ёiSSKJr SSKrSSKrSSKrSSKrSSKJr SSKJ r SSK r SSK J r SSKJr SSKJr SSKJr S S KJr \ (aSS K Jr SS KJrJrJr S q\R8\R:\R<\R>\R@\RB/r"0SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SSSS .Er#/S!Qr$S"r%\"\%5r&S#r'S$r(g)%) annotationsN)reduce) TYPE_CHECKING)_C_ops)DataType)wrap_decorator)size_args_decorator_patch)Value)Tensor) DTypeLike PlaceLike ShapeLikeFfloat16halfbfloat16float32floatfloat64doubleint8charint16shortint32intint64longbool complex64 complex128)r!cfloatcdouble)__add____radd____sub____rsub____mul____rmul____mod____rmod____div____rdiv__ __truediv__ __rtruediv__ __floordiv__ __rfloordiv____pow____rpow____eq____ne____lt____le____gt____ge__c^U4SjnU$)NcP>[STRSTRS35e)N'a' only can be called by `paddle.Tensor` in dynamic graph mode. Suggestions: 1. If you are in static graph mode, you can switch to dynamic graph mode by turning off `paddle.enable_static()` or calling `paddle.disable_static()`. 2. If you are using `@paddle.jit.to_static`, you can call `paddle.jit.enable_to_static(False)`. If you have to translate dynamic graph to static graph, please use other API to replace 'z'.)AssertionError__name__)argskwargsfuncs X/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/pir/math_op_patch.py__impl__'_fake_interface_only_..__impl__ks: himhuhugvvx z  )rArCs` rB_fake_interface_only_rGjs  OrEc [U[5(de[U5nSn/n[UR5HJupVUS:a.US:aUnUR U5 M&UR S5 M9UR U5 ML US:wdeSSKJn [R"XX!X3UR55nSUl U$)Nrr )coreT) isinstancer r enumerateshapeappendpaddle.frameworkrJrfull_batch_size_likePlace stop_gradient) ref_varvaluedtype batch_dim out_shapeidrJouts rBcreate_tensor_with_batchsizer[ys gu % %% % %LEII'--( q51}   #  #   Q ) ??%  % %E)  CC JrEc ^>^?^@Sm@SnSSjn[S5n[S5n[S5nSnS n[S 5nS nS n SU?4S jjn U?4Sjm>U>4Sjn U>4Sjn Sn U?4SjnU?4SjnU?4SjnU?4SjnU?4SjnU?4SjnU?4SjnSU>U?U@4Sjjn[U?4Sj5n[S5n[S5nSSSSS.SU?4S"jjjn[SSSSS.SU?4S#jjj5n[SSSSS.SU?4S$jjj5n[SSSSS.SU?4S%jjj5nS&nS'nS(nS)n U?4S*jn![S+5n"S,n#U?4S-jn$S.n%S/n&S0n'U?4S1jn(S2n)SU?4S3jjn*S4n+U?4S5jn,[S65n-[S75n.[S85n/[SS9j5n0U0RSS:j5n0SSS;jjn1[SS<j5n2S=SKm?SS>jn3/S?U4PS@U4PSAU4PS U4PSBU4PSCU4PSDU4PSEU 4PSFU 4PSGU4PSHU4PSIT>4PSJU 4PSKU 4PSLU 4PSU4PSMU4PSNU4PSOU4PSPU4PSQU4PSRU4PS!U04PSSU14PSTU!4PSUU"4PSVU#4PSWU$4PSXU(4PSYU)4PSZU%4PS[U'4PS\U&4PS]U*4PS^U,4PS_U.4PS`U-4PSaU/4PSbU34PScU+4PSdU"SdT?R R SU54PSeU"SeT?R R SU54PSfU"SfT?R RSU54PSgU"SgT?R RSU54PShU"ShT?R RSU54PSiU"SiT?R RSU54PSjU"SjT?R RSU54PSkU"SkT?R RSU54PSlU"SlT?R RSS54PSmU"SmT?R RSS54PSnU"SnT?R RSS54PSoU"SoT?R RSS54PSpU"SpT?R RSS54PSqU"SqT?R RSS54PSrU"SrT?R RSS54PSsU"SsT?R RSS54PStU"StT?R RSS54PSuU"SuT?R RSS54PSvU4PSwU4PSxU"SxT?R RSS54PSyU"SyT?R RSS54PSzU"SzT?R R SS54PS{U"S{T?R R"SS54PS|U"S|T?R R$SS54PS}U"S}T?R R&SS54PS~U4PSU4PSU4PSU 4PSU24Pn4U "5n5U4R)U55 [*(dU4Hn6U6S=n7U6Sn8[-[.U7U85 M S=SKm?T?R R2HKn7[5[.U75(aM[7T?R U7S5n8U8(dM:[-[.U7U85 MM T?R R8H7un9n:[7T?R U:S5n;U;(dM&[-[.U9U;5 M9 SSKJn.safe_get_dtypes* >IIE  ><= =s  c0[R"US5$)a In dy2static, Tensor also needs cpu() and cuda() interface. But, the underneath operator has only forward op but not backward one. Returns: The tensor which has copied to cpu place. Examples: In Static Graph Mode: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.static.data(name="x", shape=[2,2], dtype='float32') >>> y = x.cpu() r)rmemcpyselfs rBcpumonkey_patch_value..cpus*}}T1%%rETcUb[R"S5 USLa[R"S5 [R"US5$)a+ In dy2static, Tensor also needs cpu() and cuda() interface. But, the underneath operator has only forward op but not backward one. Args: self(Tensor): The variable itself. device_id(int, optional): The destination GPU device id. Default: None, means current device. We add this argument for dy2static translation, please do not use it. blocking(bool, optional): Whether blocking or not, Default: True. We add this argument for dy2static translation, please do not use it. Returns: The tensor which has copied to cuda place. Examples: In Static Graph Mode: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.static.data(name="x", shape=[2,2], dtype='float32') >>> y = x.cpu() >>> z = y.cuda() z3device_id is not supported, and it will be ignored.T2blocking is not supported, and it will be ignored.r )warningswarnrrc)re device_idblockings rBcuda monkey_patch_value..cudas?8  MMO P 4  MMN O}}T1%%rEc[R"S5 SSKJn [ US5(a0[ UR URR5(agUR5n[ X!RR5$)z Tensor don't have 'is_cuda' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zMTensor do not have 'is_cuda' interface for pir graph mode, try not to use it.r) frameworkplaceT) rjrkpaddlerqhasattrrKrrrJ CUDAPlace_current_expected_place_)rerqexpected_places rBis_cuda#monkey_patch_value..is_cudasl  [  % 4 ! !j JJ 00' ' &??ANnnn.F.FG GrEc0[R"S5 g)z Tensor don't have 'place' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zbTensor do not have 'place' interface for pir graph mode, try not to use it. None will be returned.Nrjrkrds rBrr!monkey_patch_value..places  p rEc0[R"S5 g)z Tensor don't have 'device' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zcTensor do not have 'device' interface for pir graph mode, try not to use it. None will be returned.Nr{rds rBdevice"monkey_patch_value..devices  q rEc2[R"S5 U$)z Tensor don't have 'contiguous' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zjTensor do not have 'contiguous' interface for static graph mode, try not to use it. self will be returned.r{rds rB contiguous&monkey_patch_value..contiguouss  x  rEc0[R"S5 g)z Tensor don't have 'is_contiguous' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zmTensor do not have 'is_contiguous' interface for static graph mode, try not to use it. True will be returned.Tr{rds rB is_contiguous)monkey_patch_value..is_contiguous s  { rEc,[UR5$)ab Returns the dimension of current Tensor Returns: the dimension Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Tensor >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # print the dimension of the Tensor >>> print(x.ndim) 3 lenrMrds rB_ndim!monkey_patch_value.._ndims*4::rEc,[UR5$)aj Returns the dimension of current Tensor Returns: the dimension Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Tensor >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # print the dimension of the Tensor >>> print(x.ndimension()) 3 rrds rB ndimension&monkey_patch_value..ndimension-(4::rEc,[UR5$)ac Returns the dimension of current Tensor Returns: the dimension Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Tensor >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # print the dimension of the Tensor >>> print(x.dim()) 3 rrds rBdimmonkey_patch_value..dimCrrEc >UR5(aUR5(dgSSKJn U"U5n[ U5S:wa [ SU5OSnSn[ U5S:Xa8[ UT RR5(dUS:wa [S5eU$[ U5S:Xa@Uun[ UT RR5(dXT:a[SUSU35eO[ U5[ U5:wa [S 5eS/[ U5-n[S[ U55HnXg*X7*-Xg*S- 'M [[ U55HnXn[ U[5(d[S US [U535e[ X7T RR5(dXU:a[SUS US X735eXXXg-- nM UR5U$)z In order to be compatible with the item interface introduced by the dynamic graph, it does nothing but returns self. It will check that the shape must be a 1-D tensor Nr)Shapec[X-5$N)r)xys rB3monkey_patch_value.._item..fs CJrEr zUonly one element tensors can be converted to Python scalars when no input coordinateszindex z is out of bounds for size z&incorrect number of indices for Tensorzargument (position z) must be long, but got z is out of bounds for axis z with size )is_dist_is_initializedpaddle.jit.dy2staticrrrrKpirr r^ranger TypeErrortypeflatten) rer?rdimsnumeloffsetstridesrXindexrss rB_item!monkey_patch_value.._itemYs <<>>$"6"6"8"8.T{9eVZZ%5%5665A: 8 K Y!^IVeVZZ%5%5666? VH$?wG4yCI% !IJJcCIoG1c'l+")"+R"8Q,3t9%!%--#-aS0He V#47FJJ,<,<==a($ 'B1#[QUQXPYZ'*,,&||~f%%rEc>[U[5(d%TRRR U5nUR U:XaU$[ R"X5$)a@ **Notes**: Convert a value to a specified data type if it differs from the current dtype; otherwise, return the original value. Args: self(Tensor): The source Tensor dtype: The target data type Returns: Tensor: Tensor with new dtype Examples: In Static Graph Mode: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> startup_prog = paddle.static.Program() >>> main_prog = paddle.static.Program() >>> with paddle.static.program_guard(startup_prog, main_prog): ... original_value = paddle.static.data(name = "new_value", shape=[2,2], dtype='float32') ... new_value = original_value.astype('int64') ... print(f"new value's dtype is: {new_value.dtype}") ... new Tensor's dtype is: paddle.int64 )rKrrrJconvert_np_dtype_to_dtype_rUrcast)rerUrss rBastype"monkey_patch_value..astypesKD%**JJOO>>uEE :: K{{4''rEc>UR5(aT"US5nT"US5$UR5(a T"UR5S5nT"US5$T"US5$)Nruint8)is_floating_point is_complexreal)retensorrrs rBbyte monkey_patch_value..bytesd  ! ! # #D&)F&'* * __  $))+v.D$( ($( (rEc>/n[R5H9upU4SjnU"U5nXlSUSUS3UlUR X45 M; U$)z/ Batch create all data type conversion methods c>^UU4SjnU$)Nc>T"UT5$rrF)rerrUs rBconversion_methodwmonkey_patch_value.._create_dtype_conversion_methods..make_conversion_method..conversion_methods!$..rErF)rUrrs` rBmake_conversion_method\monkey_patch_value.._create_dtype_conversion_methods..make_conversion_methods/)(rEz Cast a Tensor to z data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with z dtype )_supported_dtype_conversionsitemsr>__doc__rN)methods method_name target_dtyper method_implrs rB _create_dtype_conversion_methods._create_dtype_conversion_methodssu)E)K)K)M %K ) 1>K#. '*^,+,8.9 #K  NNK5 6!*N"rEc8URUR5$r)rrU)reothers rBtype_as#monkey_patch_value..type_ass{{5;;''rEc*>TRUSU5$N?scaler_rTrss rB _scalar_add_(monkey_patch_value.._scalar_add_s||Ce,,rEc,>TRUSU*5$rrrs rB _scalar_sub_(monkey_patch_value.._scalar_sub_s||Cuf--rEc*>TRUSU5$)Nrrs rB _scalar_rsub_)monkey_patch_value.._scalar_rsub_s||Cu--rEc(>TRXS5$)Nrrs rB _scalar_mul_(monkey_patch_value.._scalar_mul_s||C,,rEc0>TRUSU- S5$)Nrrrrs rB _scalar_div_(monkey_patch_value.._scalar_div_s||Cuc22rEc*>TRUSS5$)Nrrrr_rss rB _scalar_neg_(monkey_patch_value.._scalar_neg_s||Cs++rEc&>TRU5$r)absrs rB _scalar_abs_(monkey_patch_value.._scalar_abs_szz#rEFcD>^^^^UUUUUUU4SjnSUlTUlU$)Nc>[U[5(a7UR[;aT"U[R 5nT bT "X5$O[U[ 5(a[U5nT TR:Xa4UR[;a TRU[R 5nURTR:Xa TRU[R5nT bT "X5$OUcTS:XagTS:XagOT "U5n[U[5(dT (aYURHnUS:dM [XU5n O[ TRRR!URUU5nO'TRRR!/UU5nT (aUnUnUnT "X5nU$)Nr4Fr5Tr)rKrrU_supported_int_dtype_rFLOAT32rdividerrINT64r rMr[rcreation fill_constant) re other_var lhs_dtypeelemtmprZrrrs python_apireverser` scalar_methods rBrC>monkey_patch_value.._binary_creator_..__impl__s)U++::!66!$(8(89D!,(99-Is++"), &--/ &;;!;;tX-=-=>D::,!;;tX^^)rrrrrCrrsr`s```` rB_binary_creator_,monkey_patch_value.._binary_creator_s) M M ^(rEc&>TRU5$)a Returns the number of elements for current Tensor, which is a int64 Tensor with shape [] . Returns: Tensor, the number of elements for current Tensor Examples: .. code-block:: pycon >>> import paddle >>> paddle.enable_static() >>> startup_prog = paddle.static.Program() >>> main_prog = paddle.static.Program() >>> with paddle.static.program_guard(startup_prog, main_prog): ... x = paddle.assign(np.random.rand(2, 3, 4).astype("float32")) ... (output_x,) = exe.run(main_program, fetch_list=[x.size]) ... print(f"value's size is: {output_x}") value's size is: 24 )rrerss rB_size_"monkey_patch_value.._size_Ws*||D!!rEc [UR5S:XaU$[[[ [UR5555n[ R "X5$)a Permute current Tensor with its dimensions reversed. If `n` is the dimensions of `x` , `x.T` is equivalent to `x.transpose([n-1, n-2, ..., 0])`. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_T = x.T >>> exe = paddle.static.Executor() >>> x_T_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_T])[0] >>> print(x_T_np.shape) (5, 3, 2) r )rrMlistreversedrr transposereperms rB_T_monkey_patch_value.._T_nsF. tzz?a KHU3tzz?345++rEc[UR5S:a"[S[UR5S35e[[ [UR555nUSUSsUS'US'[ R "X5$)a Permute current Value with its last two dimensions reversed. If `n` is the dimensions of `x` , `x.mT` is equivalent to `x.transpose([0, 1, ..., n-1, n-2])`. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_mT = x.mT >>> exe = paddle.static.Executor() >>> x_mT_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_mT])[0] >>> print(x_mT_np.shape) (2, 5, 3) z Tensor.ndim(z/) is required to be greater than or equal to 2.rI)rrMr^rrrrrs rB_mT_ monkey_patch_value.._mT_s}. tzz?Q s4:://^_ E#djj/*+!"XtBxR$r(++rErUr~ requires_grad pin_memorysizer~r c h>Uc URnUc URnTRUUUUUUS9$)a Returns a Tensor of size ``size`` filled with ``fill_value``. By default, the returned Tensor has the same dtype and place as this tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_new = x.new_full([2, 3], 3.14, dtype="float64", device="cpu") >>> exe = paddle.static.Executor() >>> x_new_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_new])[0] >>> print(x_new_np.shape) (2, 5, 3) >>> print(str(x_new_np.dtype)) 'paddle.float64' >>> print(x_new_np.place) Place(cpu) r rUrrfull)rer fill_valuerUr~r r rss rB _new_full_&monkey_patch_value.._new_full_sJB =JJE >ZZF{{  '!   rEcf>Uc URnUc URnTRUUUUUS9$)a Returns a Tensor of size ``size`` filled with uninitialized data. By default, the returned Tensor has the same dtype and place as this tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_new = x.new_empty([2, 3], dtype="float64", device="cpu") >>> exe = paddle.static.Executor() >>> x_new_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_new])[0] >>> print(x_new_np.shape) (2, 3) >>> print(str(x_new_np.dtype)) 'paddle.float64' >>> print(x_new_np.place) Place(cpu) r )rUrremptyrerrUr~r r rss rB _new_empty_'monkey_patch_value.._new_empty_sGB =JJE >ZZF|| '!   rEc h>Uc URnUc URnTRUSUUUUS9$)a Returns a Tensor of size ``size`` filled with ``1``. By default, the returned Tensor has the same dtype and place as this tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_new = x.new_ones([2, 3], dtype="float64", device="cpu") >>> exe = paddle.static.Executor() >>> x_new_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_new])[0] >>> print(x_new_np.shape) (2, 3) >>> print(str(x_new_np.dtype)) 'paddle.float64' >>> print(x_new_np.place) Place(cpu) r r rrs rB _new_ones_&monkey_patch_value.._new_ones_ JB =JJE >ZZF{{  '!   rEc h>Uc URnUc URnTRUSUUUUS9$)a Returns a Tensor of size ``size`` filled with ``0``. By default, the returned Tensor has the same dtype and place as this tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.ones(shape=[2, 3, 5]) >>> x_new = x.new_zeros([2, 3], dtype="float64", device="cpu") >>> exe = paddle.static.Executor() >>> x_new_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_new])[0] >>> print(x_new_np.shape) (2, 3) >>> print(str(x_new_np.dtype)) 'paddle.float64' >>> print(x_new_np.place) Place(cpu) rr rrs rB _new_zeros_'monkey_patch_value.._new_zeros_8rrEcDSn[[R"U55e)Na int(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode. It's usually triggered by the logging implicitly, for example: >>> logging.info("The value of x is: {int(x)}") ^ `x` is Tensor, `int(x)` triggers int(Tensor) There are two common workarounds available: If you are logging Tensor values, then consider logging only at dynamic graphs, for example: Modify the following code >>> logging.info("The value of x is: {int(x)}") to >>> if paddle.in_dynamic_mode(): ... logging.info("The value of x is: {int(x)}") If you need to convert the Tensor type, for example: Modify the following code >>> x = int(x) to >>> x = x.astype("int64") rtextwrapdedentre error_msgs rB_int_!monkey_patch_value.._int_gs , 233rEcDSn[[R"U55e)Na float(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode. It's usually triggered by the logging implicitly, for example: >>> logging.info("The value of x is: {float(x)}") ^ `x` is Tensor, `float(x)` triggers float(Tensor) There are two common workarounds available: If you are logging Tensor values, then consider logging only at dynamic graphs, for example: Modify the following code >>> logging.info("The value of x is: {float(x)}") to >>> if paddle.in_dynamic_mode(): ... logging.info("The value of x is: {float(x)}") If you need to convert the Tensor type, for example: Modify the following code >>> x = float(x) to >>> x = x.astype("float64") r"r%s rB_float_#monkey_patch_value.._float_ * 233rEcDSn[[R"U55e)Na bool(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode. If you haven't call bool(Tensor) explicitly, it's usually triggered by the control flow implicitly, for example: >>> if x > 0: ^ `x` is Tensor, `x` > 0 is also a Tensor, `if x > 0` triggers bool(Tensor) ... y = y + 1 There are two common workarounds available: If you are checking for Tensor values, then consider checking only at dynamic graphs, for example: Modify the following code >>> if x > 0: ... raise ValueError("x should be positive") to >>> if paddle.in_dynamic_mode() and x < 0: >>> raise ValueError("x should be positive") If you need to control the flow of execution based on the value of the Tensor, then you need to rewrite the code as a control flow, for example: Modify the following code >>> if x < y: ... y = y + 1 ... else: ... y = y - 1 to >>> pred = paddle.less_than(x=x, y=y, name=None) >>> y = paddle.static.nn.cond(pred, lambda: y + 1, lambda: y - 1) For more info, please refer to https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/static/nn/cond_cn.html r"r%s rB_bool_"monkey_patch_value.._bool_s : 233rEcDSn[[R"U55e)Na( complex(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode. It's usually triggered by the logging implicitly, for example: >>> logging.info("The value of x is: {complex(x)}") ^ `x` is Tensor, `complex(x)` triggers complex(Tensor) There are two common workarounds available: If you are logging Tensor values, then consider logging only at dynamic graphs, for example: Modify the following code >>> logging.info("The value of x is: {complex(x)}") to >>> if paddle.in_dynamic_mode(): ... logging.info("The value of x is: {complex(x)}") If you need to convert the Tensor type, for example: Modify the following code >>> x = complex(x) to >>> x = x.astype("complex64") r"r%s rB _complex_%monkey_patch_value.._complex_r,rEc&>TRU5$)a$ Returns a new static Tensor, which is the clone of the original static Tensor. It remains in the current graph, that is, the cloned Tensor provides gradient propagation. Calling ``out = tensor.clone()`` is same as ``out = assign(tensor)`` . Returns: Tensor, The cloned Tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Tensor >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # create a cloned Tensor >>> y = x.clone() )assignrs rBclone!monkey_patch_value..clones.}}T""rEcg)a **Notes**: **1. This API is ONLY available in Dygraph mode** **2. Use it only Tensor has gradient, normally we use this for Parameters since other temporal Tensor will be deleted by Python's GC** Clear (set to ``0`` ) the Gradient of Current Tensor Returns: None Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> x = np.ones([2, 2], np.float32) >>> inputs2 = [] >>> for _ in range(10): >>> tmp = paddle.to_tensor(x) >>> tmp.stop_gradient=False >>> inputs2.append(tmp) >>> ret2 = paddle.add_n(inputs2) >>> loss2 = paddle.sum(ret2) >>> loss2.retain_grads() >>> loss2.backward() >>> print(loss2.gradient()) >>> loss2.clear_gradient() >>> print("After clear {}".format(loss2.gradient())) 1.0 After clear 0.0 NrFrds rBclear_gradient*monkey_patch_value..clear_gradientsD rEctUR5(d[SU35eSSKJnJn U"X"U5US9 g)z5 Notes: The type of Tensor must be Tensor Array. zHOnly Tensor with DenseTensorArray support `append` method, but received r) array_length array_write)rrXarrayN)is_dense_tensor_array_typerpaddle.tensor.arrayr;r<)rer_r;r<s rBrN"monkey_patch_value..append sC ..00Z[_Z`a  Bc\$/tUR5(d[SU35e[U5S:XaSnOUSnTRR X5$)aQ The type of Tensor must be Tensor Array. When self is TensorArray, calling pop is similar to Python's pop on list. This interface is used to simplify dygraph to static graph operations. Args: self(Tensor): The source variable, which must be DenseTensorArray *args: optional, a int means index. Returns: Tensor: self[index] zEOnly Tensor with DenseTensorArray support `pop` method, but received rrI)r>rr_pir_ops array_pop)rer?idxrss rBpopmonkey_patch_value..pops]..00WX\W]^  t9>Cq'C((33rEc.[R"U5$r)rsparse_to_denserds rBto_dense$monkey_patch_value..to_dense3s%%d++rEc.[R"U5$r)r sparse_valuesrds rBvalues"monkey_patch_value..values6s##D))rEc.[R"U5$r)rsparse_indicesrds rBindices#monkey_patch_value..indices9s$$T**rEcZ>TRRRR5(dS5eUR5(dUR 5(a<TR R UR5U5nURU5 g[S5e)Nz3We only support call 'set_shape' in to_static mode.zBCurrently, we can only set shape for dense and selected_row tensor) basedygraphin_to_static_modeis_dense_tensor_typeis_selected_row_typercreate_shaped_typerset_typer^)rerMrrss rB set_shape%monkey_patch_value..set_shape<s{{""''99;; A ;  $ $ & &$*C*C*E*E::00eDD MM$ T rEc*[[U55$r)hashidrds rB value_hash&monkey_patch_value..value_hashIsBtH~rEc>^UcUcUcT$Ub[U[5(aTRRU5nOi[UTRR TR TRTR45(aO![S[U5R-5eUcSnO[U[5(dS5eUU4SjnU"TXX45$)Nzdevice value error, must be str, paddle.CPUPlace(), paddle.CUDAPlace(), paddle.CUDAPinnedPlace(), paddle.XPUPlace() or paddle.CustomPlace(), but the type of device is Tz5blocking value error, must be the True, False or Nonec>Uc URnUnX%R:waHTRRRURS9 UR US9nSnSSS5 OUn[ UTR5(aWRUS9nSnO[ UTR5(a5USLa[R"S5 [R"T S5nSnO0[ UTR5(aWR5nSnOWnU(a[ R""U5$U$!,(df  N=f)N)rr)rUF)rmTrir)rUrTrq_dygraph_place_guardrrrrKrurnCUDAPinnedPlacerjrkrrcCPUPlacerfcopydeepcopy) tr~rUrm copy_tensort_usedt_castednew_trsres rB transform2monkey_patch_value.._to..transformrs"}F $[[**?? ,,@ &{{{7H"'K  "&&"2"233 x 8# FF$:$:;;4'MML dA.# FFOO44 #  }}U++L5s D88 E) rKstrr~_convert_to_placerJrQrfrurer^rr>r)rer~rUrmrjrnrss` rB_tomonkey_patch_value.._toLs >em0@K  &#&&88@KK%%OO$$**    ~6l++,  Hh-- G -! FvhDDrEc@UR5nX![U5'U$r)r5r_)rememo new_tensors rB __deepcopy__(monkey_patch_value..__deepcopy__sZZ\ #RXrEc ,>^ ^ ^^^^^^^^SU;aURS5nOSnSU;aURS5nOSn[U5n[U5nXV-S:dXV-S:Xa [S5e1Skn[UR 55U- n[U5S:wa [S[ [ U55-5eSS jmSS jmSS jm"S S5m U4SjmU4SjmU4SjmSU 4SjjmSU 4SjjmU 4Sjm U U UUUUUUUU4 Sjn U "U0UD6nURSS 5n U b,URS5 U RUS'U R URSS5(aU(aSOSUS'XAS'UR"S0UD6n U $) a Performs Tensor dtype and/or device conversion. A paddle.dtype and place are inferred from the arguments of ``self.to(*args, **kwargs)``.There are three ways to call `to`: 1. to(dtype, blocking=True) 2. to(device, dtype=None, blocking=True) 3. to(other, blocking=True) Returns: Tensor: self Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1,2,3]) >>> print(x) Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [1, 2, 3]) >>> x = x.to("cpu") >>> print(x.place) Place(cpu) >>> x = x.to("float32") >>> print(x.dtype) paddle.float32 >>> x = x.to("gpu", "int16") >>> print(x) Tensor(shape=[3], dtype=int16, place=Place(gpu:0), stop_gradient=True, [1, 2, 3]) >>> y = paddle.to_tensor([4,5,6]) >>> y Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True, [4, 5, 6]) >>> y = y.to(x) >>> print(y) Tensor(shape=[3], dtype=int16, place=Place(gpu:0), stop_gradient=True, [4, 5, 6]) non_blockingFrgrato() received too many arguments - expected one of: * (Union[str, paddle.CPUPlace(), paddle.CUDAPlace(), paddle.CUDAPinnedPlace(), paddle.XPUPlace(), paddle.CustomPlace()] device, Union[str, paddle.dtype, numpy.dtype] dtype, bool blocking) * (Union[str, paddle.dtype, numpy.dtype] dtype, bool blocking) * (paddle.Tensor other, bool blocking) >rUrr~rmz(to() got an unexpected keyword argument NcgrrF)rUrms rBdtype_first_sig7monkey_patch_value..to..dtype_first_sigs3rEcgrrF)r~rUrms rBdevice_first_sig8monkey_patch_value..to..device_first_sigsrEcgrrF)rrms rBtensor_like_first_sig=monkey_patch_value..to..tensor_like_first_sigsrEc\rSrSrSrg).monkey_patch_value..to.._NoArgirFN)r> __module__ __qualname____firstlineno____static_attributes__rFrErB_NoArgrscrErc>/SQn[UTR[R45=(d* [U[5=(a UR 5U;$)N) rrrrrrrrrr r!r)rKrUnprplower)arg valid_dtypesrss rBis_dtype0monkey_patch_value..to..is_dtypesG LcFLL"((#;<3$D )D rEcf>USL=(d& [UTRR[45$r)rKrJrQrprrss rB is_device1monkey_patch_value..to..is_devices($;K*S6;;3D3Dc2J"K KrEcD>[UTRR5$r)rKrr rs rB is_tensor1monkey_patch_value..to..is_tensorsc6::#3#34 4rEc>^UU4SjnU$)Nc:>[U5T:aUT$T"5$r)r)r?r@rpositions rBextract_positional_arggmonkey_patch_value..to..create_positional_arg_extractor..extract_positional_args t9x'>)xrErF)rrrs` rBcreate_positional_arg_extractorGmonkey_patch_value..to..create_positional_arg_extractors * )rEc>^^UUU4SjnU$)Nc>TU;a4[U5T:a%[UR55TT:XaUT$T"5$r)rrkeys)r?r@rkeyrs rBextract_keyword_argamonkey_patch_value..to..create_keyword_arg_extractor..extract_keyword_args?6MF h.V[[]+H5<!#;&xrErF)rrrrs`` rBcreate_keyword_arg_extractorDmonkey_patch_value..to..create_keyword_arg_extractors ' &rEc>^UU4SjnU$)Nc^>TH n[U"X5=nT5(aMUs $ T"5$r)rK)r?r@ extractorrr extractorss rBchainGmonkey_patch_value..to..chain_extractors..chains2!+I%Yt-D&DcfMM" ",xrErF)rrrs` rBchain_extractors8monkey_patch_value..to..chain_extractorss LrEc > T T "T "SS9T "SSS95T4T T "T "SS9T "SSS95T4TT "T "SS9T "SSS95T40nUR5HtunupE[U"X5=nT5(aM#U"U5(dM2[R"U5R"U0UD6nUR 5 UR s $ [S5e)Nr)rrU)rrr~rzNo matching signature found.)rrKinspect signaturebindapply_defaults argumentsr^)r?r@ signature_mapsigr conditionr bound_argsrrrrrr}rrrrs rBdispatch_to_signature=monkey_patch_value..to..dispatch_to_signature$s $7C41M "!$7C4AN #&$7C41M (M00=/B/B/D++i!$T22CFnn!(!2!23!7!.tosX V #!::n5L L V  **V,KKI &k  "Q &)*AQ*F9 > 6;;=)J6 |  !:tL)*+  7D< & L 5 * ' ! =! =F%d5f5$'   HHW !KKDM KK*e44 E$ Z*]hh rEcg)a **Notes**: **This API is ONLY available in Dygraph mode** Returns a numpy array shows the value of current :ref:`api_guide_Variable_en` Returns: ndarray: The numpy value of current Variable. Returns type: ndarray: dtype is same as current Variable Examples: .. code-block:: python >>> import paddle >>> import paddle.base as base >>> from paddle.nn import Linear >>> import numpy as np >>> data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32') >>> with base.dygraph.guard(): ... linear = Linear(32, 64) ... data_tensor = paddle.to_tensor(data) ... x = linear(data_tensor) ... print(x.numpy()) NrFrds rBnumpy!monkey_patch_value..numpyU0 rEcg)a **Notes**: **This API is ONLY available in Dygraph mode** Returns a Python list that contains the elements of current :ref:`api_guide_Variable_en` Returns: list: The Python list containing the elements of current Variable. Returns type: list: Elements have the same dtype as current Variable Examples: .. code-block:: python >>> import paddle >>> import paddle.base as base >>> import numpy as np >>> data = np.random.uniform(-1, 1, [2, 3]).astype('float32') >>> with base.dygraph.guard(): ... x = paddle.to_tensor(data) ... print(x.tolist()) # Convert tensor to Python list NrFrds rBtolist"monkey_patch_value..tolistorrEcg)z Value don't have 'register_hook' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a error here. NrF)rehooks rB register_hook)monkey_patch_value..register_hooks rEc$UR(+$)a Whether this Tensor requires gradient computation. This is a convenience property that returns the opposite of stop_gradient. Setting requires_grad=True is equivalent to setting stop_gradient=False. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([2, 3]) >>> print(x.requires_grad) # False by default >>> >>> x.requires_grad = False >>> print(x.stop_gradient) # True )rRrds rBr )monkey_patch_value..requires_grads$%%%%rEct[U[5(d[S[U535eU(+Ulg)z Set whether this Tensor requires gradient computation. Args: value (bool): True to enable gradient computation, False to disable. $requires_grad must be bool, but got NrKrrrrR)rerTs rBr rs8%&&6tE{mD "'YrEcv[U[5(d[S[U535eU(+UlU$)z Set whether this Tensor requires gradient computation. Args: requires_grad (bool): True to enable gradient computation, False to disable. rr)rer s rBrequires_grad_*monkey_patch_value..requires_grad_s@-..6tM7J6KL "/. rEc"UR5$)z Returns the number of bytes allocated on the machine for a single element of the Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn((2,3),dtype=paddle.float64) >>> x.itemsize 8 ) element_sizerds rBitemsize$monkey_patch_value..itemsizes  ""rErc0[R"S5 g)z Tensor don't have 'get_device' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zgTensor do not have 'get_device' interface for pir graph mode, try not to use it. None will be returned.Nr{rds rB get_device&monkey_patch_value..get_devices  u rErfrnrrrrxritemrrndimrrrrTmTnew_full new_emptynew_ones new_zerosrr5r8rNrEr[__hash__rIrQrMrrrrrrrrwr$r%r&r'r(r)r,r.r-r/r2r3r0r1r*r+ __matmul__ __rmatmul____neg____abs__r4r5r6r7r8r9 __float____int____bool__ __complex__rr r)_getitem_static_setitem_static)NT)r?r)FN) rrrzbool | float | paddle.TensorrUDTypeLike | Noner~PlaceLike | Noner rr r) rrrUrr~rr rr r)NNNN)returnr)rTrrNone)T)r rrr )rr)rr)"propertyr fake_interface_onlysetterrsraddsubtractmultiplyrpow floor_divide remaindermatmulequal not_equal less_than less_equal greater_than greater_equalextend_already_patch_valuesetattrr paddle.tensortensor_method_funcrtgetattrmagic_method_funcbase.variable_indexrr __getitem__ __setitem__)Arfrnrxrrr~rrrrrrrrrrrrrrrrrrrr rrrrr'r*r.r1r5r8rNrErIrMrQr[r`rrrwrrrrr rrr value_methodsdtype_conversion_methodsmethodrr magic_method origin_methodimplrrrrsr`sA @@@rBmonkey_patch_valuersd &."&HHH&      ,,,7&r((T )0(-..-3,  ^^@"",,,8,,J#'#'# - - 1-  - ! - - - - ^ #'#'# + +  + ! +  + + + Z #'#'# , ,  , ! ,  , , , \ #'#'# , ,  , ! ,  , , , \42404@40#2! ! F =40,*+   IEV wr  2  2  &&& ' '  # # h  h h %h 6 h z" h G h -(h h  h z"h h 6h h $h Gh !h" c #h$ t %h& Z 'h( k")h* Z +h, k"-h. -(/h0 >*1h2 %3h4 >*5h6 67h8  9h: i ;h< Z =h> X?h@ GAhB 6ChD  EhF r GhH 6IhJ %KhL -(MhN z"OhP &QhV  Y (9(95, O Uh`  FMM--ul  _hl  6==115,  khx  FMM22D-  whD  6==115,  ChR  FMM22E<  Qh^  6==//   ]hj  v}}33UL  ihv  Z)=)=tT J uh~  ^V]]-A-A4 N }hF  Y (9(95$ G EhN  Z):):D$ G MhV   : :E4  Uhb  !;!;T4  ahn  Y (?(? M mhv  Z)@)@$ M uh~  \6==+?+? M }hF  ]FMM,@,@$ M EhL L!MhN L!OhT  Xv}}':':E4 H Sh\  Xv}}'>'>t L [hd  Xv}}'>'>t L chl  Xv}}'?'? M kht  Xv}}'A'A5$ O sh|  &--55ud  {hF gGhH EIhJ VKhL  "MhN XOhMR @A12 #F )K )K E; 4$ !==;;Kuk**!&--dCK{{K8 <,2==+J+J 'L-6==->Dt|T2,K K++#9 rE)) __future__rrgrr#rj functoolsrtypingrrrrsrpaddle.base.libpaddlerpaddle.base.wrapped_decoratorrpaddle.utils.decorator_utilsr r r paddle._typingr rrr BOOLUINT8INT8INT16INT32rrrSUPPORT_PROMOTION_OPSrGrr[rrFrErBr(s#  *8>> MM NN MM NN NN NN   y  I    y    Y  y  i  F  F  W   W! " W# $ 7% & W' ( G) , F- ./ 05 :4 %%:;4S$rE