{-j ddlmZddlZddlZddlZddlZddlZddlZddlm Z m Z ddlm Z m Z m Z mZddlZddlmZmZddlZddlmZmZmZmZddlmZddlmZmZmZdd lm Z dd l!m"Z"dd l#m$Z$m%Z%m&Z&m'Z'dd l(m)Z)dd l*m+Z+m,Z,ddl-m.Z.m/Z/m0Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7ddl8m9Z9ddl:m;Z;mZ>ddl?m@Z@ddlAmBZBddlCmDZDe rddlEmFZFmGZGmHZHmIZIddlJmKZKmLZLmMZMmNZNddlOmPZPgZQee degefe deeReSe fgeTeeReSe ffffZUee deegefe deeReSe fegeffZVeeReSefej eSeffZWe eWgdfZXejYdZZejYdZ[dZ\dZ]dZ^dZ_dZ`d ZaGd!d"e d#d$d%gZbGd&d'e9ZcGd(d)ZdGd*d+ZeGd,dZfdS)-) annotationsN) OrderedDict namedtuple) TYPE_CHECKINGAnyCallableUnion)Selfoverload)Tensordtypennprofiler)ValueSet)core framework unique_name)VarDesc)no_grad)_convert_into_variablein_declarative_modein_sot_simulation_modein_to_static_mode)_append_activation_in_dygraph)Executor global_scope) ParameterProgram_current_expected_placeconvert_np_dtype_to_dtype_default_main_programin_dygraph_mode in_pir_mode name_structpaddle_type_to_proto_type)LayerHelperBase)ShardedStateDictbuild_sharded_state_dict) ParamAttr)in_profiler_mode) deprecated)param_one_alias)IterableIteratorMappingSequence) DTypeLike ParamAttrLike PlaceLike ShapeLike) InitializerLayerz(.)([A-Z][a-z]+)z([a-z])([A-Z])c*ts|jjdkrJtt j|j_d|j_dSd|j_tj |j ddSdS)zC A pre-hook to mark op numbers before enter layer.forward. rTFzX has recorded the op information before. 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NFcL||_||_||_||_||_dSr)r9rGr<r=rK)rr9rGr<r=rKs rCrzLayerOpsRecorder.__init___s)    rE)rrNFN)rrr__doc__rr~rErCrrZs2rErc8eZdZUdZdZded<dddd ZddZdS)HookRemoveHelperz3A HookRemoveHelper that can be used to remove hook.rint next_hook_idNextra_hook_dictrK+typing.OrderedDict[int, Callable[..., Any]]rrrNonec8tj||_tj|_txjdz c_d|_|Qt|tr td|D|_dStj|f|_dSdS)Nrgr~c3>K|]}tj|VdSr)weakrefref).0ds rC z,HookRemoveHelper.__init__..ys;..'(GKNN......rE) rr _hooks_refrr_hook_id_extra_hooks_refrrr)rrKrs rCrzHookRemoveHelper.__init__ls "+e,,(5 %%*%%')  &/400 H(-..,;...))%%%*1_)E)E(G%%% ' &rEc|}||j|vr||j=|jD]}|}||j|vr||j= dSr)rrr)rrKr extra_hookss rCrLzHookRemoveHelper.removesq!!  %!7!7dm$( / /C#%%K&4=K+G+G . / /rE)rKrrrrr)rr)rrrrr__annotations__rrLr~rErCrrgsj==L $ HHHHHH&//////rErczeZdZUdZded< ddd Zed ZejdZedZ e jdZ dddZ ddZ ddZ ddZ dddddd#ZeZddd$dd&Z ddd2Zdd5Zed6d7d89 ddd=Z ddd>Zed?d@gdddBZdddCZddEZddGZdddJZed?d@g dddOZ dddRZddTZ dddVZed;dWg dddYZ ed?d@gdddZZ!dd[Z"ed?d@g ddd\Z#ddd^Z$ddbZ%dddZ&ddeZ'ddfZ(ddgZ)ddiZ*ddjZ+edkdlg dddnZ,e+Z-e,Z.ddpZ/e/Z0ddrZ1dduZ2dvZ3ddxZ4ddzZ5dd{Z6dd}Z7dd~Z8ddZ9ddZ:ddZ;ddZ< dddZ= dddZ> dddZ?e@ dddZAe@dddddZAe@dddddZAe@ddKddddZAddZA dddZB dddZCeDjE dddZF dddZG dddZH dŐddZIddZJ dddZKddZLeFZMeFZNd dZOddZPddZQ dҐd dZR dҐd dZS dҐd dZTdҐd dZUdҐd dZVddZWd dZXdŐd dZYdŐddZZd„Z[dS(r6a Dynamic graph Layer based on OOD, includes the parameters of the layer, the structure of the forward graph and so on. Parameters: name_scope (str, optional): prefix name used by the layer to name parameters. If prefix is "my_layer", parameter name in MyLayer can be "my_layer_0.w_n", where "w" is the parameter base name and "n" is an unique suffix auto-generated. If None, prefix name will be snake cased class name. Default: None. dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". Default: "float32" Returns: None Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) >>> class MyLayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self._linear = paddle.nn.Linear(1, 1) ... self._dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... temp = self._linear(input) ... temp = self._dropout(temp) ... return temp ... >>> x = paddle.randn([10, 1], 'float32') >>> mylayer = MyLayer() >>> mylayer.eval() # set mylayer._dropout to eval mode >>> out = mylayer(x) >>> mylayer.train() # set mylayer._dropout to train mode >>> out = mylayer(x) >>> print(out) Tensor(shape=[10, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[-3.44879317], [ 0. ], [ 0. ], [-0.73825276], [ 0. ], [ 0. ], [ 0.64444798], [-3.22185946], [ 0. ], [-0.68077987]]) rtrainingNfloat32 name_scope str | Noner r1rrcd|_|(t|jj}t |}t j||_t|j|_ d|_ ||_ t|_ t|_t|_t#|_t|_t|_t+gg|_i|_t|_t|_t|_t|_t|_d|_t|_t|_dS)NTF)r<rK) rrdrrr\rgenerater@r_helper_built_dtyper"_init_in_dynamic_moder _parameters_buffersset!_non_persistable_buffer_names_set _sub_layers_loaddict_holderrr8rH_forward_pre_hooks_forward_post_hooks#_forward_pre_hooks_with_kwargs_flag$_forward_post_hooks_with_kwargs_flag!_forward_post_hooks_always_called_cast_to_low_precision_state_dict_hooks_original_funcs)rrr s rCrzLayer.__init__sM  01HIIJ+J77J%.z::(99   %4%6%6"&==#  14.&== + -2>>>!# MM  MM  MM 0 MM 1 MM . '+# MM  +}}rEc|jSr)rrs rC_moduleszLayer._moduless rEct|tstdt||j|j|dS)Nz _modules must be dict-like, got )rdictrrxrclearupdatervalues rCrzLayer._modulessd%&& NLtE{{LLMM M     &&&&&rEc|jSr)rrs rC_non_persistent_buffers_setz!Layer._non_persistent_buffers_sets 55rEct|tstdt||j|j|dS)Nz/_non_persistent_buffers_set must be a set, got )rrrrxrrr r s rCr z!Layer._non_persistent_buffers_setsk%%% O$u++OO  .44666 .55e<<<<>> import paddle >>> paddle.seed(100) >>> class MyLayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self._linear = paddle.nn.Linear(1, 1) ... self._dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... temp = self._linear(input) ... temp = self._dropout(temp) ... return temp ... >>> x = paddle.randn([10, 1], 'float32') >>> mylayer = MyLayer() >>> mylayer.eval() # set mylayer._dropout to eval mode >>> out = mylayer(x) >>> mylayer.train() # set mylayer._dropout to train mode >>> out = mylayer(x) >>> print(out) Tensor(shape=[10, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[-3.44879317], [ 0. ], [ 0. ], [-0.73825276], [ 0. ], [ 0. ], [ 0.64444798], [-3.22185946], [ 0. ], [-0.68077987]]) z'training mode is expected to be boolean) rrrr"r_dygraph_tracer train_mode eval_moder sublayers)rrrAs rCtrainz Layer.train s\$%% HFGG G    8 8)++668888)++55777 ^^%% " "E!ENN rEctr%tjd|_|D] }d|_ |S)a Sets this Layer and all its sublayers to evaluation mode. This only effects certain modules like `Dropout` and `BatchNorm`. Returns: Layer: self Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) >>> class MyLayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self._linear = paddle.nn.Linear(1, 1) ... self._dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... temp = self._linear(input) ... temp = self._dropout(temp) ... return temp ... >>> x = paddle.randn([10, 1], 'float32') >>> mylayer = MyLayer() >>> mylayer.eval() # set mylayer._dropout to eval mode >>> out = mylayer(x) >>> print(out) Tensor(shape=[10, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[-1.72439659], [ 0.31532824], [ 0.01192369], [-0.36912638], [-1.63426113], [-0.93169814], [ 0.32222399], [-1.61092973], [ 0.77209264], [-0.34038994]]) F)r"rrrrr)rrAs rCevalz Layer.evalLs\Z    4  % ' ' 1 1 3 3 3 ^^%% # #E"ENN rEfnCallable[[Self], None]ct|D]}|||||S)a Applies ``fn`` recursively to every sublayer (as returned by ``.sublayers()``) as well as self. Typical use includes initializing the parameters of a model. Parameters: fn (function): a function to be applied to each sublayer Returns: Layer, self Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> paddle.seed(2023) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> def init_weights(layer): ... if type(layer) == nn.Linear: ... print('before init weight:', layer.weight.numpy()) ... new_weight = paddle.full(shape=layer.weight.shape, dtype=layer.weight.dtype, fill_value=0.9) ... layer.weight.set_value(new_weight) ... print('after init weight:', layer.weight.numpy()) ... >>> net.apply(init_weights) >>> print(net.state_dict()) before init weight: [[ 0.89611185 0.04935038] [-0.5888344 0.99266374]] after init weight: [[0.9 0.9] [0.9 0.9]] before init weight: [[-0.18615901 -0.22924072] [ 1.1517721 0.59859073]] after init weight: [[0.9 0.9] [0.9 0.9]] OrderedDict([('0.weight', Parameter containing: Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[0.89999998, 0.89999998], [0.89999998, 0.89999998]])), ('0.bias', Parameter containing: Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False, [0., 0.])), ('1.weight', Parameter containing: Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[0.89999998, 0.89999998], [0.89999998, 0.89999998]])), ('1.bias', Parameter containing: Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False, [0., 0.]))]) )childrenapply)rrrAs rCrz Layer.applys?f]]__  E KKOOOO 4 rErlc|jS)a Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__ Returns: str, full name of this layer. Examples: .. code-block:: python >>> import paddle >>> class LinearNet(paddle.nn.Layer): ... def __init__(self): ... super().__init__(name_scope = "demo_linear_net") ... self._linear = paddle.nn.Linear(1, 1) ... ... def forward(self, x): ... return self._linear(x) ... >>> linear_net = LinearNet() >>> print(linear_net.full_name()) demo_linear_net_0 )r@rs rC full_namezLayer.full_names 4rEF)prepend with_kwargs always_callhook_ForwardPostHookrr r!rct|j|j|jg}||j|j<|rd|j|j<|rd|j|j<|r!|j|jd|S)a Register a forward post-hook for Layer. The hook will be called after `forward` function has been computed. It should have the following form, `input` and `output` of the `hook` is `input` and `output` of the `Layer` respectively. User can use forward post-hook to change the output of the Layer or perform information statistics tasks on the Layer. hook(Layer, input, output) -> None or modified output Parameters: hook(function): a function registered as a forward post-hook prepend (bool): If ``True``, the provided ``hook`` will be fired before all existing ``forward_post`` hooks on this :class:`paddle.nn.Layer`. Default: ``False`` with_kwargs (bool): If ``True``, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` always_call (bool): If ``True`` the ``hook`` will be run regardless of whether an exception is raised while calling the Module. Default: ``False`` Returns: HookRemoveHelper, a HookRemoveHelper object that can be used to remove the added hook by calling `hook_remove_helper.remove()` . Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> # the forward_post_hook change the output of the layer: output = output * 2 >>> def forward_post_hook(layer, input, output): ... # user can use layer, input and output for information statistics tasks ... ... # change the output ... return output * 2 ... >>> linear = paddle.nn.Linear(13, 5) >>> # register the hook >>> forward_post_hook_handle = linear.register_forward_post_hook(forward_post_hook) >>> value1 = np.arange(26).reshape(2, 13).astype("float32") >>> in1 = paddle.to_tensor(value1) >>> out0 = linear(in1) >>> # remove the hook >>> forward_post_hook_handle.remove() >>> out1 = linear(in1) >>> # hook change the linear's output to output * 2, so out0 is equal to out1 * 2. >>> assert (out0.numpy() == (out1.numpy()) * 2).any() rTFlast)rrrrr move_to_end)rr"rr r!hook_remove_helpers rCregister_forward_post_hookz Layer.register_forward_post_hooksB.  $96   AE !3!<=    5"+     2"+     $ 0 0"+% 1   "!rE)rr _ForwardPreHookct|j|j}||j|j<|rd|j|j<|r!|j|jd|S)a Register a forward pre-hook for Layer. The hook will be called before `forward` function has been computed. It should have the following form, `input` of the `hook` is `input` of the `Layer`, hook can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple). User can use forward pre-hook to change the input of the Layer or perform information statistics tasks on the Layer. hook(Layer, input) -> None or modified input Parameters: hook(function): a function registered as a forward pre-hook prepend (bool): If ``True``, the provided ``hook`` will be fired before all existing ``forward_pre`` hooks on this :class:`paddle.nn.Layer`. Default: ``False`` with_kwargs (bool): If true, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` Returns: HookRemoveHelper, a HookRemoveHelper object that can be used to remove the added hook by calling `hook_remove_helper.remove()` . Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> # the forward_pre_hook change the input of the layer: input = input * 2 >>> def forward_pre_hook(layer, input): ... # user can use layer and input for information statistics tasks ... ... # change the input ... input_return = (input[0] * 2) ... return input_return ... >>> linear = paddle.nn.Linear(13, 5) >>> # register the hook >>> forward_pre_hook_handle = linear.register_forward_pre_hook(forward_pre_hook) >>> value0 = np.arange(26).reshape(2, 13).astype("float32") >>> in0 = paddle.to_tensor(value0) >>> out0 = linear(in0) >>> # remove the hook >>> forward_pre_hook_handle.remove() >>> value1 = value0 * 2 >>> in1 = paddle.to_tensor(value1) >>> out1 = linear(in1) >>> # hook change the linear's input to input * 2, so out0 is equal to out1. >>> assert (out0.numpy() == out1.numpy()).any() rTFr%)rrrrr')rr"rr r(s rCregister_forward_pre_hookzLayer.register_forward_pre_hook2s@.  # D   @D 2 ;<    4"+     # / /"+% 0   "!rEshaper4attrParamAttrLike | NoneDTypeLike | Noneis_biasdefault_initializerInitializer | NonedevicePlaceLike | Noner ctj|}t|tr|dkrd}|j||||||S)aB Create parameters for this layer. Parameters: shape(list): Shape of the parameter. The data type in the list must be int. attr(ParamAttr, optional): Parameter attribute of weight. Please refer to :ref:`api_paddle_ParamAttr`. Default: None. dtype(str, optional): Data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". Default: "float32". is_bias(bool, optional): if this is a bias parameter. Default: False. default_initializer(Initializer, optional): the default initializer for this parameter. If set None, default initializer will be set to paddle.nn.initializer.Xavier and paddle.nn.initializer.Constant for non-bias and bias parameter, respectively. Default: None. device(PlaceLike, optional): the device place for the parameter. Default: None. Returns: :Tensor, created parameter. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> class MyLayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self._linear = paddle.nn.Linear(1, 1) ... w_tmp = self.create_parameter([1,1]) ... self.add_parameter("w_tmp", w_tmp) ... ... def forward(self, input): ... return self._linear(input) ... >>> mylayer = MyLayer() >>> for name, param in mylayer.named_parameters(): ... print(name, param) # will print w_tmp,_linear.weight,_linear.bias w_tmp Parameter containing: Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[0.06979191]]) _linear.weight Parameter containing: Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[1.26729357]]) _linear.bias Parameter containing: Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=False, [0.]) Nr4)rrrrlrcreate_parameter)rr-r.r r1r2r4 temp_attrs rCr9zLayer.create_parameters`nM$'' i % % )r//I|,, ueW.A&-   rEtargetrcv|d\}}}||}t||s*t|dz|zdzt ||}t |tjj tj fstd|zdz|S)a Return the parameter given by ``target`` if it exists, otherwise throw an error. Parameters: target(str): The fully-qualified string name of the Parameter to look for. Returns: Parameter: The Parameter referenced by ``target``. . has no attribute ``z` is not an nn.Parameter) rpartition get_sublayerhasattrAttributeError _get_namegetattrrpaddlerrr )rr; module_path_ param_namemodrs rCrzLayer.get_parameters&,%6%6s%;%;" Q #00==sJ''   "77*DsJ &-S*%=%=%&)"5v}!EFF P z!14N!NOO O rEz2.0.0zpaddle.nn.Layer.create_tensorz(New api in create_tensor, easier to use.)since update_toreasonrb persistable bool | Nonec.|d|j|g}n.tjd|jdg}|jj|||tj j j S)a, Create Tensor for this layer. Parameters: name(str, optional): name of the tensor. Please refer to :ref:`api_guide_Name` . Default: None persistable(bool, optional): if set this tensor persistable. Default: False dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64","int8", "int16", "int32", "int64", "uint8" or "uint16". If set None, it will be "float32". Default: None Returns: Tensor, created Tensor. Examples: .. code-block:: python >>> import paddle >>> class MyLinear(paddle.nn.Layer): ... def __init__(self, ... in_features, ... out_features): ... super().__init__() ... self.linear = paddle.nn.Linear( 10, 10) ... ... self.back_var = self.create_variable(name = "linear_tmp_0", dtype=self._dtype) ... ... def forward(self, input): ... out = self.linear(input) ... paddle.assign( out, self.back_var) ... ... return out Nr=_generated_varrbrNr rx rmr@rrrrr; create_varrrVarType DENSE_TENSORrrbrNr var_names rCcreate_variablezLayer.create_variables\  xx$ 788HH"+$/+;<==H|(6688CC#%2 D   rEc.|d|j|g}n.tjd|jdg}|jj|||tj j j S)a] Create Tensor for this layer. Parameters: name(str, optional): name of the tensor. Please refer to :ref:`api_guide_Name` . Default: None. persistable(bool, optional): if set this tensor persistable. Default: False. dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". If set None, it will be "float32". Default: None. Returns: Tensor, created Tensor. Examples: .. code-block:: python >>> import paddle >>> class MyLinear(paddle.nn.Layer): ... def __init__(self, ... in_features, ... out_features): ... super().__init__() ... self.linear = paddle.nn.Linear(10, 10) ... ... self.back_var = self.create_tensor(name = "linear_tmp_0", dtype=self._dtype) ... ... def forward(self, input): ... out = self.linear(input) ... paddle.assign(out, self.back_var) ... ... return out Nr=rQrRrSrWs rC create_tensorzLayer.create_tensorsT  xx$ 788HH"+$/+;<==H|(6688CC#%2 D   rErwrecurse list[Tensor]cFd||D}|S)a Returns a list of all Parameters from current layer and its sub-layers. Parameters: include_sublayers (bool, optional): Whether to return the parameters of the sublayer. If True, the returned list contains the parameters of the sublayer. Default: True. Returns: list, list of Tensor, a list of Parameters. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) >>> linear = paddle.nn.Linear(1, 1) >>> print(linear.parameters()) [Parameter containing: Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[0.18551230]]), Parameter containing: Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=False, [0.])] cg|]\}}|Sr~r~)rrHrs rC z$Layer.parameters..ks,   5    rErw)named_parametersrrwrs rC parameterszLayer.parametersNs?:   11"32     rEcHgd}t|tjtjfst |t ur||vrt|t tjfrt j|}||_| D] }||_ | dD]\}}| d|| dD]\}}| d||Stdt |z)a Casts all parameters and buffers to dtype and then return the Layer. Parameters: dtype(str|paddle.dtype|numpy.dtype): target data type of layer. If set str, it can be "bool", "bfloat16", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8", "complex64", "complex128". Default: None Returns: Layer, self Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> weight_attr = paddle.ParamAttr(name="weight",initializer=paddle.nn.initializer.Constant(value=1.5)) >>> bias_attr = paddle.ParamAttr(name="bias",initializer=paddle.nn.initializer.Constant(value=2.5)) >>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr).to(device="cpu",dtype="float32") >>> print(linear) Linear(in_features=2, out_features=2, dtype=float32) >>> print(linear.parameters()) [Parameter containing: Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[1.50000000, 1.50000000], [1.50000000, 1.50000000]]), Parameter containing: Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False, [2.50000000, 2.50000000])] >>> linear=linear.astype("int8") >>> print(linear) Linear(in_features=2, out_features=2, dtype=paddle.int8) >>> print(linear.parameters()) >>> # doctest: +SKIP("There are bugs in the `Layer.astype`. For details, refer to the following webpage: https://github.com/PaddlePaddle/Paddle/issues/76614") [Parameter containing: Tensor(shape=[2, 2], dtype=int8, place=Place(cpu), stop_gradient=False, [[1, 1], [1, 1]]), Parameter containing: Tensor(shape=[2], dtype=int8, place=Place(cpu), stop_gradient=False, [2, 2])] >>> # doctest: -SKIP bfloat16float16rfloat64int8int16int32int64uint8 complex64 complex128rTraNdtype value error, must be 'bfloat16', 'float16', 'float32', 'float64', 'int8', 'int16', 'int32', 'int64', 'uint8', 'complex64', 'complex128', 'bool', or paddle.dtype, numpy.dtype, but receive )rrFr nprxrlrr rrrb_to named_bufferstor)rr valid_dtypesrArHrbuffers rCastypez Layer.astypess@^     uv|RX6 7 7 E{{c!!%%%#rx11 D!>> import paddle >>> linear1 = paddle.nn.Linear(10, 3) >>> linear2 = paddle.nn.Linear(3, 10, bias_attr=False) >>> model = paddle.nn.Sequential(linear1, linear2) >>> layer_list = list(model.children()) >>> print(layer_list) [Linear(in_features=10, out_features=3, dtype=float32), Linear(in_features=3, out_features=10, dtype=float32)] N)named_children)rrHrAs rCrzLayer.childrens<.++--  HAuKKKK  rEIterable[tuple[str, Layer]]c#Kt}|jD]&\}}|||vr||||fV'dS)aReturns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself. Yields: (string, Layer): Tuple containing a name and child layer Examples: .. code-block:: python >>> import paddle >>> linear1 = paddle.nn.Linear(10, 3) >>> linear2 = paddle.nn.Linear(3, 10, bias_attr=False) >>> model = paddle.nn.Sequential(linear1, linear2) >>> for prefix, layer in model.named_children(): ... print(prefix, layer) 0 Linear(in_features=10, out_features=3, dtype=float32) 1 Linear(in_features=3, out_features=10, dtype=float32) N)rrrIadd)rmemorbrAs rCr{zLayer.named_childrensm(uu+1133 " "KD% U$%6%6Ek!!! " "rE include_self list[Layer]cFd||D}|S)a Returns a list of sub layers. Parameters: include_self(bool, optional): Whether return self as sublayers. Default: False. Returns: list of Layer, a list of sub layers. Examples: .. code-block:: pycon >>> import paddle >>> class MyLayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self._linear = paddle.nn.Linear(1, 1) ... self._dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... temp = self._linear(input) ... temp = self._dropout(temp) ... return temp >>> mylayer = MyLayer() >>> print(mylayer.sublayers()) [Linear(in_features=1, out_features=1, dtype=float32), Dropout(p=0.5, axis=None, mode=upscale_in_train, inplace=False)] cg|]\}}|Sr~r~)rrHrAs rCr`z#Layer.sublayers..s,   5    rE)rnamed_sublayers)rrrs rCrzLayer.sublayerss8>   00l0KK    rEr7prefixremove_duplicateIterable[tuple[str, Tensor]]c#Ktrtstn t}|r||d|nt |g|g}|D]V\}}|j}|D]5\} } | | |vr |r|| ||rdndz| z} | | fV6WdS)a Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter. Parameters: prefix(str, optional): Prefix to prepend to all parameter names. Default: ''. include_sublayers(bool, optional): Whether include the parameters of sublayers. If True, also include the named parameters from sublayers. Default: True. remove_duplicate(bool, optional): Whether to remove duplicated parameters in the result. Default: True. Yields: (string, Parameter): Tuple of name and Parameter Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) >>> fc1 = paddle.nn.Linear(10, 3) >>> fc2 = paddle.nn.Linear(3, 10, bias_attr=False) >>> model = paddle.nn.Sequential(fc1, fc2) >>> for name, param in model.named_parameters(): ... print(name, param) 0.weight Parameter containing: Tensor(shape=[10, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[ 0.07276392, -0.39791510, -0.66356444], [ 0.02143478, -0.18519843, -0.32485050], [-0.42249614, 0.08450919, -0.66838276], [ 0.38208580, -0.24303678, 0.55127048], [ 0.47745085, 0.62117910, -0.08336520], [-0.28653207, 0.47237599, -0.05868882], [-0.14385653, 0.29945642, 0.12832761], [-0.21237159, 0.38539791, -0.62760031], [ 0.02637231, 0.20621127, 0.43255770], [-0.19984481, -0.26259184, -0.29696006]]) 0.bias Parameter containing: Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=False, [0., 0., 0.]) 1.weight Parameter containing: Tensor(shape=[3, 10], dtype=float32, place=Place(cpu), stop_gradient=False, [[ 0.01985580, -0.40268910, 0.41172385, -0.47249708, -0.09002256, -0.00533628, -0.52048630, 0.62360322, 0.20848787, -0.02033746], [ 0.58281910, 0.12841827, 0.12907702, 0.02325618, -0.07746267, 0.31950659, -0.37924835, -0.59209681, -0.11732036, -0.58378261], [-0.62100595, 0.22293305, 0.28229684, -0.03687060, -0.59323978, 0.08411229, 0.53275704, 0.40431368, 0.03171402, -0.17922515]]) TrrrNr=r7) r#rrrrrrrIr~) rrrwr params_setr layer_prefixsublayerparamskeyrrbs rCrbzLayer.named_parameterssp&-- N0A0C0C NHJJJ !  'D !!1 !    fXv&& '6 " " "L()//11F$ " " U=EZ$7$7#*NN5)))#l'BssCcIEk!!!!  " " "rE layers_setset[Layer] | Nonec# K|t}|r!||vr|r||||fV|jD]3\}}|||rdndz|z}||d||Ed{V4dS)a Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer. The duplicate sublayer will only be yielded once. Parameters: prefix(str, optional): Prefix to prepend to all parameter names. Default: ''. include_self(bool, optional): Whether include the Layer itself. Default: False. layers_set(set, optional): The set to record duplicate sublayers. Default: None. remove_duplicate(bool, optional): Whether to remove duplicated sublayers in the result. Default: True. Yields: (string, Layer): Tuple of name and Layer Examples: .. code-block:: python >>> import paddle >>> fc1 = paddle.nn.Linear(10, 3) >>> fc2 = paddle.nn.Linear(3, 10, bias_attr=False) >>> model = paddle.nn.Sequential(fc1, fc2) >>> for prefix, layer in model.named_sublayers(): ... print(prefix, layer) 0 Linear(in_features=10, out_features=3, dtype=float32) 1 Linear(in_features=3, out_features=10, dtype=float32) >>> l = paddle.nn.Linear(10, 3) >>> model = paddle.nn.Sequential(l, l) >>> for prefix, layer in model.named_sublayers(include_self=True, remove_duplicate=True): ... print(prefix, layer) Sequential( (0): Linear(in_features=10, out_features=3, dtype=float32) (1): Linear(in_features=10, out_features=3, dtype=float32) ) 0 Linear(in_features=10, out_features=3, dtype=float32) >>> l = paddle.nn.Linear(10, 3) >>> model = paddle.nn.Sequential(l, l) >>> for prefix, layer in model.named_sublayers(include_self=True, remove_duplicate=False): ... print(prefix, layer) Sequential( (0): Linear(in_features=10, out_features=3, dtype=float32) (1): Linear(in_features=10, out_features=3, dtype=float32) ) 0 Linear(in_features=10, out_features=3, dtype=float32) 1 Linear(in_features=10, out_features=3, dtype=float32) Nr=r7Trrrr)rr~rrIr)rrrrrrrArs rCrzLayer.named_sublayersksp  J  D 22 %t$$$$,   *0022  JC}!F%:SS;cAL,,#!%!1 -          rEIterator[Layer]c#FK|D] \}}|V dS)z Return an iterator over all modules in the network. Yields: Layer: a layer in the network. N) named_modules)rrHmodules rCmodulesz Layer.moduless<++--  IAvLLLL  rErc<d}|}|||||S)a4 Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer. The duplicate sublayer will only be yielded once. Parameters: memo(set, optional): The set to record duplicate sublayers. Default: None. prefix(str, optional): Prefix to prepend to all parameter names. Default: ''. remove_duplicate(bool, optional): Whether to remove duplicated sublayers in the result. Default: True. Yields: (string, Layer): Tuple of name and Layer Trr)rrrrrrs rCrzLayer.named_moduless7&  ##%!- $   rE persistenttensorchd|jvrtdt|ts%t dt |jdd|vrtd|dkrtdt||r||j vrtd|d |Gt |tj j ks%t d t |jd||j |<|r|j |d S|j |d S) a Registers a tensor as buffer into the layer. `buffer` is a non-trainable tensor and will not be updated by optimizer, but is necessary for evaluation and inference. For example, the mean and variance in BatchNorm layers. The registered buffer is persistable by default, and will be saved into `state_dict` alongside parameters. If set persistable=False, it registers a non-persistable buffer, so that it will not be a part of `state_dict` . Buffers can be accessed as attributes using given names. Parameters: name (string): name of the buffer. The buffer can be accessed from this layer using the given name tensor (Tensor): the tensor to be registered as buffer. persistable (bool): whether the buffer is part of this layer's state_dict. Returns: None Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> linear = paddle.nn.Linear(10, 3) >>> value = np.array([0]).astype("float32") >>> buffer = paddle.to_tensor(value) >>> linear.register_buffer("buf_name", buffer, persistable=True) >>> # get the buffer by attribute. >>> print(linear.buf_name) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.]) r)super().__init__() should be called firstz4The name of buffer should be a string, but received r=zThe name of buffer can not contain `.`, because when you access the newly added buffer in the form of `self.**.**`, it will cause AttributeError.r7z$The name of buffer can not be empty.z attribute '' already exists.Nz>The registered buffer should be a Paddle.Tensor, but received )__dict__rrrlrrxrKeyErrorrBrreagerr rdiscardr~)rrbrrNs rCregister_bufferzLayer.register_bufferslV T] * *HII ID#&& A]tDzzGZ]]] D[[F  RZZABB B T4 AT%>%>@@@@AA A  f9J)J)JiQUV\Q]Q]Qfiii #)DM$  A6>>tDDDDD6::4@@@@@rEcFd||D}|S)a\ Returns a list of all buffers from current layer and its sub-layers. Parameters: include_sublayers(bool, optional): Whether include the buffers of sublayers. If True, also include the buffers from sublayers. Default: True. Returns: list of Tensor, a list of buffers. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> linear = paddle.nn.Linear(10, 3) >>> value = np.array([0]).astype("float32") >>> buffer = paddle.to_tensor(value) >>> linear.register_buffer("buf_name", buffer, persistable=True) >>> print(linear.buffers()) [Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.])] cg|]\}}|Sr~r~)rrHrws rCr`z!Layer.buffers..=s,   6    rEra)rtrcs rCbuffersz Layer.buffers!s?8  !//"30     rEc2|d\}}}||}t||s*t|dz|zdzt ||}||jvrtd|zdz|S)a Return the buffer given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_sublayer`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. Parameters: target(str): The fully-qualified string name of the buffer to look for. Returns: Tensor: The buffer referenced by ``target``. r=r>r?z` is not a buffer)r@rArBrCrDrEr)rr;rGrH buffer_namerJrws rC get_bufferzLayer.get_bufferEs'-&7&7&<&<# Q  ,,sK((   "77+EK k** cl * * {!25H!HII I rEc#0Kt}|r||d|nt|g|g}|D]V\}}|j}|D]5\} } | | |vr |r|| ||rdndz| z} | | fV6WdS)a Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor. Parameters: prefix(str, optional): Prefix to prepend to all buffer names. Default: ''. include_sublayers(bool, optional): Whether include the buffers of sublayers. If True, also include the named buffers from sublayers. Default: True. remove_duplicate(bool, optional): Whether to remove duplicated buffers in the result. Default: True. Yields: (string, Tensor): Tuple of name and tensor Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> fc1 = paddle.nn.Linear(10, 3) >>> buffer1 = paddle.to_tensor(np.array([0]).astype("float32")) >>> # register a tensor as buffer by specific `persistable` >>> fc1.register_buffer("buf_name_1", buffer1, persistable=True) >>> fc2 = paddle.nn.Linear(3, 10) >>> buffer2 = paddle.to_tensor(np.array([1]).astype("float32")) >>> # register a buffer by assigning an attribute with Tensor. >>> # The `persistable` can only be False by this way. >>> fc2.buf_name_2 = buffer2 >>> model = paddle.nn.Sequential(fc1, fc2) >>> # get all named buffers >>> for name, buffer in model.named_buffers(): ... print(name, buffer) 0.buf_name_1 Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.]) 1.buf_name_2 Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.]) TrNr=r7)rrrrrIr~) rrrwr buffers_setrrrrrrwrbs rCrtzLayer.named_bufferscs^ee !  'D !!1 !    fXv&& '6 # # "L('--//G& # # V>V{%:%:#,OOF+++#l'BssCcIFl""""  # # #rE set_to_zerocl|D]}|jr||dS)ad Clear the gradients of all parameters for this layer. Args: set_to_zero (bool, optional): Whether to set the trainable parameters' gradients to zero or None. Default is True. Returns: None Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> value = np.arange(26).reshape(2, 13).astype("float32") >>> a = paddle.to_tensor(value) >>> linear = paddle.nn.Linear(13, 5) >>> adam = paddle.optimizer.Adam(learning_rate=0.01, ... parameters=linear.parameters()) >>> out = linear(a) >>> out.backward() >>> adam.step() >>> linear.clear_gradients() N)rd trainableclear_gradient)rrps rCclear_gradientszLayer.clear_gradientssF8"" . .A{ .  --- . .rEargsrkwargscdSrr~)rrrs rC _build_oncezLayer._build_onces rErBc| d t fd} |S#t$rjD]k\}}|jvr]|vrY |jvr| }n | }|| =#t$r!}t jd|Yd}~cd}~wwxYwlwxYw)Nc jD]\}}| jvrQ| }|At|trt |dkr|\Kt d|d_| }|t|ts|f}| js jid _trTtj j j tjj5 jidddn #1swxYwYn>t# j j 5 jidddn #1swxYwY jD]L\}}| jvr|| jvr| }n | }||MS)NzPforward pre-hook must return None or a tuple of (new_args, new_kwargs), but got r=T)rrIrrrr: RuntimeErrorrrr*r RecordEventrrTracerEventTypeForwardforwardr$rrr~r) hook_idforward_pre_hookargs_kwargs_result hook_resultforward_post_hookcalled_always_called_hooksrBrrMrs rCinnerz'Layer._dygraph_call_func..inners.2-D-J-J-L-L - -))dFFF)9)9$)O)O&)5&'95AA  #$6 7 71 < <-?NFFF".!\FX!\!\!\##6#3"24"@"@K".)+u==9+6.K!,; #  &3F333" !! >)N+X-E-M>>+dlF=f==G>>>>>>>>>>>>>>> !!899>>*dlF=f==G>>>>>>>>>>>>>>>/3.F.L.L.N.N * ***dDDD.227;;;dGGG"3"3ffg##KK#4"3D&'"J"JK*)GNs$:DDD9EEEzuforward hook with ``always_call=True`` raised an exception that was silenced as another error was raised in forward: )r ExceptionrrIrrr>r?) rrBrrrrrerrMs ``` @@rC_dygraph_call_funczLayer._dygraph_call_funcsa%(UU"5 5 5 5 5 5 5 5 5 n 577N   .2.F.L.L.N.N ! !**tEEE%???!"d&OOO*;*; $ffg++KK+<*; $fg++K'2&1G$!!! _YZ__! ! 1 s3 *5B; )B  B; B5B0+B;0B55B;ctsd|js]|jsV|jjt jus|jr7tr)trtr |j |i|S|j |i|Sr) rrrrrr6rr"r*rrrrrBrs rC__call__zLayer.__call__ s"$$ >, >- >+u/@@@DK@!!A%''A+A+C+CA 4<2622 2*4*F=f== =rEct)z Defines the computation performed at every call. Should be overridden by all subclasses. Parameters: *inputs(tuple): unpacked tuple arguments **kwargs(dict): unpacked dict arguments )NotImplementedErrorrs rCrz Layer.forward-s "!rEc td)Nz"Layer shouldn't implement backward)r)rrBs rCbackwardzLayer.backward8s=>>>rErcLt|ts|J||j|<|S)a+ Adds a sub Layer instance. Added sublayer can be accessed by self.name Parameters: name(str): name of this sublayer. sublayer(Layer): an instance of Layer. Returns: Layer, the sublayer passed in. Examples: .. code-block:: python >>> import paddle >>> class MySequential(paddle.nn.Layer): ... def __init__(self, *layers): ... super().__init__() ... if len(layers) > 0 and isinstance(layers[0], tuple): ... for name, layer in layers: ... self.add_sublayer(name, layer) ... else: ... for idx, layer in enumerate(layers): ... self.add_sublayer(str(idx), layer) ... ... def forward(self, input): ... for layer in self._sub_layers.values(): ... input = layer(input) ... return input ... >>> fc1 = paddle.nn.Linear(10, 3) >>> fc2 = paddle.nn.Linear(3, 10, bias_attr=False) >>> model = MySequential(fc1, fc2) >>> for prefix, layer in model.named_sublayers(): ... print(prefix, layer) 0 Linear(in_features=10, out_features=3, dtype=float32) 1 Linear(in_features=3, out_features=10, dtype=float32) )rr6r)rrbrs rC add_sublayerzLayer.add_sublayer;s2R(E**>h.>.>>!)rEcJ|dkr|S|d}|}|D]}t||s*t|dz|zdzt ||}t |t jjstd|zdz|S)a Return the submodule given by ``target`` if it exists, otherwise throw an error. Parameters: target(str): The fully-qualified string name of the submodule to look for. Returns: Layer: The sublayer referenced by ``target``. r7r=r>r?` is not an nn.Layer) rirBrCrDrErrFrr6)rr;atomsrJitems rCrAzLayer.get_sublayeris R<<K!<<,,# J JD3%% $MMOO&;;dBSH#t$$Cc69?33 J$S4Z2H%HIII J rErArstrictc|dkrtd|d}t|tjjs"tddt |zt|dkr|}n2d|dd}| |}|rFt||ds0t| d z|dzd zt||drPt||d}t|tjjstd |dzd zt||d|dS) aD Set the sublayer given by ``target`` if it exists, otherwise throw an error. Parameters: target(str): The fully-qualified string name of the sublayer to look for. layer(Layer): The layer to set the sublayer to. strict(bool): If ``False``, the method will replace an existing sublayer or create a new sublayer if the parent module exists. If ``True``, the method will only attempt to replace an existing sublayer and throw an error if the sublayer doesn't already exist. r7z.Cannot set the sublayer without a target name!r=z`modulez` is not an nn.Layer, found rgNrr>r?r)rrirrFrr6rxr:rmrArBrCrDrEsetattr)rr;rArrparent parent_keyrJs rC set_sublayerzLayer.set_sublayers} R<<MNN N!<<,,%11 !MU !M!MM  u::??&*FF%*--J&&z22F  '&%)44    ""%::U2YFL  659 % % O&%),,Cc69?33 O$S59_7M%MNNNb 5)))))rE Layer | Nonec2|||dS)z Adds a sub layer instance. Added layer can be accessed by self.name Parameters: name(str): name of this sublayer. layer(Layer): an instance of Layer. Returns: None N)r)rrbrs rC add_modulezLayer.add_modules  $'''''rE parametercd|jvrtdt|ts%t dt |jdd|vrtd|dkrtdt||r||j vrtd|d |Pt|tj tj jfs%t d t |jd| d |j |<t|jd krF|j|jvsJd |jd||j|j||j |<|S)aAdds a Parameter instance. Added parameter can be accessed by self.name Parameters: name(str): name of this sublayer. parameter(Parameter): an instance of Parameter. Returns: Parameter, the parameter passed in. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) >>> class MyLayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self._linear = paddle.nn.Linear(1, 1) ... w_tmp = self.create_parameter([1,1]) ... self.add_parameter("w_tmp", w_tmp) ... ... def forward(self, input): ... return self._linear(input) ... >>> mylayer = MyLayer() >>> for name, param in mylayer.named_parameters(): ... print(name, param) w_tmp Parameter containing: Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[-1.01448846]]) _linear.weight Parameter containing: Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=False, [[0.18551230]]) _linear.bias Parameter containing: Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=False, [0.]) rz,super().__init__() should be called firstly.z7The name of parameter should be a string, but received r=zThe name of parameter can not contain `.`, because when you access the newly added parameter in the form of `self.**.**`, it will cause AttributeError.r7z'The name of parameter can not be empty.zThe parameter 'rNz>The parameter to be added should be a Parameter, but received r&Parameter not found, Can't not find [  ] in state_dict)rrrrlrrxrrrBrrrrFpirValuer:rrb set_value)rrbrs rC add_parameterzLayer.add_parametersN  - -MNN ND#&& /`$t**J]``` D[[F  RZZDEE E T4  /T1A%A%ADTDDDEE E  ":  +VZ-=>, , "lQUV_Q`Q`Qilll  )- &4())A-- ~)>>>>]Y^]]]?>>##D$9).$IJJJ%.D T "rErParameter | Nonec2|||dS)a Adds a Parameter instance. Added parameter can be accessed by self.name Parameters: name(str): name of this submodule. parameter(Optional[Parameter]): an instance of Parameter. Returns: None N)rrs rCregister_parameterzLayer.register_parameter s  4'''''rEcfd}t|ts$tdt|jj||dsFt}tj j dksJ|gj _ |ds t}tjdkr!j|jdtj j dksJj j |d Sd S) aY Add customized attribute while append_op. In case of quantization, we want to save some attributes into op_desc while exporting inference model by @to_static. Arguments: attrs(dict): customized attributes that will be added into op_descs. NOTE: The interface is only exposed to developers. c|rjnj}|rtnt}d}|r(t t |}|||k}|S)NF)rrrDrRnextreversed) is_pre_hook layers_hookscandidate_hookalready_registeredlast_keyrs rCis_already_registeredz2Layer._set_op_attrs..is_already_registered st.''- 7++6  "'  N 6 677%1(%;~%M"% %rEz)attrs should be type(dict), but received T)rrFrgr%N)rrrrxrrHr r,rDr:r8rKr)rRrr'rrk)rrrpre_hook_helperpost_hook_helpers` rC _set_op_attrszLayer._set_op_attrss & & & & &&%&& RDKK>1   4+,,q00(44$-E5t(.//14444   # * *+; < < < < < = =rEdict[str, Any]c|jSr)rrs rC __getstate__zLayer.__getstate__Qs }rEstatec:|j|dSr)rr )rrs rC __setstate__zLayer.__setstate__Ts U#####rEcd|jvrK|jd}||jvr5trt|j|S|j|Sd|jvr#|jd}||jvr |j|Sd|jvr<|jd}||vr+trt||S||St ||S)Nrrr)rrrrrobject__getattribute__)rrbrrrs rC __getattr__zLayer.__getattr__Ws DM ) )- 6Kt'''$&&J1$2B42HIII'-- DM ) )- 6Kt''''--  & &}Z0Hx$&&B1(4.AAA~%&&tT222rEr c 0 fd}t|tjjjjr%t |||_dSttt|dtrt |||j dd}t|tjr|t!dt#|jdkrF|j|jvsJd|jd||j|j||j |j|j||<dSt|tjjr^|dkr4|t!d||j |j|j||<dS|5|vr1|(t5dd t|jd d|<dS|j d d}t|t8r4|t!d||j |j|j||<dS|5|vr1|(t5d d t|jd d|<dS|j dd}t|t<jj rz|t!d||j |j|j|jvr|j!"|jstGj$dz|_||<dS| |vrt|tj%kst|tjjrddlm&}tOr!|tQdddd|0tt|t<jj kr|||<dS||t|dS|(t5ddt|jd d|<dSt ||dS)Nc |D] }|vr|= dSrr~)dictsrrbs rC_remove_if_existz+Layer.__setattr__.._remove_if_existks-  199$  rErrrrrzbuiltin.parameterzassignment to parameter 'z0' should be of type Parameter or None, but got ''rzassignment to sublayer 'z,' should be of type Layer or None, but got 'r _buffers_)assignzIn Dy2stat, self.z is a buffer and self.z0 is not allowed to be set to Variable when self.z is None.zassignment to buffers 'z7' should be of type core.DenseTensor or None, but got '))rrFjit dy2staticprogram_translatorStaticFunctionr __setattr__ _patched_namerErxpropertyrrYrrrr:rrbrrrrrget_defining_oprrr6rrrr rr~rrVariablerrr)rrbr rrlayersrrs ` rCr zLayer.__setattr__jsd      6:':I      tT5 1 1 1"&E  F gd4jj$55x @ @ 2   tT5 1 1 1""=$77 eY0 1 1] :~ !LMMM4())A--zT%::::YUZYYY;:: 5ej ABBB  T]DM4;K L L L F4LLL ufj. / /Q :%%'',,..2EEE~ !LMMM  T]DM4;K L L L F4LLL  DFNN }}}fjkpfqfqfz}}} F4LLL]&&}d;;F%''A :>$C! 0@$-PPP$t #$#|4||eijoepepey||| $t =,,Z>>eTZ%6771:'(G%$ t'79I 4=00>BB4HHH :N%0%9+:L%M%M %*HTNNN)dh.>.>E{{i&888Jvz/==8211111 -.. ?8D>3I".!_D!_!_PT!_!_OS!_!_!_## %TN2#GD$$7$788DJ>>>>*'KdKKswx}s~s~tHKKK *.&&tT599999rEc||jvr |j|=dS||jvr |j|=dS||jvr$|j|=|j|dSt ||dSr)rrrrrr __delattr__)rrbs rCrzLayer.__delattr__s 4# # # &&& T% % % &&& T] " " d#  2 : :4 @ @ @ @ @   tT * * * * *rE list[str]czt|j}t|j}t|j}t|j}t|j}||z|z|z|z}|S)a Return a list. Get all parameters, buffers(non-parameter tensors), sublayers, method and attr of Layer. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> class Mylayer(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self.linear1 = paddle.nn.Linear(10, 10) ... self.linear2 = paddle.nn.Linear(5, 5) ... self.conv2d = paddle.nn.Conv2D(3, 2, 3) ... self.embedding = paddle.nn.Embedding(128, 16) ... self.h_0 = paddle.to_tensor(np.zeros([10, 10]).astype('float32')) ... >>> mylayer = Mylayer() >>> print(dir(mylayer)) ['__call__', '__class__', '__delattr__', '__dict__', ..., 'training'] )dirrrrkeysrrr)rmethodrrdrrrs rC__dir__z Layer.__dir__s,T^$$T]''))**$*//1122 )..0011 t}))++,,~ *Y6@ rEcdS)zs Extra representation of this layer, you can have custom implementation of your own layer. r7r~rs rC extra_reprzLayer.extra_repr s rrEc$g}|}|d}g}|jD]B\}}t |}t |d}|d|zdz|zC|jjdz}|rPt|dkr|dd |zdzz }nt|dkr ||dz }|r|dd |zdzz }|dz }|S) Nrfr(z): rgz r)) rrirrIreprrsrkrrr:rm)r extra_linesrsublayer_linesrbrA sublayer_str final_strs rCrzLayer.__repr__ s7 __&&  &&t,, +1133 E EKD%;;L%lA66L  ! !#*u"4|"C D D D DN+c1  ,;!##Vfkk+&>&>>EE [!!Q&&[^+  E &++n"="==D DIS rE_StateDictHookcLt|j}||j|j<|Sr)rrr)rr"r(s rCregister_state_dict_hookzLayer.register_state_dict_hook s,.d.DEE>B1:;!!rE destination_StateDict | Nonestructured_name_prefix _StateDictc\|t}|jD]\}}|||||z<|jD]\}}|||jvr||||z<|r>|jD]$\}}||||||zdz%|S)z The difference from state_dict() is that state_dict_hook will not be called, but the original types of parameters and buffers will be maintained. Nr=)rrrIrrr_obtain_parameters_buffers) rr&rwr(rbdatarw layer_name layer_items rCr+z Layer._obtain_parameters_buffers' s  %--K*0022 B BJD$=A 2T9: M//11 D DLD&" FFF=C 2T9:  *.*:*@*@*B*B  & J)99#).;cA rEinclude_non_persistable_bufferuse_hook keep_varsc Z|t}|jD]%\}}||r|n||||z<&|jD]Q\}} |s*| '||jvr|r| n| |||z<1| |r| n| |||z<R|rA|jD]'\} } | | |||| zdz|||(|r+|j D]} | |} | | }|S)a| Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict Parameters: destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None. include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True. include_non_persistable_buffer(bool, optional): If true, include non persistable buffers of current layer and its sub-layers, it is used in pure fp16 and jit.save. Default: False. use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True. keep_vars(bool, optional) : If false, the returned tensors in the state dict are detached from autograd. Default: True. Nr=) rrrIdetachrrr_state_dict_implrvalues)rr&rwr(r/r0r1rbr,rwr-r.state_dict_hookrs rCr4zLayer._state_dict_implH s(  %--K*0022  JD$%8DD4;;==2T9:!M//11  LD&1 &D$JJJ#,@ 6 =>%"+@ 6 =>  *.*:*@*@*B*B  & J)//#).;cA6 !   .#'#9#@#@#B#B . .-ok:: *"-KrEc8||||d||S)a" Get all parameters and buffers of current layer and its sub-layers. And set them into a dict Parameters: destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None. include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True. use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True. keep_vars(bool, optional) : If false, the returned tensors in the state dict are detached from autograd. Default: True. Returns: dict, a dict contains all the parameters and persistable buffers. Examples: .. code-block:: python >>> import paddle >>> emb = paddle.nn.Embedding(10, 10) >>> state_dict = emb.to_static_state_dict() >>> paddle.save( state_dict, "paddle_dy.pdparams") Tr&rwr(r/r0r1)r4rr&rwr(r0r1s rCto_static_state_dictzLayer.to_static_state_dict s4@$$#/#9+/ %   rEcdSrr~r9s rC state_dictzLayer.state_dict s SrE.)rr1cdSrr~)rr&rr1s rCr<zLayer.state_dict s SrEcdSrr~)rrr1s rCr<zLayer.state_dict s SrEr&rr1cdSrr~)rr&rr1rs rCr<zLayer.state_dict s SrEc t|}dfd }|dkrt|dtsdvrgd }tt |t|D]}||||||d d d dd dd ddS|j|iS)a" Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict Parameters: destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None. include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True. use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True. keep_vars(bool, optional) : If false, the returned tensors in the state dict are detached from autograd. Default: True. Returns: dict: a dict contains all the parameters and persistable buffers. Examples: .. code-block:: python >>> import paddle >>> emb = paddle.nn.Embedding(10, 10) >>> state_dict = emb.state_dict() >>> paddle.save(state_dict, "paddle_dy.pdparams") rrlr rc@|vrtd|d||<dS)Nz"got multiple values for argument 'r)r)rr rs rCsafe_set_paramz(Layer.state_dict..safe_set_param s3f}} KS K K KLLLF3KKKrErrgrr?r&NTr7Fr1r8)rrlr r)r:rrlrminr4rY)rrrlen_argsrCbase_param_keysrqs ` rCr<zLayer.state_dict s0t99      MMja#66M   DDDOS3+?+?@@AA @ @s3T#Y????(("JJ}d;;"&'-zz(B'?'?/4 **[%88 ) %t$d5f555rEr'c(i}|dd}t|d|}|||jD]7\}}|0|||d}||8|S)aRecursively builds a sharded state dictionary for the model and its sub-layers. Args: structured_name_prefix: Prefix to prepend to all tensor names for hierarchical naming. Returns: Dictionary mapping tensor names to ShardedWeight. The dictionary contains both the current layer's parameters and all sub-layer parameters. r7F)r(rwN)r< shard_rulesrr=)r()r<r(r rrIsharded_state_dict)rr(rIr<current_sharded_dictr-r. sub_shardeds rCrIzLayer.sharded_state_dict s __#%#%  8!)    !!"6777'+&6&<&<&>&> 7 7 "J %(;;.D+Sj+S+S+S< #))+666!!rE aoa_configdict[str:list[str]] | Nonec Fddlm}|||fi|S)aI Returns an iterator over the full, unsharded model parameters. The output parameters can be customized using the `aoa_config` argument. Args: sharded_state_dict (ShardedStateDict): The state dict containing parameter shards local to the current process. aoa_config (dict[str, list[str]] | None, optional): AoA (Almost AllReduce) configuration. Default is None. kwargs: Optional keyword arguments: - h_group: The horizontal communication group. If using group communication, both h_group and v_group must be provided. - v_group: The vertical communication group. - process_group: The communication group in single-group setups (when h_group and v_group are not used). - num_splits (int): The number of splits to divide the parameters. - shard_idx (int): The index of the split handled by the current process. Default is 0. - memory_growth_threshold (int): The memory threshold (in bytes) for controlling memory growth during parameter assembly. Default is 8 * (2 ** 30), i.e., 8GB. Returns: Iterator: An iterator over the full, unsharded model parameters, optionally filtered and customized according to `aoa_config`. r) full_param)1paddle.distributed.flex_checkpoint.dcp.full_paramrOrI)rrLrrOs rCfullz Layer.full& sH>      z$1133ZJJ6JJJrEr<use_structured_nametuple[list[str], list[str]]cgtg}fd}g}|dD]q\}}|r|n|j} |||} || 3#t $r2} t jd|dt| zYd} ~ jd} ~ wwxYw D]}|vr||tr|D]\}} | | n'd} trwttjjj} tjjjd|Dt-| nLtt/j} t1jd |Dt-| |D]\}} | || n=#t $r}t d d}~wt4$r}t d d}~wwxYw|fS) a Set parameters and persistable buffers from state_dict. All the parameters and buffers will be reset by the tensor in the state_dict Parameters: state_dict(dict) : Dict contains all the parameters and persistable buffers. use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter or buffer name as key. Default: True. Returns: missing_keys(list):A list of str containing the missing keys unexpected_keys(list):A list of str containing the unexpected keys Examples: .. code-block:: python >>> import paddle >>> emb = paddle.nn.Embedding(10, 10) >>> state_dict = emb.state_dict() >>> paddle.save(state_dict, "paddle_dy.pdparams") >>> para_state_dict = paddle.load("paddle_dy.pdparams") >>> emb.set_state_dict(para_state_dict) c 4|d}|'|t|dt|tt frt |t |krF|t|dt |dt ||||fStj |j r| n|j }t |t |j krL|t|dt |dt |j d|||fS)Nz# is not found in the provided dict.z receives the length of z, but the expected shape is z receives a shape r=) rYrkrrrrr:r~inspectismethodr-)rrr state_shape match_keysrr<s rC _check_matchz*Layer.set_state_dict.._check_matchm sNN3--E}##C((( C!L!L!LMMM%$.. $u::U++ '',,,$BBE BB58ZZBB NN3''' %<'' 44%EKKMMM  $$U[(9(999 '',,,$uu${2C2Cuuaefkfqararuuus###e|#rEF)r0zSkip loading for z. Ncpt|j}|}|rt j}n|rt j }n| rbt j }| |t j |}n |rt j }| |t jt"jdd|}n`t j }| |t j|}|||dS)N:r)rfind_varrb get_tensor_place is_cpu_placerCPUPlaceis_cuda_pinned_placeCUDAPinnedPlace is_xpu_placePlace set_placeXPUPlace xpu_device_idis_custom_place CustomPlacerFr4 get_devicericustom_device_id CUDAPlace gpu_device_idr)rndarraytrplaces rC_set_varz&Layer.set_state_dict.._set_var s NN++CH55@@BBHHJJ>>##> MOOEE++--> 022EE^^%%> AKK +++ M!//*;*;< AKK +++ , 002288==a@**,,EE  AKK +++ N1??+<+<==Egu%%%%%rEcg|]\}}|Sr~r~rrrs rCr`z(Layer.set_state_dict.. GGG<5%GGGrEcg|]\}}|Sr~r~rts rCr`z(Layer.set_state_dict.. rurEzThis error might happens in dy2static, while calling 'set_state_dict' dynamically in 'forward', which is not supported. If you only need call 'set_state_dict' once, move it to '__init__'.)rr4rIrbrkrr>r?rlrr"rr#rrFbaser_current_expected_place__default_executor libpaddlercreate_loaded_parameterr _get_devicer_create_loaded_parameterr)rr<rRrrZmatched_param_staterrkey_name match_reserrrrrexecutorrrYrs ` @@rCset_state_dictzLayer.set_state_dictK s< UU  $ $ $ $ $ $ $<!///??EEGG F FJC1AssuzH F(L599 #**95555 F F F 9#999CHHDEEEEEEEE F??$$ , ,C*$$&&s+++   8  3 ' ' u&&&& ' & & &0 ==' -FFHH  'K)-EEGG3FGGG$  ( 66HH1GG3FGGG$  %8++LE5HUE****+    R    R  _,,s=!A:: B6(B11B6C%H H: H H:&H55H:Mapping[str, Any]rc hg}||d\}}|rt|dkrF|dddd|Dt|dkrF|dddd|Dt|dkr@t d |jjd |t||S) a Copy parameters and buffers from :attr:`state_dict` into this module and its descendants. If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. Parameters: state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): whether to strictly enforce that the keys in :attr:`state_dict` match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` assign (bool, optional): When set to ``False``, the properties of the tensors in the current module are preserved whereas setting it to ``True`` preserves properties of the Tensors in the state dict. The only exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter` for which the value from the module is preserved. Default: ``False`` Returns: ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: * ``missing_keys`` is a list of str containing any keys that are expected by this module but missing from the provided ``state_dict``. * ``unexpected_keys`` is a list of str containing the keys that are not expected by this module but present in the provided ``state_dict``. T)rRrz%Unexpected key(s) in state_dict: {}. z, c3"K|] }d|dV dS"Nr~rks rCrz(Layer.load_state_dict.. s*!D!Dq(a(((!D!D!D!D!D!DrEz"Missing key(s) in state_dict: {}. c3"K|] }d|dV dSrr~rs rCrz(Layer.load_state_dict.. s*!A!Aq(a(((!A!A!A!A!A!ArEz*Error(s) in loading state_dict for {}: {}z ) rr:insertformatrmrrrr})rr<rr error_msgsrrs rCload_state_dictzLayer.load_state_dict sX@!# (,(;(; D)<) ) % o  ?##a''!!;BB !D!DO!D!D!DDD <  1$$!!8?? !A!AL!A!A!AAA z??Q  >EEN+V[[-D-D  !???rEblocking non_blockingc8|||||ddS)a Cast the parameters and buffers of Layer by the give device, dtype and blocking. Parameters: device(str|paddle.CPUPlace()|paddle.CUDAPlace()|paddle.CUDAPinnedPlace()|paddle.XPUPlace()|None, optional): The device of the Layer which want to be stored. If None, the device is the same with the original Tensor. If device is string, it can be ``cpu``, ``gpu:x`` and ``xpu:x``, where ``x`` is the index of the GPUs or XPUs. Default: None. dtype(str|numpy.dtype|paddle.dtype|None, optional): The type of the data. If None, the dtype is the same with the original Tensor. Default: None. blocking(bool|None, optional): If False and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. If None, the blocking is set True. Default: None. non_blocking(bool|None, optional): If True and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. If None, the non_blocking is set False. Default: None. Returns: self Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> linear=paddle.nn.Linear(2, 2) >>> linear.weight >>> print(linear.weight) Parameter containing: Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False, [[ 0.89611185, 0.04935038], [-0.58883440, 0.99266374]]) >>> linear.to(dtype='float64') >>> linear.weight >>> print(linear.weight) Parameter containing: Tensor(shape=[2, 2], dtype=float64, place=Place(gpu:0), stop_gradient=False, [[ 0.89611185, 0.04935038], [-0.58883440, 0.99266374]]) >>> linear.to(device='cpu') >>> linear.weight >>> print(linear.weight) Parameter containing: Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [[ 0.89611185, 0.04935038], [-0.58883440, 0.99266374]]) >>> # doctest: +REQUIRES(env:GPU) >>> linear.to(device=paddle.CUDAPinnedPlace(), blocking=False) >>> linear.weight >>> print(linear.weight) Parameter containing: Tensor(shape=[2, 2], dtype=float64, place=Place(gpu_pinned), stop_gradient=False, [[ 0.89611185, 0.04935038], [-0.58883440, 0.99266374]]) TF)r4r rrrwrvry)rr4r rrs rCruzLayer.to s2D}}%"    rEfuncICallable[[Tensor, PlaceLike | None, DTypeLike | None, bool | None], None]c*|r0|D]}|||||||jD]\}}|t 5|||||} dddn #1swxYwY|jFt 5|||||} dddn #1swxYwY|jD]\}} | || ||||j|<||_dSr) r_applyrrIrgrad _grad_ivarrr) rrr4r rrwrArr param_applied grad_appliedbufs rCrz Layer._apply[ s  O O O T65(WF =GE%'/4=!ABB 6.u55E 7   ! ! eT]33)%00  +K88J'!'""Z/36:cACG !$(#<#>#> #&:::O%%x $$&&--////F  &,!6!6&;;&,;OO 4 4!;;U;33 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4H  hn&<&.transform s: f&>q&A&A ??1feX>> >rEignore)category)rrlrFr4_convert_to_placerrerrxrrr>catch_warningsfilterwarnings UserWarningrr)rr4r rrrwrvrs` ` rCryzLayer._to_impl s F N  $K  &#&& 88@@   AjnoujvjvjAA  HHh--  G  -   LLlD11  K  1!)BLB55d ? ? ? ? ? ?  $ & & O O  #H{ C C C C KK 65(.layer_transE s/"%4rErq) rrFr rrrxrlrr rr)rrrvrs ` rCrxz Layer.type" s     x&,!9 : : H~~$$L(((S"(O44 J$?II      ::k** *Te** rEc@|tjS)z Casts all floating point parameters and buffers to ``double`` datatype. Returns: Module: self )rxrFrirs rCdoublez Layer.doubleQ yy(((rEc@|tjS)z Casts all floating point parameters and buffers to ``half`` datatype. Returns: Module: self )rxrFrhrs rChalfz Layer.halfZ rrErzLayer | Sequence[Layer] | NonecgnttrgnQtttfrtn%t dtjdfd}||S)a Casts all floating point parameters and buffers to ``float`` data type. Parameters: excluded_layers(nn.Layer|list|tuple|None, optional): Specify the layers that need to be kept original data type. if excluded_layers is None, casts all floating point parameters and buffers. Default: None. Returns: Layer: self Examples: .. code-block:: pycon >>> import paddle >>> class Model(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self.linear = paddle.nn.Linear(1, 1) ... self.dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... out = self.linear(input) ... out = self.dropout(out) ... return out >>> model = Model() >>> model.float() Model( (linear): Linear(in_features=1, out_features=1, dtype=paddle.float32) (dropout): Dropout(p=0.5, axis=None, mode=upscale_in_train, inplace=False) ) N9excluded_layers should be type nn.Layer or list, but got r=c>t|tjdSr)r{rFrrArzs rCrz Layer.float..layer_trans  ufno F F F F FrE)rrxrrrrrrrzrs ` rCfloatz Layer.floatc sF!0 7""_ ot , , ./OO $ 7 7 "?33OOmDQ`LaLaLjmmm  G G G G Gzz+&&&rEctjdurtjd|S t jgnttrgnQtttfrtn%tdtj dfd}| |S)a Casts all floating point parameters and buffers to ``float16`` data type. .. note:: ``nn.BatchNorm`` does not support ``bfloat16`` weights, so it would not be converted by default. Parameters: excluded_layers(nn.Layer|list|tuple|None, optional): Specify the layers that need to be kept original data type. if excluded_layers is None, casts all floating point parameters and buffers except ``nn.BatchNorm``. Default: None. Returns: Layer: self Examples: .. code-block:: python >>> # doctest: +SKIP('Paddle compiled by the user does not support float16, so keep original data type.') >>> import paddle >>> class Model(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self.linear = paddle.nn.Linear(1, 1) ... self.dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... out = self.linear(input) ... out = self.dropout(out) ... return out ... >>> model = Model() >>> model.float16() Model( (linear): Linear(in_features=1, out_features=1, dtype=float32) (dropout): Dropout(p=0.5, axis=None, mode=upscale_in_train) ) FzQPaddle compiled by the user does not support float16, so keep original data type.Nrr=c>t|tjdSr)r{rFrhrs rCrz"Layer.float16..layer_trans rrE)rFampis_float16_supportedr>r?r BatchNormrrxrrrrrrs ` rCrhz Layer.float16 sT : * * , , 5 5 Mc   K.5R\NN?  ot , , ./OO $ 7 7 "?33OOmDQ`LaLaLjmmm  G G G G Gzz+&&&rEctjdurtjd|S t jgnttrgnQtttfrtn%tdtj dfd}| |S)a/ Casts all floating point parameters and buffers to ``bfloat16`` data type. .. note:: ``nn.BatchNorm`` does not support ``bfloat16`` weights, so it would not be converted by default. Parameters: excluded_layers(nn.Layer|list|tuple|None, optional): Specify the layers that need to be kept original data type. if excluded_layers is None, casts all floating point parameters and buffers except ``nn.BatchNorm``. Default: None. Returns: Layer: self Examples: .. code-block:: python >>> # doctest: +SKIP('bfloat need V100 compile') >>> import paddle >>> class Model(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self.linear = paddle.nn.Linear(1, 1) ... self.dropout = paddle.nn.Dropout(p=0.5) ... ... def forward(self, input): ... out = self.linear(input) ... out = self.dropout(out) ... return out ... >>> model = Model() >>> model.bfloat16() >>> #UserWarning: Paddle compiled by the user does not support bfloat16, so keep original data type. Model( (linear): Linear(in_features=1, out_features=1, dtype=float32) (dropout): Dropout(p=0.5, axis=None, mode=upscale_in_train) ) FzRPaddle compiled by the user does not support bfloat16, so keep original data type.Nrr=c>t|tjdSr)r{rFrgrs rCrz#Layer.bfloat16..layer_trans s ufo G G G G GrE)rFris_bfloat16_supportedr>r?rrrrxrrrrrrs ` rCrgzLayer.bfloat16 sV : + + - - 6 6 Md   K.5R\NN?  ot , , ./OO $ 7 7 "?33OOmDQ`LaLaLjmmm  H H H H Hzz+&&&rEint | PlaceLike | Nonec\|1tjtj}ndt |t rtj|}n:t |tjrnt dt|||S)a Move all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing the optimizer if the layer will live on GPU while being optimized. Parameters: device(int, optional): if specified, all parameters will be copied to that device. Returns: Layer: self Nz2device must be int, paddle.CUDAPlace or None, got r8) rFrmcudacurrent_devicerrrrxryrr4s rCrz Layer.cuda s >%fk&@&@&B&BCCFF  $ $ %f--FF  0 1 1  ST&\\SS }}F}+++rEc$|tjd}ndt|trtj|}n:t|tjrnt dt |||S)a Move all model parameters and buffers to the XPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the layer will live on XPU while being optimized. Parameters: device(int, optional): if specified, all parameters will be copied to that device. Returns: Layer: self Nrz1device must be int, paddle.XPUPlace or None, got r8)rFrgrrrrxryrs rCxpuz Layer.xpu6 s >_Q''FF  $ $ _V,,FF  0 0  RDLLRR }}F}+++rEcP|tjS)zm Move all model parameters and buffers to the CPU. Returns: Layer: self r8)ryrFrars rCcpuz Layer.cpuQ s }}FO$5$5}666rEc td)NzMReached a code path in Module.get_extra_state() that should never be called. )rrs rCget_extra_statezLayer.get_extra_stateZ s [   rE requires_gradcD|D] }| |_ |S)a7 Change if autograd should record operations on parameters in this layer. Parameters: requires_grad (bool): whether autograd should record operations on parameters in this layer. Default: ``True``. Returns: Layer: self )rdr)rrrs rCrequires_grad_zLayer.requires_grad__ s."" 0 0A"//AOO rE set_to_nonecn|D]}|j||  dS)z Reset gradients of all model parameters. Parameters: set_to_none (bool): instead of setting to zero, set the grads to None. Currently, set_to_none=True is not fully supported. N)rdrr)rrrs rC zero_gradzLayer.zero_gradn sF"" 2 2Av!  [111 2 2rEc|jjSr)rrrs rCrDzLayer._get_namez s ~&&rE)Nr)rrr r1rr)T)rrrr )rr )rrrr r) r"r#rrr rr!rrr)r"r*rrr rrr)NNFNN)r-r4r.r/r r0r1rr2r3r4r5rr )r;rlrr)NNN)rbrrNrOr r0rr )rwrrr]r)r r0rr )rry)rr|)F)rrrr)r7TT)rrlrwrrrrr)r7FNT) rrlrrrrrrrr|)rr)Nr7T)rrrrlrr)rbrlrr rNrrr)r;rlrr )rrrr)rrrrrr)rBrrrrr)rBrrr)rbrlrr6rr6)r;rlrr6)r;rlrAr6rrrr)rbrlrrrr)rbrlrr rr )rbrlrrrr)rr)rrrr)rbrlrr)rbrlr rrr)rbrlrr)rr)r"r#rr)NTr7)r&r'rwrr(rlrr))NTr7FTT)r&r'rwrr(rlr/rr0rr1rrr))NTr7TT) r&r'rwrr(rlr0rr1rrr))r&r)rrlr1rrr))rrlr1rrr))rr))rrrrrr))r7)r(rlrr')rLrM)r<r)rRrrrS)TF)r<rrrrr)NNNN) r4r5r r0rrOrrOrr ) rrr4r5r r0rrOrwrrr) rpr r4r5r r0rrOrr )NNNNTF) r4r5r r0rrOrrOrwrrvr)rr)rrrr )rzrrr )r4rrr )rr)rrrr )rrrr)\rrrrrrr rsetterr rrrrr)register_forward_hookr,r9rr+rYr[r,rdrxrr{rrbrrrrrrrtrrrrrrrrAr get_submodule set_submodulerregister_modulerrrrrrr rrrrr%r+r4r:r r<rIrQrdeprecate_stat_dictrrrurrryrset_dict load_dictrxrrrrhrgrrrrrrrDr~rErCr6r6sC 44lNNNAJ/-/-/-/-/-b  X _''_' 66X6!'==('=>>>>>@4444l8888t@!! U"U"U"U"U"U"p7 ! N"N"N"N"N"N"f&*"&26#'< < < < < |2Z19 #'"& 5 5 5 5  5 t #'"& 6 6 6 6 6 p_)9566""""76"HPPPPPd4""""4#####J_)9566"&!% J"J"J"J"76J"\"(,!% GGGGGR    #'!%      8_m\233=ACACACACA43CAJ_)9566!!!!76!F<_)9566"&!% @#@#@#@#76@#D.....@    UUUUn > > > > " " " "????,,,,\:_gx())8=$*$*$*$**)$*L!M M ( ( ( (!OIIIIV ( ( ( (:=:=:=x$$$$3333&l:l:l:l:\ + + + +@,""""*."&&( F*."&&(/4<<<<<@*."&&( ' ' ' ' ' R*."&&( X  X X!%bEX/6/6/6/6f')$"$"$"$"$"P48#K#K#K#K#KJ"%)D-D-D-D-#"D-R <@<@<@<@<@@$("& $$( I I I I I f#'>@@@@H$("& $$("&#PPPPPd####"HI----^))))))))AE1'1'1'1'1'hAE@'@'@'@'@'FAEA'A'A'A'A'F,,,,,6,,,,,67777         2 2 2 2 2'''''rE)g __future__rrrVretypingr>r collectionsrrrrrr numpyrrtyping_extensionsr r rFr r rrpaddle.autograd.backward_utilsr paddle.baserrrpaddle.base.corerpaddle.base.dygraphrpaddle.base.dygraph.baserrrrpaddle.base.dygraph_utilsrpaddle.base.executorrrpaddle.base.frameworkrrrr|r r!r"r#r$r%paddle.base.layer_helper_baser&5paddle.distributed.flex_checkpoint.dcp.sharded_weightr'r(paddle.frameworkr)paddle.profiler.utilsr* paddle.utilsr+paddle.utils.decorator_utilsr,collections.abcr-r.r/r0paddle._typingr1r2r3r4paddle.nn.initializerr5__all__rrlrr*r#r)r#compiler^r`rDrRr\rdrsr{r}rrrr6r~rErCrsV#"""""  ////////666666666666,,,,,,,, ............3333334444444444$$$$$$'''''' DCCCCC77777777                      :99999'&&&&&222222######2EEEEEEEEEEEEMMMMMMMMMMMM111111  gv  &' gvtCH~ .fd38n6L0M MNP &&!6) gvtCH~v 6 >?A 4V $f&8f&EE F :,,- -.. bj)** "!!!,///111 (((MMM     J!N4E#FGG   eeeeeeeeP         / / / / / / / /Fq/'q/'q/'q/'q/'q/'q/'q/'q/'q/'rE