^j_4ddlmZddlZddlmZmZmZddlZddlm Z ddlm Z ddl m Z er ddl mZmZmZddZ ddZ dd Z dd Z dd Z d d!d Z d"d Z d d#dZ d$ d%dZ d&dZ d'dZGdde j8ZGdde j8Z d d(dZ d d)dZ d*dZ! d+dZ" d, d-dZ# d, d.dZ$y)/) annotationsN)AnyNoReturn TYPE_CHECKING)Tensor)NamedMemberAccessor)CallableIterableSequencectd)Na$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) RuntimeErrork/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/torch/_functorch/make_functional.pyraise_parameter_tying_errorrs  : rct|}t|}t|j}t|j}|j|st d|d|i}|j D] \}}|gf||<|j D]/\}}||vrt d|d||dj |1t|jS)a[ 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%$s\*!"34-2245 !7!?  % %&< = !2 34&&<%= ?  >@(..0. V%("I&!.-3351 V * * "3%'GH  &!!$++C0 1 !((* ++rcNt|d}t|d}t||}i}t|}|D]@\}} | |vr |tj| d|| <|| } |j || Bt |dk(rd\} } n t|\} } | | |fS)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 !57HII D"3'H$/a D=u//&ABDG1g D+. /   A% v12 v 5) ##rcLt||jtjS)aZ This function removes all the Parameters from the model and return them as a tuple as well as their original attribute names. The weights must be re-loaded with `load_weights` before the model can be used again. Note that this function modifies the model in place and after this call, mod.parameters() will be empty. )r>named_parametersnn Parameterr1s rextract_weightsrDas C!5!5r|| DDrc2t||jdS)Nc|SNr)xs rz!extract_buffers..rsar)r> named_buffersrCs rextract_buffersrKos C!2!2K @@rct|}|r"|Dcgc]}tj|}}|j||ycc}w)a  Reload a set of weights so that `mod` can be used again to perform a forward pass. Note that the `params` are regular Tensors (that can have history) and so are left as Tensors. This means that mod.parameters() will still be empty after this call. N)rrArB set_tensors)r1r<r= as_paramsr8r:s r load_weightsrOusA#3'H+12a",,q/22 '3sAcg}t|}t|j|D]T\\}}}t|D]>\}} |dk(r"|j |j | |-|j | |@V|S)Nr)rr0r enumerater swap_tensorr.) r1r6elemsresultr8_ attr_nameselemi attr_names r _swap_staterZsF"3'H!$Y__%6!>5J%j1 5LAyAv h229dCD##It4  55 Mrc>t|}|j||yrG)rrM)r1r<buffersrNr8s r load_buffersr]s #3'H (rc>t|t|k7r#tdt|dt|t|||t|dkDrGt|t|k7r#tdt|dt|t||||S)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/rrOr])modelweights weight_namesr\ buffer_namess r load_statercs <CL( \!2 33DS\N S   g. 7|a | G , $S%6$77HWW  UL'2 Lrctj}t|dkDr tdt \}}dfd }||fS)amake_functional_deprecated_v1(model) -> weights, func, weight_names Given an nn.Module, make_functional_deprecated_v1 extracts the state (weights) and returns a functional version of the model, `func`. This makes it so that it is possible use transforms over the parameters of `model`. `func` can be invoked as follows: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, func, _ = make_functional_deprecated_v1(model) func(weights, (x,)) ``` And here is an example of applying the grad transform: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, _, func = make_functional_deprecated_v1(model) grad_weights = grad(func)(weights, (x,)) ``` To put the state back into a model, use `load_state`. rzmake_functional_deprecated_v1(model): `model` has buffers. Please use make_functional_with_buffers_deprecated_v1(model) instead.cRtj}t||||SrG)copydeepcopyrO)r`data mutable_model descriptorsr_s rfunz*make_functional_deprecated_v1..funs( e, ]K9d##r)r`tuple[Tensor, ...]rhtuple[Any, ...]returnr)listr\r/r rD)r_r\r`rUrkrjs` @rmake_functional_deprecated_v1rps[85==?#G 7|a I  .e4G[!$ C $$rcrt\}}t\}} dfd }|||fS)a`make_functional_with_buffers_deprecated_v1(model) -> weights, buffers, func, weight_names, buffer_names Given an nn.Module, make_functional_with_buffers_deprecated_v1 extracts the state (weights and buffers) and returns a functional version of the model, `func`. `func` can be invoked as follows: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) func(weights, buffers, (x,)) ``` And here is an example of applying the grad transform: ``` x = torch.randn(4, 3) model = nn.Linear(3, 3) weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) func(weights, buffers, (x,)) grad_weights = grad(func)(weights, buffers, (x,)) ``` To put the state back into a model, use `load_state`. cltj}t||t||||SrG)rfrgrOr])r`r\rhribuf_descriptorsr_weight_descriptorss rrkz7make_functional_with_buffers_deprecated_v1..funs7  e, ]$6@]OW=d##r)r`rlr\rlrhrmrnr)rDrK)r_r`rUr\rkrsrts` @@r*make_functional_with_buffers_deprecated_v1rusfB&5U%;"G "1%"8G_a$#$#$$ $ GS"4o EErcxeZdZdZ dfd Ze d ddZ ddZxZS) FunctionalModuleWithBufferszW This is the callable object returned by :func:`make_functional_with_buffers`. ct|||_||_||_t ||_|j j|yrG)super__init__stateless_model param_namesrbr all_names_mapupdate)selfr{r|rbparam_names_mapbuffer_names_map __class__s rrzz$FunctionalModuleWithBuffers.__init__sJ .&(!/2 !!"23rctj|}t|\}}}t|\}}}|r|D]} | j dt |||||||fSNF)rfrgrDrKrequires_grad_rw) r_disable_autograd_tracking model_copyr=r|rr\rbrparams r _create_fromz(FunctionalModuleWithBuffers._create_from*s} ]]5) />z/J, _2A*2M// $ ,$$U+ , (KHX      rc"t|j|jt|t|z} |j|i|t|j|j|S#t|j|j|wxYwrG)rZr{r}r+)rr=r\argskwargs old_states rforwardz#FunctionalModuleWithBuffers.forward=s~     &ME'N *  M'4''88 ,,d.@.@) LK,,d.@.@) Ls A++#B) r{ nn.Moduler|tuple[str, ...]rbrrdict[str, list[str]]rrrnNoneFr_rrboolrnJtuple[FunctionalModuleWithBuffers, tuple[Tensor, ...], tuple[Tensor, ...]]) r=Iterable[Tensor]r\rrrrrrnr __name__ __module__ __qualname____doc__rz staticmethodrr __classcell__rs@rrwrws4"4%4& 4 . 4 / 4 4  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)ror\r/r rr)r_rr\s rmake_functionalrwsSz5==?#G 7|a ;    ( ( )B ) rc0tj||S)amake_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)rwr)r_rs rmake_functional_with_buffersrs#t ' 3 3 )B 4 rcLtt|}td|D}|S)Nc3bK|]'}tj|j)ywrGr,stackdetach.0shardss r z"transpose_stack..s')/ F""$-/)r+r0)tuple_of_tuple_of_tensorsresultss rtranspose_stackrs3!&c+D&E F3LG Nrczt|dk(r tdtd|Dstd|Ds tdt|dtfd|Ds tdt |Dcgc] }t |c}\}}}t |}t |}|d||fScc}w)a(combine_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.c34K|]}|jywrGtrainingrms rrz-combine_state_for_ensemble..4s+q +sc36K|]}|j ywrGrrs rrz-combine_state_for_ensemble..4s2Raqzz>2RszTcombine_state_for_ensemble: Expected all models to have the same training/eval mode.c38K|]}t|uywrG)type)rr model0_typs rrz-combine_state_for_ensemble..:s5tAw*$5szHcombine_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 0  fQiJ 5f5 5 V  !;A B% &u - BE67V $Fg&G 8VW $$ CsB8c( dfd }|S)Nct dk\r tdt dk(r" ij }t|S d}|dkrtd|dt  fdt |D}t i\}}}t d|D}t t |}t d|D}|||fS) N,NYI: ensemble_shape with more than 1 elementr num_models  should be > 0c3LK|]}ijywrGtorrUrr)r model_classs rrz3functional_init..wrapped..W+ 89K ( ( + +F 3 !$c38K|]}t|dyw)rN)rprr_s rrz3functional_init..wrapped..[sTE5e.wrapped..]"K F+224Kr)r/ ValueErrorrrpr+ranger0) rrr_ num_modelsrrUfnr<r`r)ensemble_shapers `` rwrappedz functional_init..wrappedKs ~ ! #KL L ~ ! #0033F;E07 7#A& ?{:,nEF F =B:=N  5[$5Q&5QR 2uTVTTW &K7KKE!!r)rrrrrn>tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]rrrr)rs``` rfunctional_initrFs' ""!" G"* Nrc( dfd }|S)Nc t dk\r tdt dk(r" ij }t|S d}|dkrtd|dt  fdt |D}t  i\}}}}}tt d|D\} } t t| } t d| D} t t| } t d | D} | | |||fS) Nrrrrrc3LK|]}ijywrGrrs rrz@functional_init_with_buffers..wrapped..rrc38K|]}t|ddyw)Nr)rurs rrz@functional_init_with_buffers..wrapped..s$;5A"1Erc3bK|]'}tj|j)ywrGrrs rrz@functional_init_with_buffers..wrapped..rrc3bK|]'}tj|j)ywrGrrs rrz@functional_init_with_buffers..wrapped..rr)r/rrrpr+rrur0)rrr_rrrUrrarbr`r\r)rrs`` rrz-functional_init_with_buffers..wrappedrs, ~ ! #KL L ~ ! #0033F;E07 7#A& ?{:,nEF F =B:=N   7{D7SF7S T     #  W &K7KKW &K7KK\<??r)rrrrrnztuple[tuple[Tensor, ...], tuple[Tensor, ...], Callable[..., Any], tuple[str, ...], tuple[str, ...]] | tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]rrs``` rfunctional_init_with_buffersrcs/)@)@!)@ I)@V Nr)rnr)r0dict[str, Tensor] | Iterable[tuple[str, Tensor]]rrrnr)r1rr2z+Callable[..., Iterable[tuple[str, Tensor]]]r3zCallable[[Tensor], Tensor]rn@tuple[tuple[Tensor, ...], tuple[str, ...], dict[str, list[str]]])r1rrnrr) r1rr< Sequence[str]r=Sequence[Tensor]rNrrnr)r1rr6rrSrrnz list[Tensor]) r1rr<rr\rrNrrnrr*) r_rr`rrarr\rrbrrnr)r_rrnr)r_rrnzctuple[tuple[Tensor, ...], tuple[Tensor, ...], Callable[..., Any], tuple[str, ...], tuple[str, ...]]rr)rztuple[tuple[Tensor, ...], ...]rnrl)rzSequence[nn.Module]rnr)rcpu)rtype[nn.Module]rtuple[()] | tuple[int, ...]r)torch.types.DevicernzMCallable[..., tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]])rrrrr)rrnzCallable[..., tuple[tuple[Tensor, ...], tuple[Tensor, ...], Callable[..., Any], tuple[str, ...], tuple[str, ...]] | tuple[tuple[Tensor, ...], Callable[..., Any], tuple[str, ...]]])% __future__rrftypingrrrr,torch.nnrAr%torch.nn.utils._named_member_accessorrcollections.abcr r r rr%r>rDrKrOrZr]rcrpruModulerwrrrrrrrrrrrs# // E<< ,B ,G , ,F$ $>$)$F $4 E  EE EA AEA ( ( ( ( (  ("   3 E E15E0ER9>< <15<O<~=;% ;%O;%@35!& / S >35!&: :/: :  :r