U
    KcW                     @   sJ  d dl Z d dlZd dlZd dlZd dlZd dlmZ d dlmZm	Z	 d dl
Zd dlZd dlmZ eedoreedZe jdd Zd	d
 ZG dd deZejjZejjZeedddZi ZG dd deZdd Zdd Zdd Zdd ZG dd deZG dd dZ G dd dej!Z"G d d! d!e"Z#G d"d# d#ej!Z$e$ Z%dS )$    N)ABC)AnyDict)_utils_internalgetdlopenflagssetdlopenflagsc                  c   s4   t rt } t| tjB  dV  t r0t|  dS )z
    Context manager to set the RTLD_GLOBAL dynamic linker flag while we open a
    shared library to load custom operators.
    N)_SET_GLOBAL_FLAGSsysr   r   ctypesRTLD_GLOBAL)Z	old_flags r   ./tmp/pip-unpacked-wheel-gikjz4vx/torch/_ops.pydl_open_guard   s    r   c                 C   s   t j|  |p|| jkS N)torch_C%_dispatch_has_kernel_for_dispatch_keyname
py_kernelsopkr   r   r   has_key!   s    r   c                   @   s$   e Zd Zdd Zdd Zdd ZdS )PyOperatorABCc                 O   s   d S r   r   selfargskwargsr   r   r   __call__*   s    zPyOperatorABC.__call__c                 C   s   d S r   r   )r   dispatch_keyfnr   r   r   py_impl-   s    zPyOperatorABC.py_implc                 C   s   d S r   r   r   r   r   r   r   0   s    zPyOperatorABC.nameN)__name__
__module____qualname__r   r!   r   r   r   r   r   r   )   s   r   r   c                 C   sP  t | |r|S tj}|tjks(t||r6t | |r6|S tj}|tjksPt||r^t | |r^|S tj| 	 tj
|pt | tj}tj}|tjkrt||rt | |r|s|S tj}|tjkst||rt | |r|tjkrtj| 	 tjjrtdn
|s|S tj}t||r2t | |r2|S tj|rD|S tdd S )Nzambiguous autogradother kernelzcould not find kernel)r   DispatchKeyZ&CompositeExplicitAutogradNonFunctional	Undefinedis_included_in_aliasZCompositeExplicitAutogradr   r   Z)_dispatch_has_kernel_for_any_dispatch_keyr   Z*_dispatch_get_backend_keyset_from_autogradZ%CompositeImplicitAutogradNestedTensorCompositeImplicitAutogradZAutogradOtherZ _dispatch_autogradother_backendsRuntimeErrorZAutogradZ_dispatch_has_backend_fallback)r   r   ZcandZhas_backend_kernelr   r   r   resolve_key9   sf    
   

  
r+   c                   @   sD   e Zd Zdd Zdd Zdd Zdd Zd	d
 Zdd Zdd Z	dS )
PyOperatorc                 C   s$   || _ i | _i | _|| _| t|< d S r   )_nametablepython_key_mode_tabler#   pyop_namespacer   r   r   r   r   __init__q   s
    zPyOperator.__init__c                 C   s   |  | || j|< d S r   )_fallthrough_fnr.   )r   r   r   r   r   fallthroughz   s    zPyOperator.fallthroughc                    s    fdd}|S )Nc                    s   t  r:t tjjjr: }|jks,t| j|< | S  }|tj	j
jksTtdt|tj	j
sft|jkstt| j|< | S NzGPlease register a mode for the torch._C.DispatchKey.Python key instead.)inspectisclass
issubclassr   utils_python_dispatchTorchDispatchModer/   AssertionErrorr   r&   Python
isinstancer.   )r    moder   dispatch_key_or_moder   r   r   inner~   s"     

z!PyOperator.py_impl.<locals>.innerr   r   rA   rB   r   r@   r   r!   }   s    zPyOperator.py_implc                 O   s|   ddl m} |tjjjkr^t| }|d k	s4td|| jksNtd| d| j| ||S || j	kslt| j	| ||S )Nr   _get_current_dispatch_modeMIllegal invocation of dispatch on torch._C.DispatchKey.Python without a mode.zCurrent active mode z not registered)
torch.utils._python_dispatchrE   r   r   r&   r=   typer<   r/   r.   )r   r   r   r   rE   	curr_moder   r   r   dispatch   s    

zPyOperator.dispatchc                 O   sL   t ||}tj|r,tjj| |f||S t||}| j| f||S r   )_to_flat_tupler   Z	overridesZhas_torch_functionZhandle_torch_function_compute_keysetrJ   highestPriorityTypeId)r   r   r   	flat_argsZdispatch_key_setr   r   r   r      s    
 
zPyOperator.__call__c                 C   s   | j S r   )r   r"   r   r   r   r      s    zPyOperator.namec                    s    fdd}|S )Nc                     s0   t j }|t| |@ }j| f| |S r   )r   r   Z_dispatch_keyset_full_afterrL   rJ   rM   )r   r   Zall_keys_after_currentZall_keys_after_current_maskedr   r   r   r   rB      s     z)PyOperator._fallthrough_fn.<locals>.innerr   )r   operatorr   rB   r   rO   r   r3      s    	zPyOperator._fallthrough_fnN)
r#   r$   r%   r2   r4   r!   rJ   r   r   r3   r   r   r   r   r,   p   s   	
r,   c                 C   s0   t jj| \}}t jj|\}}|| }|S r   )r   r9   Z_pytreeZtree_flatten)r   r   rN   _Zflat_kwargsflat_allr   r   r   rK      s    rK   c                 C   s   t | |}t|S r   )_get_tensorskey_extractor)r   r   tensorsr   r   r   rL      s    
rL   c                 C   s    t | |}dd |D }t|S )Nc                 S   s   g | ]}t |tjr|qS r   )r>   r   ZTensor).0tr   r   r   
<listcomp>   s      z _get_tensors.<locals>.<listcomp>)rK   tuple)r   r   rR   Ztensor_argsr   r   r   rS      s    
rS   c                 C   s6   t j }| D ]}|t j|B }q|t j  }|S r   )r   r   Z_dispatch_tls_local_include_setZ_dispatch_keysZ_dispatch_tls_local_exclude_set)rU   Zkey_setZtensorr   r   r   rT      s
    
rT   c                   @   s   e Zd Zdd ZdddZdd Zdd	 Zd
d Zdd Ze	dd Z
dd Zdd Zdd Zdd Ze	dd Ze	dd Ze	dd ZdS )
OpOverloadc                 C   s   || _ || _|| _|| _|| _|jdkr,dn|j| _| jj| _|jrX|  jd|j 7  _i | _	d
| jjdd | j| _i | _|j| _|j|_| j| _i | _d S )N default.{}.{}::   )_op_op_dk_schema_overloadpacket_tagsZoverload_name_overloadnamer   r-   r   formatsplitr#   r/   r$   r%   __annotations__)r   overloadpacketr   Zop_dkschematagsr   r   r   r2      s(    
 zOpOverload.__init__Nc                 C   s   | S r   r   r   memor   r   r   __deepcopy__   s    zOpOverload.__deepcopy__c                 C   s   dj | jjd| jf S )Nz'<OpOverload(op='{}.{}', overload='{}')>r_   rg   rc   r   rh   rf   r"   r   r   r   __repr__   s     zOpOverload.__repr__c                 O   s   | j ||pi S r   ra   r   r   r   r   r      s    zOpOverload.__call__c                 C   s
   t | jS r   hashra   r"   r   r   r   __hash__  s    zOpOverload.__hash__c                 C   s   dj | jjd| jf S )Nz{}.{}.{}r_   rp   r"   r   r   r   __str__  s    zOpOverload.__str__c                 C   s   | j jdd S )Nr_   r   )rc   r   rh   r"   r   r   r   	namespace
  s    zOpOverload.namespacec                 O   sP   t jjj}|| jkr$| j| ||S t j|  |rH| j|f||S tS d S r   )	r   r   r&   r)   r   r   r   rb   NotImplemented)r   r   r   Zdkr   r   r   	decompose  s    

zOpOverload.decomposec                    s    fdd}|S )Nc                    sp   t  r:t tjjjr: }|jks,t| j|< | S t	 tj
jsLt tj
jjksbtd| j < | S r5   )r6   r7   r8   r   r9   r:   r;   r/   r<   r>   r   r&   r=   r   )r    r?   r@   r   r   rB     s     

z!OpOverload.py_impl.<locals>.innerr   rC   r   r@   r   r!     s    zOpOverload.py_implc              
      s   t |dks|d  st ztj| W n( tk
rV } z
t W 5 d }~X Y nX  tjjjkrj	s|t
|   S  fdd}t
|| |S t  j  }t
|| |S )Nr   c                     sR   ddl m} t| }|d k	s&td|jkrBj f| |S j| | |S )Nr   rD   rF   )rG   rE   rH   r<   r/   rb   )r   r   rE   rI   keyr   r   r   handler?  s    

z'OpOverload.__getattr__.<locals>.handler)lenisupperAttributeErrorr   r   Z_dispatch_key_parse	Exceptionr&   r=   r/   setattrr+   r   get)r   attrer|   rr   rz   r   __getattr__1  s"    
zOpOverload.__getattr__c                 C   s   | j S r   )r-   r"   r   r   r   r   W  s    zOpOverload.namec                 C   s   | j S r   )rd   r"   r   r   r   rj   Z  s    zOpOverload.overloadpacketc                 C   s   | j S r   rr   r"   r   r   r   r   ^  s    zOpOverload.opc                 C   s   | j S r   )re   r"   r   r   r   rl   b  s    zOpOverload.tags)N)r#   r$   r%   r2   ro   rq   r   ru   rv   propertyrw   ry   r!   r   r   rj   r   rl   r   r   r   r   rZ      s$   

&

rZ   c                   @   sZ   e Zd Zdd ZdddZdd Zdd	 Zd
d Zedd Z	dd Z
dd Zdd ZdS )OpOverloadPacketc                 C   s   || _ || _|| _|| _d S r   )_qualified_op_namer#   ra   _overload_names)r   qualified_op_nameop_namer   overload_namesr   r   r   r2   l  s    zOpOverloadPacket.__init__Nc                 C   s   | S r   r   rm   r   r   r   ro   u  s    zOpOverloadPacket.__deepcopy__c                 C   s   dj | jd S )Nz<OpOverloadPacket(op='{}.{}')>r_   rg   r   rh   r"   r   r   r   rq   x  s    
zOpOverloadPacket.__repr__c                 C   s
   t | jS r   rs   r"   r   r   r   ru   }  s    zOpOverloadPacket.__hash__c                 C   s   dj | jd S )Nr^   r_   r   r"   r   r   r   rv     s    zOpOverloadPacket.__str__c                 C   s   | j S r   rr   r"   r   r   r   r     s    zOpOverloadPacket.opc              	   C   s   |dkrdS z| dr&t| j|W S W n2 tk
rZ   tdt| t| j|d Y nX zX|dkrjdn|}tj| j	|\}}}tj
| j	|}t| ||||}t| || |W S  tk
r   tdt| |d Y nX d S )N__file__	torch.ops__zi'{}' can't have an overload name beginning with '__' and the underlying op {} has no attribute {} either.r\   r[   z3The underlying op of '{}' has no overload name '{}')
startswithgetattrra   r   rg   strr   r   Z_get_operation_overloadr   Z_get_schemarZ   r   r*   )r   r{   Zuse_keyZop_Zop_dk_rl   rk   overloadr   r   r   r     sD    	
   
 zOpOverloadPacket.__getattr__c                 O   s   | j ||pi S r   rr   r   r   r   r   r     s    zOpOverloadPacket.__call__c                 C   s   dd | j D S )Nc                 S   s   g | ]}|r|nd qS )r\   r   )rV   nr   r   r   rX     s     z.OpOverloadPacket.overloads.<locals>.<listcomp>)r   r"   r   r   r   	overloads  s    zOpOverloadPacket.overloads)N)r#   r$   r%   r2   ro   rq   ru   rv   r   r   r   r   r   r   r   r   r   r   k  s   	

.r   c                       s(   e Zd ZdZ fddZdd Z  ZS )_OpNamespacea0  
    An op namespace to dynamically bind Operators into Python.

    Say a user has created a custom Operator called "my_namespace::my_op". To
    call this op, the user will write torch.ops.my_namespace.my_op(...).
    At startup, this operation will not yet be bound into Python. Instead, the
    following sequence of magic tricks will occur:
    1. `torch.ops.my_namespace` will invoke the `__getattr__` magic method
       on the `torch.ops` object, which will create a new `_OpNamespace`
       object called `my_namespace` and set it as an attribute on the `ops`
       object.
    2. `torch.ops.my_namespace.my_op` will then invoke `__getattr__` on
       the `my_namespace` object, which will retrieve the operation via
       `torch.get_operation`, a function bound from C++, and then in a similar
       fashion bind this new object onto the `my_namespace` object.
    3. `torch.ops.my_namespace.my_op(...)` then calls this new operation
        and subsequent accesses will incur no further lookup (the namespace and
        operation will already exist).
    c                    s   t t| d|  || _d S )Nz
torch.ops.)superr   r2   r   r1   	__class__r   r   r2     s    z_OpNamespace.__init__c              
   C   s   |dkrdS |dkrt  | j}d||}ztj|\}}W n< tk
r| } zt d| j d| d|W 5 d }~X Y nX tjj	|| | j
d | |_
t||||}| j
d | |_
t| || |S )	Nr   r   
__origin__z{}::{}z'_OpNamespace' 'z' object has no attribute ''r]   )r   r   rg   r   r   Z_jit_get_operationr*   ZjitZ	_builtinsZ_register_builtinr$   r   r   )r   r   Znamespace_namer   r   r   r   Zopoverloadpacketr   r   r   r     s2       z_OpNamespace.__getattr__)r#   r$   r%   __doc__r2   r   __classcell__r   r   r   r   r     s   r   c                       s   e Zd Z fddZ  ZS )_PyOpNamespacec                    s   t t| d t| _d S Nr   )r   r   r2   r0   r"   r   r   r   r2     s    z_PyOpNamespace.__init__)r#   r$   r%   r2   r   r   r   r   r   r     s   r   c                       s0   e Zd ZdZ fddZdd Zdd Z  ZS )_Opsz_ops.pyc                    s$   t t| d t | _t | _d S r   )r   r   r2   setloaded_librariesr   pyopsr"   r   r   r   r2     s    z_Ops.__init__c                 C   s0   || j jkr| j j| S t|}t| || |S r   )r   r0   r   r   )r   r   rw   r   r   r   r     s
    z_Ops.__getattr__c              	   C   sD   t jdkrdS t|}t  t| W 5 Q R X | j| dS )a  
        Loads a shared library from the given path into the current process.

        The library being loaded may run global initialization code to register
        custom operators with the PyTorch JIT runtime. This allows dynamically
        loading custom operators. For this, you should compile your operator
        and the static registration code into a shared library object, and then
        call ``torch.ops.load_library('path/to/libcustom.so')`` to load the
        shared object.

        After the library is loaded, it is added to the
        ``torch.ops.loaded_libraries`` attribute, a set that may be inspected
        for the paths of all libraries loaded using this function.

        Args:
            path (str): A path to a shared library to load.
        Ztorch_deployN)	r	   
executabler   Zresolve_library_pathr   r
   CDLLr   add)r   pathr   r   r   load_library#  s    

z_Ops.load_library)r#   r$   r%   r   r2   r   r   r   r   r   r   r   r     s   
r   )&
contextlibr
   r6   r	   typesabcr   typingr   r   Ztorch._Cr   Z	torch.jitr   hasattrr   contextmanagerr   r   r   r   Z_dispatch_is_included_in_aliasr(   r&   r+   r0   r,   rK   rL   rS   rT   rZ   r   
ModuleTyper   r   r   opsr   r   r   r   <module>   s<   
4Q
 e;1