o "jO,@sGdddZedZedZedZedZedZedZedZd d Zd d Z d dZ ddZ ddZ ddZ ddZddZddZddZddZdd Zd!d"Zd#d$Zd%S)&c@s0eZdZdZddgZddZddZdd Zd S) RegistryzA general registry object.nametabcCs||_i|_dSN)rrselfrra/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/incubate/autograd/primreg.py__init__s zRegistry.__init__cCs(||jvs Jd|d||j|<dS)Nzname "z"" should not be registered before.)r)rrvaluerrrregisters  zRegistry.registercCs |j|Sr)rgetrrrrlookups zRegistry.lookupN)__name__ __module__ __qualname____doc__ __slots__r r r rrrrrs  rZ primop_fnZ orig2primZ prim2origZ primop_jvpZprimop_transposeZprimop_position_argnamesZ compositecC t|Sr) _primop_fnr Zoptyperrr lookup_fn, rcCrr) _orig2primr rrrrlookup_orig2prim0rrcCrr) _prim2origr rrrrlookup_prim2orig4rrcCrr) _primop_jvpr rrrr lookup_jvp8rrcCrr)_primop_transposer rrrrlookup_transpose<rrcCrr)_composite_opsr rrrrlookup_composite@rr!cCst|j}|dusJd|jd|^}}g}|D]1}tt|jj||}t|dks:Jdt|dt|dkrF| |q| |dq|S)a4 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. Nzargs 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_argnamesr typelistmapblockvarinputlenappend)opargsZ input_names_Zinputsrvarsrrrop_position_inputsDs      r2cCs~t|j}|dusJd|^}}tt|jj||}t|dks/Jdt|dt|dkr9|}|S|d}|S)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. Nz0args 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%r r&r'r(r)r*outputr,)r.r/r0Z output_nameZoutvarsr3rrrop_position_outputrs   r4cs:ttstdtdt|fdd}|S)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#cst||Sr)rr fop_typerrwrappers zREGISTER_FN..wrapper) isinstancestr TypeErrorr&r%r )r9Zposition_argnamesr:rr8r REGISTER_FNs   r>c.ttstdtdfdd}|S)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) r5r#cfdd}t|dS)Nc6|jksJd|jd|g|Ri|SNz3op.type should be equal to op_type, but op.type is z and op_type is r&r.r/kwargsr7r9rr_lower z3REGISTER_ORIG2PRIM..wrapper.._lower)rr r7rGr8r6rr:z#REGISTER_ORIG2PRIM..wrapperr;r<r=r&r9r:rr8rREGISTER_ORIG2PRIM   rMcr?)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 r5r#cr@)Ncs.|jksJd|jd|i|SrBrCrDrFrrrGs z3REGISTER_COMPOSITE..wrapper.._lower)r r rIr8r6rr:rJz#REGISTER_COMPOSITE..wrapperrKrLrr8rREGISTER_COMPOSITErNrOcr?)a 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) ... r5r#cr@)NcrArBrCrDrFrrrG-rHz3REGISTER_PRIM2ORIG..wrapper.._lower)rr rIr8r6rr:,rJz#REGISTER_PRIM2ORIG..wrapperrKrLrr8rREGISTER_PRIM2ORIGs   rPcr?)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) r5r#cfdd}t|S)NcrArBrCrDrFrr_jvpRrHz+REGISTER_JVP..wrapper.._jvp)rr )r7rRr8r6rr:Q zREGISTER_JVP..wrapperrKrLrr8r REGISTER_JVP8   rTcr?)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 r5r#crQ)Ncs8|jksJd|jd||g|Ri|SrBrC)r.Z dot_checkerr/rErFrr _transposexs z7REGISTER_TRANSPOSE..wrapper.._transpose)rr )r7rVr8r6rr:wrSz#REGISTER_TRANSPOSE..wrapperrKrLrr8rREGISTER_TRANSPOSE^rUrWN)rrrrrrr%r rrrrrr!r2r4r>rMrOrPrTrWrrrrs,.*%((' &