a
    dP                     @   s   d dl Z d dlZddlmZ d dlmZ d dlmZ e	ej
dspedej
jd< edej
jd< ed	ej
jd	< d d
lmZ d dlmZ d dlmZ dd Zdd ZG dd dej
jZG dd dZdddZdS )    N   )_dummy_type)tree_flatten)tree_unflattenZ_CudaStreamBase
_CUDAGraph_graph_pool_handle_cuda_isCurrentStreamCapturing)r   r   r   c                   C   s   t  S )z
    Returns True if CUDA graph capture is underway on the current CUDA stream, False otherwise.

    If a CUDA context does not exist on the current device, returns False without initializing the context.
    r
    r   r   Z/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torch/cuda/graphs.pyis_current_stream_capturing   s    r   c                   C   s   t  S )z
    Returns an opaque token representing the id of a graph memory pool.
    See :ref:`Graph memory management<graph-memory-management>`.

    .. warning::
        This API is in beta and may change in future releases.
    r	   r   r   r   r   graph_pool_handle   s    r   c                       sv   e Zd ZdZ fddZd fdd	Z fddZ fd	d
Z fddZ fddZ	 fddZ
 fddZ  ZS )	CUDAGraphzw
    Wrapper around a CUDA graph.

    .. warning::
        This API is in beta and may change in future releases.
    c                    s   t t| | S N)superr   __new__)cls	__class__r   r   r   /   s    zCUDAGraph.__new__Nc                    s$   |du rt    nt  | dS )aX  
        Begins capturing CUDA work on the current stream.

        Typically, you shouldn't call ``capture_begin`` yourself.
        Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
        which call ``capture_begin`` internally.

        Arguments:
            pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
                :meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) that hints this graph may share memory
                with the indicated pool.  See :ref:`Graph memory management<graph-memory-management>`.
        N)r   capture_begin)selfpoolr   r   r   r   2   s    zCUDAGraph.capture_beginc                    s   t    dS )aP  
        Ends CUDA graph capture on the current stream.
        After ``capture_end``, ``replay`` may be called on this instance.

        Typically, you shouldn't call ``capture_end`` yourself.
        Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
        which call ``capture_end`` internally.
        N)r   capture_endr   r   r   r   r   F   s    	zCUDAGraph.capture_endc                    s   t    dS )z?
        Replays the CUDA work captured by this graph.
        N)r   replayr   r   r   r   r   Q   s    zCUDAGraph.replayc                    s   t    dS )zD
        Deletes the graph currently held by this instance.
        N)r   resetr   r   r   r   r   W   s    zCUDAGraph.resetc                    s
   t   S )z
        Returns an opaque token representing the id of this graph's memory pool.
        This id can optionally be passed to another graph's ``capture_begin``,
        which hints the other graph may share the same memory pool.
        )r   r   r   r   r   r   r   ]   s    zCUDAGraph.poolc                    s
   t   S )zB
        Enables debugging mode for CUDAGraph.debug_dump.
        )r   enable_debug_moder   r   r   r   r   e   s    zCUDAGraph.enable_debug_modec                    s   t  |S )z
        Arguments:
            debug_path (required): Path to dump the graph to.

        Calls a debugging function to dump the graph if the debugging is
        enabled via CUDAGraph.enable_debug_mode()
        )r   
debug_dump)r   Z
debug_pathr   r   r   r   k   s    zCUDAGraph.debug_dump)N)__name__
__module____qualname____doc__r   r   r   r   r   r   r   r   __classcell__r   r   r   r   r   (   s   r   c                   @   s.   e Zd ZdZdZd	ddZdd Zdd ZdS )
grapha  
    Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph`
    object for later replay.

    See :ref:`CUDA Graphs <cuda-graph-semantics>` for a general introduction,
    detailed use, and constraints.

    Arguments:
        cuda_graph (torch.cuda.CUDAGraph): Graph object used for capture.
        pool (optional): Opaque token (returned by a call to :func:`~torch.cuda.graph_pool_handle()` or
            :meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) hinting this graph's capture
            may share memory from the specified pool. See :ref:`Graph memory management<graph-memory-management>`.
        stream (torch.cuda.Stream, optional): If supplied, will be set as the current stream in the context.
            If not supplied, ``graph`` sets its own internal side stream as the current stream in the context.

    .. note::
        For effective memory sharing, if you pass a ``pool`` used by a previous capture and the previous capture
        used an explicit ``stream`` argument, you should pass the same ``stream`` argument to this capture.

    .. warning::
        This API is in beta and may change in future releases.
    Nc                 C   sl   | j jd u rtj | j _|d u r&dn|f| _|d ur:|n| j j| _| jd usRJ tj| j| _|| _	d S )Nr   )
r   default_capture_streamtorchcudaStreamr   Zcapture_streamstream
stream_ctx
cuda_graph)r   r+   r   r)   r   r   r   __init__   s    zgraph.__init__c                 C   s8   t j  t  t j  | j  | jj	| j
  d S r   )r&   r'   synchronizegcZcollectZempty_cacher*   	__enter__r+   r   r   r   r   r   r   r/      s
    


zgraph.__enter__c                 C   s   | j   | j||| d S r   )r+   r   r*   __exit__)r   exc_type	exc_value	tracebackr   r   r   r0      s    
zgraph.__exit__)NN)r   r    r!   r"   r%   r,   r/   r0   r   r   r   r   r$   v   s     
r$      Fc           %         sp  t  rt  rtdd}t| ts6d}| f} |f}g  t| |D ]\}}t|t jjrt	|j
dkrt	|jdkrt	|jdksJ dtdd | D sJ dt|\}} t| td	d |D sDJ d
qDdd  D }	dd | D  fddtt	| D }
dd tt	| D }dd tt	| D }t }t j  t jt j  t| ||
D ]p\}}}t|D ]V}t|| \}}t jjtdd |D tdd |D tdd |D d|d}q~~qtW d   n1 s0    Y  t j  g }g }t| ||D ]j\}}}t jj||d || }W d   n1 s^0    Y  t|\}}|t| || q$g }g }tt|
t|t|tD ]\}}}}tdd |D }t jj||dP t jjtdd |D tdd |D tdd |D d|d}W d   n1 s<0    Y  g }d}|D ]0}|jrv|||  |d7 }n
|d qRt|}|| || qtt|}tt|}dd } g }!t| D ]\}"}| ||" ||" |" |	|" ||" |
|" ||" ||" ||" 	}#t|t jjrLdd }$|$||j|#|j|_|!| n
|!|# q|rh|!d S t|!S ) a  
    Accepts callables (functions or :class:`nn.Module<torch.nn.Module>`\ s)
    and returns graphed versions.

    Each graphed callable's forward pass runs its source callable's
    forward CUDA work as a CUDA graph inside a single autograd node.

    The graphed callable's forward pass also appends
    a backward node to the autograd graph. During backward, this node runs the
    callable's backward work as a CUDA graph.

    Therefore, each graphed callable should be a drop-in replacement for its source callable
    in an autograd-enabled training loop.

    See :ref:`Partial-network capture<partial-network-capture>` for detailed use and constraints.

    If you pass a tuple of several callables, their captures will use the same memory pool.
    See :ref:`Graph memory management<graph-memory-management>` for when this is appropriate.

    Arguments:
        callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph.
            See :ref:`Graph memory management<graph-memory-management>` for when passing a tuple of callables
            is appropriate.  If you pass a tuple of callables, their order in the tuple must be the same order
            they'll run in the live workload.
        sample_args (tuple of Tensors, or tuple of tuples of Tensors): Samples args for each callable.
            If a single callable was passed, ``sample_args`` must be a single tuple of argument Tensors.
            If a tuple of callables was passed, ``sample_args`` must be tuple of tuples of argument Tensors.
        num_warmup_iters (int): The number of warmup iterations. Currently, ``DataDistributedParallel`` needs
            11 iterations for warm up. Default: ``3``.
        allow_unused_input (bool): If False, specifying inputs that were not used when computing outputs
            (and therefore their grad is always zero) is an error. Defaults to False.

    .. note::
        The ``requires_grad`` state of each Tensor in ``sample_args`` must match the state
        that's expected for the corresponding real input in the training loop.

    .. warning::
        This API is in beta and may change in future releases.

    .. warning::
        ``sample_args`` for each callable must contain only Tensors. Other types are not allowed.

    .. warning::
        Returned callables do not support higher order differentiation (e.g., double backward).

    .. warning::
        In any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters
        may be trainable. Buffers must have ``requires_grad=False``.

    .. warning::
        After you pass a :class:`torch.nn.Module` through :func:`~make_graphed_callables`,
        you may not add or remove any of that Module's parameters or buffers.

    .. warning::
        :class:`torch.nn.Module`\s passed to :func:`~torch.cuda.make_graphed_callables` must not have module hooks
        registered on them at the time they are passed. However, registering hooks on modules *after* passing them
        through :func:`~torch.cuda.make_graphed_callables` is allowed.

    .. warning::
        When running a graphed callable, you must pass its arguments in the same order and format
        they appeared in that callable's ``sample_args``.

    .. warning::
        The automatic mixed precision is supported in :func:`~torch.cuda.make_graphed_callables` only with disabled
        caching. The context manager `torch.cuda.amp.autocast()` must have `cache_enabled=False`.
    z_make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`.FTr   zModules must not have hooks registered at the time they are passed. However, registering hooks on modules after passing them through make_graphed_callables is allowed.c                 s   s   | ]}|j d u V  qdS )FNrequires_grad.0br   r   r   	<genexpr>      z)make_graphed_callables.<locals>.<genexpr>zIn any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters may be trainable. All buffers must have ``requires_grad=False``.c                 s   s   | ]}t |tjV  qd S r   )
isinstancer&   ZTensor)r8   argr   r   r   r:     r;   zfIn the beta API, sample_args for each callable must contain only Tensors. Other types are not allowed.c                 S   s   g | ]}t |qS r   )len)r8   argsr   r   r   
<listcomp>  r;   z*make_graphed_callables.<locals>.<listcomp>c                 S   s*   g | ]"}t |tjjr"t| nd qS )r   )r<   r&   nnModuletuple
parameters)r8   cr   r   r   r@     s   c                    s   g | ]} | |  qS r   r   r8   iZflatten_sample_argsZper_callable_module_paramsr   r   r@     s   c                 S   s   g | ]}t j qS r   r&   r'   r   r8   _r   r   r   r@     r;   c                 S   s   g | ]}t j qS r   rI   rJ   r   r   r   r@     r;   c                 s   s   | ]}|j r|V  qd S r   r5   r8   or   r   r   r:   &  r;   c                 s   s   | ]}|j r|V  qd S r   r5   rF   r   r   r   r:   '  r;   c                 s   s   | ]}|j rt|V  qd S r   r6   r&   Z
empty_likerL   r   r   r   r:   (  r;   )outputsinputsZgrad_outputsZonly_inputsZallow_unusedN)r   c                 s   s"   | ]}|j rt|nd V  qd S r   rN   rL   r   r   r   r:   K  r;   c                 s   s   | ]}|j r|V  qd S r   r5   rL   r   r   r   r:   N  r;   c                 s   s   | ]}|j r|V  qd S r   r5   rF   r   r   r   r:   O  r;   c                 s   s   | ]}|d ur|V  qd S r   r   rL   r   r   r   r:   P  r;   r   c	           
         s8   G 	fdddt jj  fdd}	|	S )Nc                       s@   e Zd ZefddZeejjj fddZ	dS )zOmake_graphed_callables.<locals>.make_graphed_autograd_function.<locals>.Graphedc                    s`   t D ].}|  ||  kr| ||  q   ttsNJ tdd D S )Nc                 s   s   | ]}|  V  qd S r   detachrL   r   r   r   r:   {  r;   zjmake_graphed_callables.<locals>.make_graphed_autograd_function.<locals>.Graphed.forward.<locals>.<genexpr>)rangedata_ptrcopy_r   r<   rC   )ctxrP   rG   )	fwd_graphlen_user_argsstatic_input_surfacestatic_outputsr   r   forwards  s    zWmake_graphed_callables.<locals>.make_graphed_autograd_function.<locals>.Graphed.forwardc                    sr   t |t ksJ t|D ]*\}}|d ur| | kr|| q   tts`J tdd D S )Nc                 s   s"   | ]}|d ur|  n|V  qd S r   rQ   r7   r   r   r   r:     r;   zkmake_graphed_callables.<locals>.make_graphed_autograd_function.<locals>.Graphed.backward.<locals>.<genexpr>)r>   ziprT   rU   r   r<   rC   )rV   Zgradsggrad)	bwd_graphstatic_grad_inputsstatic_grad_outputsr   r   backward}  s    zXmake_graphed_callables.<locals>.make_graphed_autograd_function.<locals>.Graphed.backwardN)
r   r    r!   staticmethodr[   r&   autogradfunctionZonce_differentiablerb   r   )r_   rW   rX   r`   ra   rY   rZ   r   r   Graphedr  s
   	rf   c                     s(   t | \}} jt|  }t|S r   )_tree_flattenapplyrC   _tree_unflatten)	user_argsZflatten_user_argsrK   out)rf   module_paramsoutput_unflatten_specr   r   functionalized  s    zVmake_graphed_callables.<locals>.make_graphed_autograd_function.<locals>.functionalized)r&   rd   ZFunction)
rW   r_   rl   rX   rm   rY   rZ   ra   r`   rn   r   )
rf   r_   rW   rX   rl   rm   r`   ra   rY   rZ   r   make_graphed_autograd_functioni  s    	$z>make_graphed_callables.<locals>.make_graphed_autograd_functionc                    s    fdd}|S )Nc                     s    j kr|  S |  S d S r   )training)rj   funcgraph_training_stategraphedorig_fwdr   r   new_fwd  s    
zEmake_graphed_callables.<locals>.make_graphed_forward.<locals>.new_fwdr   )rr   rs   rt   ru   rv   r   rq   r   make_graphed_forward  s    z4make_graphed_callables.<locals>.make_graphed_forward) r&   Zis_autocast_enabledZis_autocast_cache_enabledRuntimeErrorr<   rC   r\   rA   rB   r>   Z_backward_hooksZ_forward_hooksZ_forward_pre_hooksallbuffersrg   appendrS   r   r'   r-   r)   r(   rd   r^   r$   reversedr6   list	enumeraterp   r[   )%Z	callablesZsample_argsZnum_warmup_itersZallow_unused_inputZjust_one_callablerE   r?   Zflatten_argrK   Zper_callable_len_user_argsZ"per_callable_static_input_surfacesZ
fwd_graphsZ
bwd_graphsZmempoolrr   rY   rO   Zgrad_inputsZper_callable_static_outputsZ"per_callable_output_unflatten_specrW   Zflatten_outputsspecZ per_callable_static_grad_outputsZper_callable_static_grad_inputsrZ   r_   rl   ra   r`   Zgrad_idxr=   ro   retrG   rt   rw   r   rH   r   make_graphed_callables   s    C
,


(
(&	

/
	r   )r4   F)r.   r&   _utilsr   Ztorch.utils._pytreer   rg   r   ri   hasattrZ_C__dict__Ztorch._Cr   r   r   r   r   r   r$   r   r   r   r   r   <module>   s    	N<