ϑiO,"SS5r\"S5r\"S5r\"S5r\"S5r\"S5r\"S5r\"S5rS rS r S r S r S r Sr SrSrSrSrSrSrSrSrg)c2\rSrSrSrSS/rSrSrSrSr g ) RegistryzA general registry object.nametabcXl0UlgNrrselfrs `/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/incubate/autograd/primreg.py__init__Registry.__init__s cTXR;d SUS35eX RU'g)Nzname "z"" should not be registered before.)r)r rvalues r registerRegistry.registers388# TF< = #rc8URRU5$r)rgetr s r lookupRegistry.lookupsxx||D!!rr N) __name__ __module__ __qualname____firstlineno____doc__ __slots__r rr__static_attributes__rr rrs$I "rr primop_fn orig2prim prim2orig primop_jvpprimop_transposeprimop_position_argnames compositec,[RU5$r) _primop_fnroptypes r lookup_fnr+,   V $$rc,[RU5$r) _orig2primrr)s r lookup_orig2primr/0r,rc,[RU5$r) _prim2origrr)s r lookup_prim2origr24r,rc,[RU5$r) _primop_jvprr)s r lookup_jvpr58s   f %%rc,[RU5$r)_primop_transposerr)s r lookup_transposer8<s  # #F ++rc,[RU5$r)_composite_opsrr)s r lookup_compositer;@s   ((rc [RUR5nUcSURS35eUGtp#/nUHn[[ UR R URU555n[U5S:dS[U5S35e[U5S:aURU5 MURUS5 M U$)a Returns the position inputs of `op` as registered with REGISTER_FN. Args: op(Operator): The op that needs to get the inputs Returns: Tensor(s): Inputs of the op Examples: .. code-block:: python >>> from paddle.incubate.autograd.primops import _simple_binop >>> from paddle.base.layer_helper import LayerHelper >>> from paddle.incubate.autograd.primreg import REGISTER_FN >>> # doctest: +SKIP('Depends on external code.') >>> @REGISTER_FN('div_p', 'X', 'Y', 'Z') >>> def div(x, y, out=None): ... return _simple_binop(LayerHelper('div_p', **locals())) The registered inputs are ['X', 'Y'] for div_p and accordingly this function will return inputs in the order of X then Y. zargs of z, should not be None in op_position_inputs().z>len(vars) should be greater than or equal to 0, but len(vars)=.) _primop_position_argnamesrtypelistmapblockvarinputlenappend)opargs input_names_inputsrvarss r op_position_inputsrODs4 % + +BGG 4D   277)GH O[ FC bhhtn564yA~ LSQUYKWX Y ~ t9q= MM$  MM$q' " MrcF[RUR5nUcS5eUGtp#[[ UR R URU555n[U5S:dS[U5S35e[U5S:aUnU$USnU$)a Returns the output of `op` as registered with REGISTER_FN. Args: op(Operator): The op that needs to get the output Returns: Tensor(s): Output of the op Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> from paddle.incubate.autograd.primops import _simple_binop >>> from paddle.base.layer_helper import LayerHelper >>> from paddle.incubate.autograd.primreg import REGISTER_FN >>> @REGISTER_FN('div_p', 'X', 'Y', 'Z') >>> def div(x, y, out=None): ... return _simple_binop(LayerHelper('div_p', **locals())) The registered output is ['Z'] for div_p and accordingly this function will return output Z. z0args should not be None in op_position_output().r=zDlen(outvars) should be greater than or equal to 0, but len(outvars)=r>r?) r@rrArBrCrDrEoutputrG)rIrJrL output_nameoutvarsrQs r op_position_outputrTrs4 % + +BGG 4D  OOO OQ3rxx||RYY{%;<=G w<1  NsSZ|n\]^  7|a M Mrc^[T[5(d[S[T5S35e[R TU5 U4SjnU$)a Decorator for registering the Python function for a primitive op. Args: op_type(str): The op name position_argnames(list[str]): Input and output names of the op Returns: wrapper: Inner wrapper function Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> from paddle.incubate.autograd.primops import _simple_binop >>> from paddle.base.layer_helper import LayerHelper >>> from paddle.incubate.autograd.primreg import REGISTER_FN >>> @REGISTER_FN('tanh_p', 'X', 'Y') >>> def tanh(x, out=None): ... return _simple_unop(LayerHelper('tanh_p', **locals())) op_type must be str, but got r>c4>[RTU5 U$r)r(r)fop_types r wrapperREGISTER_FN..wrappersGQ'r) isinstancestr TypeErrorrAr@r)rYposition_argnamesrZs` r REGISTER_FNr`sH2 gs # #7W aHII&&w0AB Nrcn^[T[5(d[S[T5S35eU4SjnU$)a Decorator for registering the lower function for an original op into sequence of primitive ops. Args: op_type(str): The op name Returns: wrapper: Inner wrapper function Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> from paddle.base.layer_helper import LayerHelper >>> from paddle.incubate.autograd.utils import get_input_var_list >>> from paddle.incubate.autograd import primops >>> from paddle.incubate.autograd.primreg import REGISTER_ORIG2PRIM >>> @REGISTER_ORIG2PRIM('tanh') >>> def tanh_orig2prim(op): ... x, = get_input_var_list(op) ... return primops.tanh(x) rVr>cB>^UU4Sjn[RTU5 g)Ncj>URT:XdSURST35eT"U/UQ70UD6$Nz3op.type should be equal to op_type, but op.type is z and op_type is rArIrJkwargsrXrYs r _lower3REGISTER_ORIG2PRIM..wrapper.._lowerJ77g% EbggYN^_f^gh %R)$)&) )r)r.rrXrhrYs` r rZ#REGISTER_ORIG2PRIM..wrapper * GV,rr\r]r^rArYrZs` r REGISTER_ORIG2PRIMrps72 gs # #7W aHII- Nrcn^[T[5(d[S[T5S35eU4SjnU$)a Decorator for registering the lower function for an original op into sequence of primitive ops. Args: op_type(str): The op name Returns: wrapper: Inner wrapper function Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> import paddle >>> from paddle.incubate.autograd.primreg import REGISTER_COMPOSITE >>> @REGISTER_COMPOSITE('softmax') >>> def softmax_composite(x, axis): ... molecular = paddle.exp(x) ... denominator = paddle.broadcast_to(sum(molecular, axis=axis, keepdim=True), x.shape) ... res = paddle.divide(molecular, denominator) ... return res rVr>cB>^UU4Sjn[RTU5 g)Ncb>URT:XdSURST35eT"U0UD6$rdrerfs r rh3REGISTER_COMPOSITE..wrapper.._lowersE77g% EbggYN^_f^gh %d%f% %r)r:rrks` r rZ#REGISTER_COMPOSITE..wrappers & 0rrnros` r REGISTER_COMPOSITErvs72 gs # #7W aHII1 Nrcn^[T[5(d[S[T5S35eU4SjnU$)ar Decorator for registering the lower function for an primitive op into sequence of original ops. Args: op_type(str): The op name Returns: wrapper: Inner wrapper function Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> import paddle >>> from paddle.incubate.autograd.primreg import REGISTER_PRIM2ORIG >>> from paddle.incubate.autograd.utils import get_input_var_list >>> @REGISTER_PRIM2ORIG('tanh_p') >>> def tanh_prim2orig(op): ... x, = get_input_var_list(op) ... return paddle.tanh(x) ... rVr>cB>^UU4Sjn[RTU5 g)Ncj>URT:XdSURST35eT"U/UQ70UD6$rdrerfs r rh3REGISTER_PRIM2ORIG..wrapper.._lower-rjr)r1rrks` r rZ#REGISTER_PRIM2ORIG..wrapper,rmrrnros` r REGISTER_PRIM2ORIGr|s70 gs # #7W aHII- Nrcn^[T[5(d[S[T5S35eU4SjnU$)a Decorator for registering the JVP function for a primitive op. Args: op_type(str): The op name Returns: wrapper: Inner wrapper function Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> from paddle.incubate.autograd import primops >>> from paddle.incubate.autograd.primreg import REGISTER_JVP >>> @REGISTER_JVP('add_p') >>> def add_jvp(op, x_dot, y_dot): ... return primops.add(x_dot, y_dot) rVr>cD>^UU4Sjn[RTU5 T$)Ncj>URT:XdSURST35eT"U/UQ70UD6$rdrerfs r _jvp+REGISTER_JVP..wrapper.._jvpRrjr)r4r)rXrrYs` r rZREGISTER_JVP..wrapperQs * Wd+rrnros` r REGISTER_JVPr87, gs # #7W aHII Nrcn^[T[5(d[S[T5S35eU4SjnU$)a Decorator for registering the transpose function for a primitive op that denotes a linear operation in the forward AD graph. Args: op_type(str): The op name Returns: wrapper: Inner wrapper function Examples: .. code-block:: python >>> # doctest: +SKIP('Depends on external code.') >>> from paddle.incubate.autograd.primreg import REGISTER_TRANSPOSE >>> @REGISTER_TRANSPOSE('add_p') >>> def add_transpose(op, z_bar): ... return z_bar, z_bar rVr>cD>^UU4Sjn[RTU5 T$)Ncj>URT:XdSURST35eT"X/UQ70UD6$rdre)rI dot_checkerrJrgrXrYs r _transpose7REGISTER_TRANSPOSE..wrapper.._transposexsJ77g% EbggYN^_f^gh %R6t6v6 6r)r7r)rXrrYs` r rZ#REGISTER_TRANSPOSE..wrapperws 7 ""7J7rrnros` r REGISTER_TRANSPOSEr^rrN)rr(r.r1r4r7r@r:r+r/r2r5r8r;rOrTr`rprvr|rrrrr rs ""&k " k " k " |$ /0$%?@+&%%%&,)+\'T"J%P%P$N#L#r