_names, buffers=(), buffer_names=()) -> model load_state takes `weights` and `buffers` and assigns them to the model. This is the inverse operation of `make_functional_deprecated_v1`. zlen(weight_names)=z != len(weights)=rzlen(buffer_names)=z != len(buffers)=)r/rrPr^)modelweights weight_namesr] buffer_namess r load_staterds <CL( \!2 33DS\N S  g. 7|a | G , $S%6$77HWW  U'2 Lrc^^[TR55n[U5S:a [S5e[ T5unmnSUU4SjjnX$T4$)amake_functional_deprecated_v1(model) -> weights, func, weight_names Given an nn.Module, make_functional_deprecated_v1 extracts the state (weights) and returns a functional version of the model, `func`. This makes it so that it is possible use transforms over the parameters of `model`. `func` can be invoked as follows: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, func, _ = make_functional_deprecated_v1(model) func(weights, (x,)) ``` And here is an example of applying the grad transform: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, _, func = make_functional_deprecated_v1(model) grad_weights = grad(func)(weights, (x,)) ``` To put the state back into a model, use `load_state`. rzmake_functional_deprecated_v1(model): `model` has buffers. Please use make_functional_with_buffers_deprecated_v1(model) instead.cT>[R"T5n[UTU5 U"U6$rG)copydeepcopyrP)radata mutable_model descriptorsr`s rfun*make_functional_deprecated_v1..funs( e, ]K9d##r)ratuple[Tensor, ...]rituple[Any, ...]returnr)listr]r/r rD)r`r]rarVrlrks` @rmake_functional_deprecated_v1rrs^85==?#G 7|a I  .e4G[!$$  $$rct^^^[T5unmn[T5unmnSUUU4SjjnXUTT4$)amake_functional_with_buffers_deprecated_v1(model) -> weights, buffers, func, weight_names, buffer_names Given an nn.Module, make_functional_with_buffers_deprecated_v1 extracts the state (weights and buffers) and returns a functional version of the model, `func`. `func` can be invoked as follows: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) func(weights, buffers, (x,)) ``` And here is an example of applying the grad transform: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) func(weights, buffers, (x,)) grad_weights = grad(func)(weights, buffers, (x,)) ``` To put the state back into a model, use `load_state`. cn>[R"T5n[UTU5 [UTU5 U"U6$rG)rgrhrPr^)rar]rirjbuf_descriptorsr`weight_descriptorss rrl7make_functional_with_buffers_deprecated_v1..funs7  e, ]$6@]OW=d##r)rarnr]rnrirorpr)rDrL)r`rarVr]rlrurvs` @@r*make_functional_with_buffers_deprecated_v1rxsiB&5U%;"G "1%"8G_a$#$#$$ $$ S"4o EErc^\rSrSrSrSU4Sjjr\SS Sjj5rS SjrSr U=r $) FunctionalModuleWithBuffersizO This is the callable object returned by :func:`make_functional_with_buffers`. c>[TU]5 XlX lX0l[ U5UlUR RU5 grG)super__init__stateless_model param_namesrcr all_names_mapupdate)selfr~rrcparam_names_mapbuffer_names_map __class__s rr}$FunctionalModuleWithBuffers.__init__sD .&(!/2 !!"23rc[R"U5n[U5up4n[U5upgnU(aUHn U R S5 M [ X$XuU5UU4$NF)rgrhrDrLrequires_grad_rz) r`disable_autograd_tracking model_copyr=rrr]rcrparams r _create_from(FunctionalModuleWithBuffers._create_from*sp ]]5) />z/J,_2A*2M// $$$U+  (HX      rc"[URUR[U5[U5-5nUR"U0UD6[URURU5 $![URURU5 f=frG)r[r~rr+)rr=r]argskwargs old_states rforward#FunctionalModuleWithBuffers.forward=s|     &ME'N *  M''88 ,,d.@.@) LK,,d.@.@) Ls A++#B)rrcrr~) r~ nn.Modulertuple[str, ...]rcrrdict[str, list[str]]rrrpNoneFr`rrboolrpJtuple[FunctionalModuleWithBuffers, tuple[Tensor, ...], tuple[Tensor, ...]]) r=Iterable[Tensor]r]rrrrrrpr __name__ __module__ __qualname____firstlineno____doc__r} staticmethodrr__static_attributes__ __classcell__rs@rrzrzs4"4%4& 4 . 4 / 4 4 [TU]5 XlX lX0lgrG)r|r}r~rr6)rr~rr6rs rr}FunctionalModule.__init__Vs! .&"rc[R"U5n[U5up4nU(aUHnURS5 M [ X$U5U4$r)rgrhrDrr)r`rrr=rr6rs rrFunctionalModule._create_fromasN ]]5) )8)D&Y $$$U+  CVKKrc[URURU5nUR"U0UD6[URURU5 $![URURU5 f=frG)r[r~r6)rr=rrrs rrFunctionalModule.forwardmsb 4 4dnnfM  I''88 ,,dnni HK,,dnni Hs A#A9)r6rr~)r~rrrr6rrprrr`rrrrpz+tuple[FunctionalModule, tuple[Tensor, ...]])r=rrrrrrprrrs@rrrQss #" #% #( #  # func, params Given a ``torch.nn.Module``, :func:`make_functional` extracts the state (params) and returns a functional version of the model, ``func``. This makes it so that it is possible use transforms over the parameters of ``model``. ``func`` can be invoked as follows: .. code-block:: python import torch import torch.nn as nn from functorch import make_functional x = torch.randn(4, 3) model = nn.Linear(3, 3) func, params = make_functional(model) func(params, x) And here is an example of applying the grad transform over the parameters of a model. .. code-block:: python import torch import torch.nn as nn from functorch import make_functional, grad x = torch.randn(4, 3) t = torch.randn(4, 3) model = nn.Linear(3, 3) func, params = make_functional(model) def compute_loss(params, x, t): y = func(params, x) return nn.functional.mse_loss(y, t) grad_weights = grad(compute_loss)(params, x, t) If the model has any buffers, please use :func:`make_functional_with_buffers` instead. Args: model (torch.nn.Module): Input model. disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters. The returned params are unrelated to the set of params from the original model. If False (default), the params will have ``requires_grad=True`` on them (aka they will be trackable with regular PyTorch autograd), matching the requires_grad-ness of the params from the original model. Otherwise, the returned params will have ``requires_grad=False``. Default, False. If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``. Otherwise, if you're only planning on using functorch's gradient transforms, then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking history with PyTorch autograd. rzdmake_functional(model): `model` has buffers. Please use make_functional_with_buffers(model) instead.r)rqr]r/r rr)r`rr]s rmake_functionalrwsPz5==?#G 7|a ;    ( (  ) rc([RXS9$)a$make_functional_with_buffers(model, disable_autograd_tracking=False) -> func, params, buffers Given a ``torch.nn.Module``, make_functional_with_buffers extracts the state (params and buffers) and returns a functional version of the model ``func`` that can be invoked like a function. ``func`` can be invoked as follows: .. code-block:: python import torch import torch.nn as nn from functorch import make_functional_with_buffers x = torch.randn(4, 3) model = nn.Linear(3, 3) func, params, buffers = make_functional_with_buffers(model) func(params, buffers, x) And here is an example of applying the grad transform over the parameters of a model: .. code-block:: python import torch import torch.nn as nn from functorch import make_functional_with_buffers, grad x = torch.randn(4, 3) t = torch.randn(4, 3) model = nn.Linear(3, 3) func, params, buffers = make_functional_with_buffers(model) def compute_loss(params, buffers, x, t): y = func(params, buffers, x) return nn.functional.mse_loss(y, t) grad_weights = grad(compute_loss)(params, buffers, x, t) Args: model (torch.nn.Module): Input model. disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters. The returned params are unrelated to the set of params from the original model. If False (default), the params will have ``requires_grad=True`` on them (aka they will be trackable with regular PyTorch autograd), matching the requires_grad-ness of the params from the original model. Otherwise, the returned params will have ``requires_grad=False``. Default, False. If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``. Otherwise, if you're only planning on using functorch's gradient transforms, then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking history with PyTorch autograd. r)rzr)r`rs rmake_functional_with_buffersrs t ' 3 3  4 rcL[[U65n[SU55nU$)Nc3j# UH)n[R"U5R5v M+ g7frGr,stackdetach.0shardss r "transpose_stack..s)3L F""$$3L13)r+r0)tuple_of_tuple_of_tensorsresultss rtranspose_stackrs3!&c+D&E F3LG Nrc^[U5S:Xa [S5e[SU55(d"[SU55(d [S5e[US5m[U4SjU55(d [S5e[ UVs/sHn[ U5PM sn6up#n[ U5n[ U5nUSX44$s snf)acombine_state_for_ensemble(models) -> func, params, buffers Prepares a list of torch.nn.Modules for ensembling with :func:`vmap`. Given a list of ``M`` ``nn.Modules`` of the same class, stacks all of their parameters and buffers together to make ``params`` and ``buffers``. Each parameter and buffer in the result will have an additional dimension of size ``M``. :func:`combine_state_for_ensemble` also returns ``func``, a functional version of one of the models in :attr:`models`. One cannot directly run ``func(params, buffers, *args, **kwargs)`` directly, you probably want to use ``vmap(func, ...)(params, buffers, *args, **kwargs)`` Here's an example of how to ensemble over a very simple model: .. code-block:: python num_models = 5 batch_size = 64 in_features, out_features = 3, 3 models = [torch.nn.Linear(in_features, out_features) for i in range(num_models)] data = torch.randn(batch_size, 3) fmodel, params, buffers = combine_state_for_ensemble(models) output = vmap(fmodel, (0, 0, None))(params, buffers, data) assert output.shape == (num_models, batch_size, out_features) .. warning:: All of the modules being stacked together must be the same (except for the values of their parameters/buffers). For example, they should be in the same mode (training vs eval). This API is subject to change -- we're investigating better ways to create ensembles and would love your feedback how to improve this. rz?combine_state_for_ensemble: Expected at least one model, got 0.c38# UHoRv M g7frGtrainingrms rr-combine_state_for_ensemble..4s+Fq Fsc3B# UHoR(+v M g7frGrrs rrr4s2R6azz>>6szTcombine_state_for_ensemble: Expected all models to have the same training/eval mode.c3>># UHn[U5TLv M g7frG)type)rr model0_typs rrr:s5ftAw*$fszHcombine_state_for_ensemble: Expected all models to be of the same class.)r/r allrr0rr)modelsr`funcsr=r]rs @rcombine_state_for_ensemblersP 6{a M   +F+ + +s2R62R/R/R 0  fQiJ 5f5 5 5 V  !;A B6% &u -6 BE7V $Fg&G 8V $$ CsC c,^^^SUUU4SjjnU$)Nc>^^[T 5S:a [S5e[T 5S:Xa"T "T0TD6RT 5n[U5$T SnUS::a[SUS35e[ UU UU 4Sj[ U555n[T "T0TD65upVn[ SU55n[ [ U65n[ SU55nXU4$) N,NYI: ensemble_shape with more than 1 elementr num_models  should be > 0c3R># UHnT"T0TD6RT5v M g7frGtorrVrr)r model_classs rr3functional_init..wrapped..W- =NK ( ( + +F 3 3=N$'c3># UHn[U5Sv M g7f)rN)rrrr`s rrr[sTVE5e.wrappedKs ~ ! #KL L ~ ! #0033F;E07 7#A& ?{:,nEF F =B:=N  5[$5Q&5QR uTVTTW &K7KKE!!r)rrrrrp>tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]rrrr)rs``` rfunctional_initrFs, ""!" G""* Nrc,^^^SUUU4SjjnU$)Nc >^^[T 5S:a [S5e[T 5S:Xa"T "T0TD6RT 5n[U5$T SnUS::a[SUS35e[ UU UU 4Sj[ U555n[ T "T0TD65unnnnn[[ SU556up[ [U 65n [ SU 55n [ [U 65n [ S U 55n XXgU4$) Nrrrrrc3R># UHnT"T0TD6RT5v M g7frGrrs rr@functional_init_with_buffers..wrapped..rrc3># UHn[U5SSv M g7f)Nr)rxrs rrrs##E;5A"1E#rc3j# UH)n[R"U5R5v M+ g7frGrrs rrrrrc3j# UH)n[R"U5R5v M+ g7frGrrs rrrrr)r/rrrrr+rrxr0)rrr`rrrVrrbrcrar]r)rrs`` rr-functional_init_with_buffers..wrappedrs& ~ ! #KL L ~ ! #0033F;E07 7#A& ?{:,nEF F =B:=N   7{D7SF7S T     #  W &K7KKW &K7KK<??r)rrrrrpztuple[tuple[Tensor, ...], tuple[Tensor, ...], Callable[..., Any], tuple[str, ...], tuple[str, ...]] | tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]rrs``` rfunctional_init_with_buffersrcs5)@)@!)@ I)@)@V Nr)rpr)r0dict[str, Tensor] | Iterable[tuple[str, Tensor]]rrrpr)r1rr2z+Callable[..., Iterable[tuple[str, Tensor]]]r3zCallable[[Tensor], Tensor]rp@tuple[tuple[Tensor, ...], tuple[str, ...], dict[str, list[str]]])r1rrprr) r1rr< Sequence[str]r=Sequence[Tensor]rOrrpr)r1rr6rrTrrpz list[Tensor]) r1rr<rr]rrOrrprr*) r`rrarrbrr]rrcrrpr)r`rrpr)r`rrpzctuple[tuple[Tensor, ...], tuple[Tensor, ...], Callable[..., Any], tuple[str, ...], tuple[str, ...]]rr)rztuple[tuple[Tensor, ...], ...]rprn)rzSequence[nn.Module]rpr)rcpu)rtype[nn.Module]rtuple[()] | tuple[int, ...]r)torch.types.DevicerpzMCallable[..., tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]])rrrrr)rrpzCallable[..., tuple[tuple[Tensor, ...], tuple[Tensor, ...], Callable[..., Any], tuple[str, ...], tuple[str, ...]] | tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]])% __future__rrgtypingrrrr,torch.nnrAr%torch.nn.utils._named_member_accessorrcollections.abcr r r rr%r>rDrLrPr[r^rdrrrxModulerzrrrrrrrrrrrs# // E<< ,B ,G , ,F$ $>$)$F $4 E  EE EA AEA ( ( ( ( (  ("   3 E E15E0ER9>< <15<O<~=;% ;%O;%@35!& / S >35!&: :/: :  :r