U
    hdQ                     @   s  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mZm	Z	m
Z
mZmZ d dlmZmZ d dlmZ d dlmZ d dlmZ d dlmZmZ d dlmZ d d	lmZ d d
lmZmZ d dl m!Z! d dl"m#Z# d dl$m%Z% d dl&m'Z'm(Z( d dl)m
Z* d dl+m,Z, d dl+m-Z- d dl.m/Z/ dddddddddddddd d!d"gZ0G d#d$ d$ej1Z2G d%d& d&e3Z4G d'd( d(Z5G d)d* d*eZ6G d+d, d,eZ7G d-d. d.eej1Z8dS )/    N)config	serializesigutilstypestypingutils)Cache	CacheImpl)global_compiler_lock)
Dispatcher)NumbaPerformanceWarning)Purposetypeof)get_current_device)wrap_arg)compile_cudaCUDACompiler)driver)get_context)cuda_target)missing_launch_config_msgnormalize_kernel_dimensions)r   cuda)_dispatcher)warnZhsinZhcosZhlogZhlog10Zhlog2ZhexpZhexp10Zhexp2ZhsqrtZhrsqrtZhfloorZhceilZhrcpZhrintZhtruncZhdivc                
       s   e Zd ZdZed0 fdd	Zedd Zed	d
 Zdd Z	edd Z
edd Ze fddZdd Zdd Zedd Zedd Zedd Zedd Zedd  Zd!d" Zd#d$ Zd%d& Zd1d'd(Zd2d*d+Zd3d,d-Zd.d/ Z  ZS )4_Kernelz
    CUDA Kernel specialized for a given set of argument types. When called, this
    object launches the kernel on the device.
    NFTc                    s  |rt dt   d| _d | _|| _|| _|| _|| _|p@g | _	||
rNdndd}t
 j}t| jtj| j| j|||||d	}|j}| jj}|j}|j}||j|j||||||	\ }|sg }d  k| _| jrd _ fd	d
tD }|r>tjsd}d| }t|tjtj t!}tj"|d}|#| |D ]} $| qB|j%| _&|j'| _'|j(| _) | _*|j+| _+|| _|j| _|j,| _,g | _-g | _.g | _/d S )Nz,Cannot compile a device function as a kernelF   r   )fastmathoptdebuglineinfoinliner   nvvm_optionsccZcudaCGGetIntrinsicHandleTc                    s"   g | ]}d |    kr|qS )Z__numba_wrapper_)get_asm_str).0fnlib N/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/numba/cuda/dispatcher.py
<listcomp>l   s    z$_Kernel.__init__.<locals>.<listcomp>z9https://numba.readthedocs.io/en/stable/cuda/bindings.htmlzUse of float16 requires the use of the NVIDIA CUDA bindings and setting the NUMBA_CUDA_USE_NVIDIA_BINDING environment variable to 1. Relevant documentation is available here:
zcpp_function_wrappers.cu)0RuntimeErrorsuper__init__Z
objectmodeentry_pointpy_funcargtypesr!   r"   
extensionsr   compute_capabilityr   r   voidtarget_context__code__co_filenameco_firstlinenoZprepare_cuda_kernellibraryfndescr&   cooperativeZneeds_cudadevrtcuda_fp16_math_funcsr   ZCUDA_USE_NVIDIA_BINDINGNotImplementedErrorospathdirnameabspath__file__joinappendZadd_linking_filename
entry_name	signaturetype_annotation_type_annotation_codelibrarycall_helperenvironment_referenced_environmentsZliftedZreload_init)selfr2   r3   linkr!   r"   r#   r   r4   Zmax_registersr   devicer$   r%   cresZtgt_ctxcodefilenameZlinenumkernelressmsgZbasedirZfunctions_cu_pathfilepath	__class__r)   r,   r0   .   s~    


   

z_Kernel.__init__c                 C   s   | j S N)rL   rP   r+   r+   r,   r;      s    z_Kernel.libraryc                 C   s   | j S r]   )rK   r^   r+   r+   r,   rJ      s    z_Kernel.type_annotationc                 C   s   | j S r]   )rO   r^   r+   r+   r,   _find_referenced_environments   s    z%_Kernel._find_referenced_environmentsc                 C   s
   | j  S r]   )r7   codegenr^   r+   r+   r,   r`      s    z_Kernel.codegenc                 C   s   t | jjS r]   )tuplerI   argsr^   r+   r+   r,   argument_types   s    z_Kernel.argument_typesc	           
         sX   |  | }	t| |	  d|	_||	_||	_||	_d|	_||	_||	_	||	_
||	_||	_|	S )&
        Rebuild an instance.
        N)__new__r/   r0   r1   r=   rH   rI   rK   rL   r!   r"   rM   r4   )
clsr=   rG   rI   codelibraryr!   r"   rM   r4   instancer[   r+   r,   _rebuild   s    
z_Kernel._rebuildc              
   C   s(   t | j| j| j| j| j| j| j| jdS )a  
        Reduce the instance for serialization.
        Compiled definitions are serialized in PTX form.
        Type annotation are discarded.
        Thread, block and shared memory configuration are serialized.
        Stream information is discarded.
        )r=   rG   rI   rg   r!   r"   rM   r4   )	dictr=   rH   rI   rL   r!   r"   rM   r4   r^   r+   r+   r,   _reduce_states   s    
   z_Kernel._reduce_statesc                 C   s   | j   dS )z7
        Force binding to current CUDA context
        N)rL   
get_cufuncr^   r+   r+   r,   bind   s    z_Kernel.bindc                 C   s   | j  jjS )zN
        The number of registers used by each thread for this kernel.
        )rL   rl   attrsregsr^   r+   r+   r,   regs_per_thread   s    z_Kernel.regs_per_threadc                 C   s   | j  jjS )zD
        The amount of constant memory used by this kernel.
        )rL   rl   rn   constr^   r+   r+   r,   const_mem_size   s    z_Kernel.const_mem_sizec                 C   s   | j  jjS )zM
        The amount of shared memory used per block for this kernel.
        )rL   rl   rn   Zsharedr^   r+   r+   r,   shared_mem_per_block   s    z_Kernel.shared_mem_per_blockc                 C   s   | j  jjS )z:
        The maximum allowable threads per block.
        )rL   rl   rn   Z
maxthreadsr^   r+   r+   r,   max_threads_per_block   s    z_Kernel.max_threads_per_blockc                 C   s   | j  jjS )zM
        The amount of local memory used per thread for this kernel.
        )rL   rl   rn   localr^   r+   r+   r,   local_mem_per_thread   s    z_Kernel.local_mem_per_threadc                 C   s
   | j  S )z6
        Returns the LLVM IR for this kernel.
        )rL   get_llvm_strr^   r+   r+   r,   inspect_llvm   s    z_Kernel.inspect_llvmc                 C   s   | j j|dS )z7
        Returns the PTX code for this kernel.
        r%   )rL   r&   )rP   r%   r+   r+   r,   inspect_asm   s    z_Kernel.inspect_asmc                 C   s
   | j  S )zp
        Returns the SASS code for this kernel.

        Requires nvdisasm to be available on the PATH.
        )rL   Zget_sassr^   r+   r+   r,   inspect_sass   s    z_Kernel.inspect_sassc                 C   sb   | j dkrtd|dkr tj}td| j| jf |d td|d t| j |d td|d dS )
        Produce a dump of the Python source of this function annotated with the
        corresponding Numba IR and type information. The dump is written to
        *file*, or *sys.stdout* if *file* is *None*.
        Nz Type annotation is not availablez%s %sfilezP--------------------------------------------------------------------------------zP================================================================================)rK   
ValueErrorsysstdoutprintrH   rc   )rP   r~   r+   r+   r,   inspect_types  s    
z_Kernel.inspect_typesr   c                 C   sH   t  }| j }t|tr*tdd |}||||}|jj	}|| S )a  
        Calculates the maximum number of blocks that can be launched for this
        kernel in a cooperative grid in the current context, for the given block
        and dynamic shared memory sizes.

        :param blockdim: Block dimensions, either as a scalar for a 1D block, or
                         a tuple for 2D or 3D blocks.
        :param dynsmemsize: Dynamic shared memory size in bytes.
        :return: The maximum number of blocks in the grid.
        c                 S   s   | | S r]   r+   )xyr+   r+   r,   <lambda>(      z5_Kernel.max_cooperative_grid_blocks.<locals>.<lambda>)
r   rL   rl   
isinstancera   	functoolsreduceZ$get_active_blocks_per_multiprocessorrR   ZMULTIPROCESSOR_COUNT)rP   blockdimZdynsmemsizectxcufuncZactive_per_smZsm_countr+   r+   r,   max_cooperative_grid_blocks  s    

z#_Kernel.max_cooperative_grid_blocksc                    s  | j   | jrT jd } j|\}}|ttjks>t	t }	|j
d|d g }
g }t| j|D ]\}}| ||||
| qhtjrtjd}nd }|r|jp|}tj jf|||||fd| ji | jrtt|	|| |	jdkr fddfddd	D }fd
dd	D }|	j}| j|\}}}|d krNd}n$|\}}}tj|}d|||f }d|||f }|rd||d f f|dd   }n|f}|| |
D ]}|  qd S )NZ__errcode__r   )streamr=   c                    s<    j d j| f \}}t }tt||| |jS )Nz%s__%s__)	moduleget_global_symbolrG   ctypesc_intr   device_to_host	addressofvalue)rG   Zmemszval)r   r+   r,   load_symbolU  s    
z#_Kernel.launch.<locals>.load_symbolc                    s   g | ]} d | qS )tidr+   r'   ir   r+   r,   r-   ]  s     z"_Kernel.launch.<locals>.<listcomp>Zzyxc                    s   g | ]} d | qS )ctaidr+   r   r   r+   r,   r-   ^  s      z"In function %r, file %s, line %s, z%stid=%s ctaid=%sz%s: %s   )rL   rl   r!   rG   r   r   r   Zsizeofr   AssertionErrorZmemsetziprc   _prepare_argsr   USE_NV_BINDINGZbindingZCUstreamhandleZlaunch_kernelr=   r   r   r   rM   Zget_exceptionr@   rA   rC   )rP   rb   griddimr   r   	sharedmemexcnameZexcmemZexcszZexcvalretr
kernelargstvZzero_streamZstream_handler   r   rT   ZexcclsZexc_argslocZlocinfosymrZ   linenoprefixwbr+   )r   r   r,   launch/  sb    





z_Kernel.launchc                 C   sh  t | jD ]}|j||||d\}}q
t|tjrt|||}tj	}t
d}	t
d}
||j}||jj}t|}tjrt|}t
|}||	 ||
 || || || t|jD ]}|||j|  qt|jD ]}|||j|  qnPt|tjrBttd| |}|| n"|tjkrttt|tj}|| n|tjkrt|}|| n|tj krt!|}|| n|tj"krt#t|}|| n|tj$kr|t!|j% |t!|j& nL|tj'krL|t|j% |t|j& nt|tj(tj)frz|t*|tj+ nt|tj,rt|||}|j-}tjrt
t|}|| nt|tj.rt/|t/|kst0t1||D ]\}}| 2||||| qnVt|tj3rZz| 2|j|j4||| W n  t5k
rV   t5||Y nX n
t5||dS )zF
        Convert arguments to ctypes and append to kernelargs
        )r   r   r   zc_%sN)6reversedr4   Zprepare_argsr   r   ZArrayr   Z	to_devicer   Z	c_ssize_tZc_void_psizeZdtypeitemsizer   Zdevice_pointerr   intrF   rangendimshapestridesIntegergetattrZfloat16Zc_uint16npviewZuint16Zfloat64Zc_doubleZfloat32Zc_floatbooleanZc_uint8Z	complex64realimagZ
complex128Z
NPDatetimeZNPTimedeltaZc_int64Zint64ZRecordZdevice_ctypes_pointerZ	BaseTuplelenr   r   r   Z
EnumMemberr   r?   )rP   tyr   r   r   r   	extensionZdevaryZc_intpZmeminfoparentZnitemsr   ZptrdataZaxcvalZdevrecr   r   r+   r+   r,   r   w  s    











    z_Kernel._prepare_args)	NFFFFNNTF)N)r   )r   r   )__name__
__module____qualname____doc__r
   r0   propertyr;   rJ   r_   r`   rc   classmethodri   rk   rm   rp   rr   rs   rt   rv   rx   rz   r{   r   r   r   r   __classcell__r+   r+   r[   r,   r   (   sR                f











Hr   c                   @   s$   e Zd Zdd Zdd Zdd ZdS )ForAllc                 C   s6   |dk rt d| || _|| _|| _|| _|| _d S )Nr   z0Can't create ForAll with negative task count: %s)r   
dispatcherntasksthread_per_blockr   r   )rP   r   r   tpbr   r   r+   r+   r,   r0     s    zForAll.__init__c                 G   s^   | j dkrd S | jjr| j}n| jj| }| |}| j | d | }|||| j| jf | S )Nr   r   )r   r   specialized
specialize_compute_thread_per_blockr   r   )rP   rb   r   r   r   r+   r+   r,   __call__  s    


zForAll.__call__c                 C   sZ   | j }|dkr|S t }tt|j }t|j d| j	dd}|j
f |\}}|S d S )Nr   i   )funcZb2d_funcZmemsizeZblocksizelimit)r   r   nextiter	overloadsvaluesrj   rL   rl   r   Zget_max_potential_block_size)rP   r   r   r   rV   kwargs_r+   r+   r,   r     s    z ForAll._compute_thread_per_blockN)r   r   r   r0   r   r   r+   r+   r+   r,   r     s   
r   c                   @   s   e Zd Zdd Zdd ZdS )_LaunchConfigurationc           	      C   sd   || _ || _|| _|| _|| _tjr`d}|d |d  |d  }||k r`d| d}tt| d S )N   r   r      z
Grid size zB will likely result in GPU under-utilization due to low occupancy.)	r   r   r   r   r   r   ZCUDA_LOW_OCCUPANCY_WARNINGSr   r   )	rP   r   r   r   r   r   Zmin_grid_sizeZ	grid_sizerY   r+   r+   r,   r0     s    	z_LaunchConfiguration.__init__c                 G   s   | j || j| j| j| jS r]   )r   callr   r   r   r   rP   rb   r+   r+   r,   r     s     z_LaunchConfiguration.__call__N)r   r   r   r0   r   r+   r+   r+   r,   r     s   r   c                   @   s$   e Zd Zdd Zdd Zdd ZdS )CUDACacheImplc                 C   s   |  S r]   )rk   )rP   rV   r+   r+   r,   r   "  s    zCUDACacheImpl.reducec                 C   s   t jf |S r]   )r   ri   )rP   r7   payloadr+   r+   r,   rebuild%  s    zCUDACacheImpl.rebuildc                 C   s   dS )NTr+   )rP   rS   r+   r+   r,   check_cachable(  s    zCUDACacheImpl.check_cachableN)r   r   r   r   r   r   r+   r+   r+   r,   r   !  s   r   c                   @   s   e Zd ZdZeZdS )	CUDACachezS
    Implements a cache that saves and loads CUDA kernels and compile results.
    N)r   r   r   r   r   Z_impl_classr+   r+   r+   r,   r   3  s   r   c                       s8  e Zd ZdZdZeZef fdd	Ze	dd Z
dd Zejd	d
d?ddZdd Zd@ddZe	dd Zdd Zdd Zdd Zdd Zdd Ze	dd ZdAd!d"ZdBd#d$ZdCd%d&ZdDd'd(ZdEd)d*Zd+d, ZdFd-d.Zd/d0 Zd1d2 Z dGd3d4Z!dHd5d6Z"dId7d8Z#dJd9d:Z$e%d;d< Z&d=d> Z'  Z(S )KCUDADispatchera  
    CUDA Dispatcher object. When configured and called, the dispatcher will
    specialize itself for the given arguments (if no suitable specialized
    version already exists) & compute capability, and launch on the device
    associated with the current context.

    Dispatcher objects are not to be constructed by the user, but instead are
    created using the :func:`numba.cuda.jit` decorator.
    Fc                    s"   t  j|||d d| _i | _d S )N)targetoptionspipeline_classF)r/   r0   _specializedspecializations)rP   r2   r   r   r[   r+   r,   r0   L  s
    
	zCUDADispatcher.__init__c                 C   s
   t | S r]   )
cuda_typesr   r^   r+   r+   r,   _numba_type_\  s    zCUDADispatcher._numba_type_c                 C   s   t | j| _d S r]   )r   r2   _cacher^   r+   r+   r,   enable_caching`  s    zCUDADispatcher.enable_cachingr   )maxsizer   c                 C   s   t ||\}}t| ||||S r]   )r   r   )rP   r   r   r   r   r+   r+   r,   	configurec  s    zCUDADispatcher.configurec                 C   s   t |dkrtd| j| S )N)r   r      z.must specify at least the griddim and blockdim)r   r   r   r   r+   r+   r,   __getitem__h  s    zCUDADispatcher.__getitem__c                 C   s   t | ||||dS )a3  Returns a 1D-configured dispatcher for a given number of tasks.

        This assumes that:

        - the kernel maps the Global Thread ID ``cuda.grid(1)`` to tasks on a
          1-1 basis.
        - the kernel checks that the Global Thread ID is upper-bounded by
          ``ntasks``, and does nothing if it is not.

        :param ntasks: The number of tasks.
        :param tpb: The size of a block. An appropriate value is chosen if this
                    parameter is not supplied.
        :param stream: The stream on which the configured dispatcher will be
                       launched.
        :param sharedmem: The number of bytes of dynamic shared memory required
                          by the kernel.
        :return: A configured dispatcher, ready to launch on a set of
                 arguments.)r   r   r   )r   )rP   r   r   r   r   r+   r+   r,   forallm  s    zCUDADispatcher.forallc                 C   s   | j dS )aS  
        A list of objects that must have a `prepare_args` function. When a
        specialized kernel is called, each argument will be passed through
        to the `prepare_args` (from the last object in this list to the
        first). The arguments to `prepare_args` are:

        - `ty` the numba type of the argument
        - `val` the argument value itself
        - `stream` the CUDA stream used for the current call to the kernel
        - `retr` a list of zero-arg functions that you may want to append
          post-call cleanup work to.

        The `prepare_args` function must return a tuple `(ty, val)`, which
        will be passed in turn to the next right-most `extension`. After all
        the extensions have been called, the resulting `(ty, val)` will be
        passed into Numba's default argument marshalling logic.
        r4   )r   getr^   r+   r+   r,   r4     s    zCUDADispatcher.extensionsc                 O   s   t td S r]   )r   r   )rP   rb   r   r+   r+   r,   r     s    zCUDADispatcher.__call__c                 C   sB   | j rtt| j }ntjj| f| }|||||| dS )zJ
        Compile if necessary and invoke this kernel with *args*.
        N)	r   r   r   r   r   r   r   Z
_cuda_callr   )rP   rb   r   r   r   r   rV   r+   r+   r,   r     s    zCUDADispatcher.callc                    s(   |rt  fdd|D } t|S )Nc                    s   g | ]}  |qS r+   )typeof_pyvalr'   ar^   r+   r,   r-     s     z4CUDADispatcher._compile_for_args.<locals>.<listcomp>)r   compilera   )rP   rb   kwsr3   r+   r^   r,   _compile_for_args  s    z CUDADispatcher._compile_for_argsc                 C   sN   zt |tjW S  tk
rH   t|rBt tj|ddtj Y S  Y nX d S )NF)sync)r   r   argumentr   r   Zis_cuda_arrayZas_cuda_array)rP   r   r+   r+   r,   r     s    
zCUDADispatcher.typeof_pyvalc                    s   t  j}t fdd|D } jr,td j||f}|rD|S  j}t j	|d}|
| |  d|_| j||f< |S )zd
        Create a new instance of this dispatcher specialized for the given
        *args*.
        c                    s   g | ]} j |qS r+   )Z	typingctxZresolve_argument_typer   r^   r+   r,   r-     s     z-CUDADispatcher.specialize.<locals>.<listcomp>zDispatcher already specialized)r   T)r   r5   ra   r   r.   r   r   r   r   r2   r   Zdisable_compiler   )rP   rb   r%   r3   Zspecializationr   r+   r^   r,   r     s$    
zCUDADispatcher.specializec                 C   s   | j S )z>
        True if the Dispatcher has been specialized.
        )r   r^   r+   r+   r,   r     s    zCUDADispatcher.specializedNc                 C   sH   |dk	r| j |j jS | jr0tt| j  jS dd | j  D S dS )a  
        Returns the number of registers used by each thread in this kernel for
        the device in the current context.

        :param signature: The signature of the compiled kernel to get register
                          usage for. This may be omitted for a specialized
                          kernel.
        :return: The number of registers used by the compiled variant of the
                 kernel for the given signature and current device.
        Nc                 S   s   i | ]\}}||j qS r+   )rp   r'   sigoverloadr+   r+   r,   
<dictcomp>  s    z6CUDADispatcher.get_regs_per_thread.<locals>.<dictcomp>)r   rb   rp   r   r   r   r   itemsrP   rI   r+   r+   r,   get_regs_per_thread  s    z"CUDADispatcher.get_regs_per_threadc                 C   sH   |dk	r| j |j jS | jr0tt| j  jS dd | j  D S dS )a  
        Returns the size in bytes of constant memory used by this kernel for
        the device in the current context.

        :param signature: The signature of the compiled kernel to get constant
                          memory usage for. This may be omitted for a
                          specialized kernel.
        :return: The size in bytes of constant memory allocated by the
                 compiled variant of the kernel for the given signature and
                 current device.
        Nc                 S   s   i | ]\}}||j qS r+   )rr   r   r+   r+   r,   r    s    z5CUDADispatcher.get_const_mem_size.<locals>.<dictcomp>)r   rb   rr   r   r   r   r   r  r  r+   r+   r,   get_const_mem_size  s    z!CUDADispatcher.get_const_mem_sizec                 C   sH   |dk	r| j |j jS | jr0tt| j  jS dd | j  D S dS )a  
        Returns the size in bytes of statically allocated shared memory
        for this kernel.

        :param signature: The signature of the compiled kernel to get shared
                          memory usage for. This may be omitted for a
                          specialized kernel.
        :return: The amount of shared memory allocated by the compiled variant
                 of the kernel for the given signature and current device.
        Nc                 S   s   i | ]\}}||j qS r+   )rs   r   r+   r+   r,   r    s    z;CUDADispatcher.get_shared_mem_per_block.<locals>.<dictcomp>)r   rb   rs   r   r   r   r   r  r  r+   r+   r,   get_shared_mem_per_block  s    z'CUDADispatcher.get_shared_mem_per_blockc                 C   sH   |dk	r| j |j jS | jr0tt| j  jS dd | j  D S dS )a(  
        Returns the maximum allowable number of threads per block
        for this kernel. Exceeding this threshold will result in
        the kernel failing to launch.

        :param signature: The signature of the compiled kernel to get the max
                          threads per block for. This may be omitted for a
                          specialized kernel.
        :return: The maximum allowable threads per block for the compiled
                 variant of the kernel for the given signature and current
                 device.
        Nc                 S   s   i | ]\}}||j qS r+   )rt   r   r+   r+   r,   r  &  s    z<CUDADispatcher.get_max_threads_per_block.<locals>.<dictcomp>)r   rb   rt   r   r   r   r   r  r  r+   r+   r,   get_max_threads_per_block  s    z(CUDADispatcher.get_max_threads_per_blockc                 C   sH   |dk	r| j |j jS | jr0tt| j  jS dd | j  D S dS )a  
        Returns the size in bytes of local memory per thread
        for this kernel.

        :param signature: The signature of the compiled kernel to get local
                          memory usage for. This may be omitted for a
                          specialized kernel.
        :return: The amount of local memory allocated by the compiled variant
                 of the kernel for the given signature and current device.
        Nc                 S   s   i | ]\}}||j qS r+   )rv   r   r+   r+   r,   r  9  s    z;CUDADispatcher.get_local_mem_per_thread.<locals>.<dictcomp>)r   rb   rv   r   r   r   r   r  r  r+   r+   r,   get_local_mem_per_thread)  s    z'CUDADispatcher.get_local_mem_per_threadc                 C   sP   | j r| t| | jj}d|}tj||| jd}t	
| j}||||fS )z
        Get a typing.ConcreteTemplate for this dispatcher and the given
        *args* and *kws* types.  This allows resolution of the return type.

        A (template, pysig, args, kws) tuple is returned.
        zCallTemplate({0}))keyZ
signatures)_can_compilecompile_devicera   r2   r   formatr   Zmake_concrete_templateZnopython_signaturesr   Zpysignature)rP   rb   r   	func_namerG   Zcall_templateZpysigr+   r+   r,   get_call_template<  s    
  z CUDADispatcher.get_call_templatec           
      C   s   || j kr| j | jd}| jd}| jd}| jd}| jdrRdnd|d}t j}t| j||||||||d		}	|	| j |< |	j	|	j
|	j|	jg W 5 Q R X n
| j | }	|	S )
zCompile the device function for the given argument types.

        Each signature is compiled once by caching the compiled function inside
        this object.

        Returns the `CompileResult`.
        r!   r"   r#   r   r   r   r   )r   r   r    )r   Z_compiling_counterr   r   r   r5   r   r2   r7   Zinsert_user_functionr1   r<   r;   )
rP   rb   return_typer!   r"   r#   r   r$   r%   rS   r+   r+   r,   r  W  s2    




zCUDADispatcher.compile_devicec                 C   s,   dd |D }| j ||dd || j|< d S )Nc                 S   s   g | ]
}|j qS r+   )_coder   r+   r+   r,   r-     s     z/CUDADispatcher.add_overload.<locals>.<listcomp>Tr   )Z_insertr   )rP   rV   r3   Zc_sigr+   r+   r,   add_overload~  s    zCUDADispatcher.add_overloadc                 C   s   t |\}}|dks$|tjks$t| jr<tt| j	 S | j
|}|dk	rT|S | j|| j}|dk	r| j|  d7  < nH| j|  d7  < | jstdt| j|f| j}|  | j|| | || |S )z
        Compile and bind to the current context a version of this kernel
        specialized for the given signature.
        Nr   zCompilation disabled)r   Znormalize_signaturer   noner   r   r   r   r   r   r   r   Zload_overloadZ	targetctxZ_cache_hitsZ_cache_missesr  r.   r   r2   r   rm   Zsave_overloadr  )rP   r  r3   r  rV   r+   r+   r,   r     s$    zCUDADispatcher.compilec                 C   sh   | j d}|dk	r8|r(| j| j S | j|  S n,|rPdd | j D S dd | j D S dS )z
        Return the LLVM IR for this kernel.

        :param signature: A tuple of argument types.
        :return: The LLVM IR for the given signature, or a dict of LLVM IR
                 for all previously-encountered signatures.

        rR   Nc                 S   s   i | ]\}}||j  qS r+   )r;   rw   r   r+   r+   r,   r    s    z/CUDADispatcher.inspect_llvm.<locals>.<dictcomp>c                 S   s   i | ]\}}||  qS r+   )rx   r   r+   r+   r,   r    s    )r   r   r   r;   rw   rx   r  rP   rI   rR   r+   r+   r,   rx     s    	zCUDADispatcher.inspect_llvmc                    s|   t  j | jd}|dk	rD|r2| j| j S | j|  S n4|r` fdd| j D S  fdd| j D S dS )a+  
        Return this kernel's PTX assembly code for for the device in the
        current context.

        :param signature: A tuple of argument types.
        :return: The PTX code for the given signature, or a dict of PTX codes
                 for all previously-encountered signatures.
        rR   Nc                    s   i | ]\}}||j  qS r+   )r;   r&   r   ry   r+   r,   r    s    z.CUDADispatcher.inspect_asm.<locals>.<dictcomp>c                    s   i | ]\}}||  qS r+   )rz   r   ry   r+   r,   r    s    )	r   r5   r   r   r   r;   r&   rz   r  r  r+   ry   r,   rz     s    	

zCUDADispatcher.inspect_asmc                 C   sB   | j drtd|dk	r*| j|  S dd | j D S dS )a  
        Return this kernel's SASS assembly code for for the device in the
        current context.

        :param signature: A tuple of argument types.
        :return: The SASS code for the given signature, or a dict of SASS codes
                 for all previously-encountered signatures.

        SASS for the device in the current context is returned.

        Requires nvdisasm to be available on the PATH.
        rR   z(Cannot inspect SASS of a device functionNc                 S   s   i | ]\}}||  qS r+   )r{   )r'   r  defnr+   r+   r,   r    s    z/CUDADispatcher.inspect_sass.<locals>.<dictcomp>)r   r   r.   r   r{   r  r  r+   r+   r,   r{     s    zCUDADispatcher.inspect_sassc                 C   s2   |dkrt j}| j D ]\}}|j|d qdS )r|   Nr}   )r   r   r   r  r   )rP   r~   r   r  r+   r+   r,   r     s    zCUDADispatcher.inspect_typesc                 C   s   | ||}|S )rd   r+   )rf   r2   r   rh   r+   r+   r,   ri     s    
zCUDADispatcher._rebuildc                 C   s   t | j| jdS )zd
        Reduce the instance for serialization.
        Compiled definitions are discarded.
        )r2   r   )rj   r2   r   r^   r+   r+   r,   rk      s    zCUDADispatcher._reduce_states)r   r   )r   r   r   )N)N)N)N)N)N)N)N)N)N))r   r   r   r   Z
_fold_argsr   Ztargetdescrr   r0   r   r   r   r   	lru_cacher   r   r   r4   r   r   r   r   r   r   r  r  r  r	  r
  r  r  r  r   rx   rz   r{   r   r   ri   rk   r   r+   r+   r[   r,   r   :  sH   










'$




r   )9numpyr   r@   r   r   r   Z
numba.corer   r   r   r   r   r   Znumba.core.cachingr   r	   Znumba.core.compiler_lockr
   Znumba.core.dispatcherr   Znumba.core.errorsr   Znumba.core.typing.typeofr   r   Znumba.cuda.apir   Znumba.cuda.argsr   Znumba.cuda.compilerr   r   Znumba.cuda.cudadrvr   Znumba.cuda.cudadrv.devicesr   Znumba.cuda.descriptorr   Znumba.cuda.errorsr   r   Z
numba.cudar   Znumbar   r   warningsr   r>   ZReduceMixinr   objectr   r   r   r   r   r+   r+   r+   r,   <module>   s\             1.